20260103_003745/papers.csv

Rank,ID,Title,Authors,Year,Venue,Track,Status,Primary Area,Keywords,Citations,BM25 Score,Combined Score,DOI,URL,PDF,Source,TLDR,Abstract
1,Qr9TjKYzjl,Small features matter: Robust representation for world models,Zarif Ikram; Miranda Anna Christ; Ling Pan; Dianbo Liu,2025,ICLR 2025,main,Reject,"unsupervised, self-supervised, semi-supervised, and supervised representation learning",Representation learning;model based reinforcement learning;world models,0,13.224,0.000,,https://openreview.net/forum?id=Qr9TjKYzjl,,offline_iclr,,"In Model-Based Reinforcement Learning (MBRL), an agent learns to make decisions by building a world model that predicts the environment's dynamics. The accuracy of this world model is crucial for generalizability and sample efficiency. Many works rely on pixel-level reconstruction, which may focus o"
2,8BJl6LQgW5,Visual Representation Learning for World Models by Predicting Fine-Grained Motion,Zhao-Han Peng; Shaohui Li; Zhi Li; Yu LIU; You He,2025,ICLR 2025,main,Withdraw,reinforcement learning,world models;model-based reinforcement learning;visual representation learning,0,12.828,0.000,,https://openreview.net/forum?id=8BJl6LQgW5,,offline_iclr,,"Originating from model-based reinforcement learning (MBRL) methods, algorithms based on world models have been widely applied to boost sample efficiency in visual environments. However, existing world models often struggle with irrelevant background information and omit moving tiny objects that can "
3,3sWghzJvGd,Towards Unraveling and Improving Generalization in World Models,Qiaoyi Fang; Weiyu Du; Hang Wang; Junshan Zhang,2024,NIPS 2024,main,Reject,reinforcement_learning,world models;reinforcement learning;generalization,0,12.467,0.000,,https://openreview.net/forum?id=3sWghzJvGd,,offline_nips,,"World model has recently emerged as a promising approach to reinforcement learning (RL), as evidenced by its great successes that world model based agents exhibit state-of-the-art performance on a wide range visual control tasks. In this study, we aim to first obtain a clear understanding of the gen"
4,k7nYm2yU5i,Towards Understanding Robustness and Generalization in World Models,Qiaoyi Fang; Weiyu Du; Hang Wang; Junshan Zhang,2025,ICLR 2025,main,Withdraw,reinforcement learning,World models;robustness;generalization;model-based reinforcement learning,0,12.428,0.000,,https://openreview.net/forum?id=k7nYm2yU5i,,offline_iclr,,"World model has recently emerged as a promising approach to reinforcement learning (RL), as evidenced by the recent successes that world model based agents achieve state-of-the-art performance on a wide range of visual control tasks. This work aims to obtain a deep understanding of the robustness an"
5,W7WUJTGByR,Flow Equivariant World Modeling for Partially Observed Dynamic Environments,,2026,ICLR 2026,main,Active,generative models,World Model;Memory;Partial Observability;Equivariance;Structured Representation Learning,0,12.131,0.000,,https://openreview.net/forum?id=W7WUJTGByR,,offline_iclr,,"Embodied systems experience the world as 'a symphony of flows': a combination of many continuous streams of sensory input coupled to self-motion, interwoven with the motion of external objects. These streams obey smooth, time-parameterized symmetries, which combine through a precisely structured alg"
6,7DtgxVZGj-y,Contrastive Unsupervised Learning of World Model with Invariant Causal Features,Rudra P. K. Poudel; Harit Pandya; Roberto Cipolla,2023,ICLR 2023,main,Reject,,world models;causality;contrastive learning;model-based reinforcement learning;reinforcement learning;out-of-distribution generalisation;sim-to-real transfer;robot navigation,0,12.095,0.000,,https://openreview.net/forum?id=7DtgxVZGj-y,,offline_iclr,"We present a world model, which learns the causal features using invariance principle and achieves state-of-the-art performance on out-of-distribution generalisation.","In this paper we present a world model, which learns the causal features using invariance principle. We use contrastive unsupervised learning to learn the invariant causal features, which enforces invariance across augmentations of irrelevant parts or styles of the observation. Since the world model"
7,353,Contrastive ground-level image and remote sensing pre-training improves representation learning for natural world imagery,Andy V Huynh*; Lauren Gillespie; Jael Lopez-Saucedo; Claire Tang; Rohan Sikand,2024,ECCV 2024,main,Poster,,,0,12.038,0.000,,https://eccv2024.ecva.net//virtual/2024/poster/353,https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/10352.pdf,offline_eccv,,"""Multimodal image-text contrastive learning has shown that joint representations can be learned across modalities. Here, we show how leveraging multiple views of image data with contrastive learning can improve downstream fine-grained classification performance for species recognition, even when one"
8,9981405,DreamingV2: Reinforcement Learning with Discrete World Models without Reconstruction,Masashi Okada; Tadahiro Taniguchi; Masashi Okada; Tadahiro Taniguchi,2022,IROS 2022,main,Poster,,,0,11.959,0.000,,https://ieeexplore.ieee.org/document/9981405/,,offline_iros,,"The present paper proposes a novel reinforce-ment learning method with world models, DreamingV2, a collaborative extension of DreamerV2 and Dreaming. Dream- erV2 is a cutting-edge model-based reinforcement learning from pixels that uses discrete world models to represent latent states with categoric"
9,TLXp0scq3x,Clone Deterministic 3D Worlds with Geometrically Regularized World Models,Zaishuo Xia; Yukuan Lu; Xinyi Li; Yifan Xu; Yubei Chen,2026,ICLR 2026,main,Withdraw,generative models,World Models; Generative Models,0,11.817,0.000,,https://openreview.net/forum?id=TLXp0scq3x,,offline_iclr,,"A world model is an internal model that simulates how the world evolves. Given past observations and actions, it predicts the future physical state of both the embodied agent and its environment. Accurate world models are essential for enabling agents to think, plan, and reason effectively in comple"
10,H1gax6VtDB,Contrastive Learning of Structured World Models,Thomas Kipf; Elise van der Pol; Max Welling,2020,ICLR 2020,main,Talk,,state representation learning;graph neural networks;model-based reinforcement learning;relational learning;object discovery,0,11.766,0.000,,https://openreview.net/forum?id=H1gax6VtDB,,offline_iclr,Contrastively-trained Structured World Models (C-SWMs) learn object-oriented state representations and a relational model of an environment from raw pixel input.,"A structured understanding of our world in terms of objects, relations, and hierarchies is an important component of human cognition. Learning such a structured world model from raw sensory data remains a challenge. As a step towards this goal, we introduce Contrastively-trained Structured World Mod"
11,29p13QihRM,Language-Guided Object-Centric World Models for Predictive Control,Youngjoon Jeong; Junha Chun; Soonwoo Cha; Taesup Kim,2025,ICLR 2025,main,Reject,"unsupervised, self-supervised, semi-supervised, and supervised representation learning",Object-Centric Representation;World Model;Predictive Control,0,11.754,0.000,,https://openreview.net/forum?id=29p13QihRM,,offline_iclr,,"A world model is essential for an agent to predict the future and plan in domains such as autonomous driving and robotics. To achieve this, recent advancements have focused on video generation, which has gained significant attention due to the impressive success of diffusion models. However, these m"
12,1867,Disentangled World Models: Learning to Transfer Semantic Knowledge from Distracting Videos for Reinforcement Learning,Qi Wang; Zhipeng Zhang; Baao Xie; Xin Jin; Yunbo Wang,2025,ICCV 2025,main,Poster,,,0,11.567,0.000,,https://iccv.thecvf.com/virtual/2025/poster/1867,https://openaccess.thecvf.com/content/ICCV2025/papers/Wang_Disentangled_World_Models_Learning_to_Transfer_Semantic_Knowledge_from_Distracting_ICCV_2025_paper.pdf,offline_iccv,,"Training visual reinforcement learning (RL) in practical scenarios presents a significant challenge, i.e., RL agents suffer from low sample efficiency in environments with variations. While various approaches have attempted to alleviate this issue by disentangled representation learning, these metho"
13,YH1gieQrxH,Learning Abstract World Models with a Group-Structured Latent Space,,2026,ICLR 2026,main,Active,reinforcement learning,representation learning;reinforcement learning;geometric prior;abstract world model;model-based reinforcement learning,0,11.540,0.000,,https://openreview.net/forum?id=YH1gieQrxH,,offline_iclr,,"Learning meaningful abstract models of Markov Decision Processes (MDPs) is
crucial for improving generalization from limited data. In this work, we show how
geometric priors can be imposed on the low-dimensional representation manifold
of a learned transition model. We incorporate known symmetric st"
14,LFCSVVIy1x,Can World Models Benefit VLMs for World Dynamics?,,2026,ICLR 2026,main,Active,"applications to computer vision, audio, language, and other modalities",World Model;Multi-modal Large Language Model;Multi-modal Representation Learning,0,11.527,0.000,,https://openreview.net/forum?id=LFCSVVIy1x,,offline_iclr,,"Trained on internet-scale video data, world models are increasingly recognized as powerful world simulators that can generate consistent and plausible dynamics over structure, motion, and physics. While recent studies have explored the few-shot learning capabilities of world models on vision tasks, "
15,7orD38wzdi,Ego-centric Learning of Communicative World Models for Autonomous Driving,Hang Wang; Dechen Gao; Qiaoyi Fang; Junshan Zhang,2025,ICLR 2025,main,Reject,reinforcement learning,World Model;Reinforcement Learning;Autonomous Driving;Distributed Learning,0,11.506,0.000,,https://openreview.net/forum?id=7orD38wzdi,,offline_iclr,,"We study multi-agent reinforcement learning (MARL) for tasks in complex high-dimensional environments, such as autonomous driving. 
MARL is known to suffer from the *partial observability* and *non-stationarity* issues. To tackle these challenges, information sharing is often employed, which however"
16,paper685,On the Learnability of Knowledge in Multi-Agent Logics,Ionela G Mocanu,2021,IJCAI 2021,Doctoral Consortium,Poster,,Agent-based and Multi-agent Systems: Multi-agent Learning; Knowledge Representation and Reasoning: Knowledge Representation Languages; Knowledge Representation and Reasoning: Logics for Knowledge Representation; Knowledge Representation and Reasoning: Reasoning about Knowledge and Belief,0,11.503,0.000,,https://www.ijcai.org/proceedings/2021/685,https://www.ijcai.org/proceedings/2021/0685.pdf,offline_ijcai,,"Since knowledge engineering is an inherently challenging and somewhat unbounded task, machine learning has been widely proposed as an alternative. In real world scenarios, we often need to explicitly model multiple agents, where intelligent agents act towards achieving goals either by coordinating w"
17,wdBqDf3BZs,Stable Planning through Aligned Representations in Model-Based Reinforcement Learning,,2026,ICLR 2026,main,Active,reinforcement learning,Model-Based Reinforcement Learning;Planning;Reinforcement Learning;Alignment Model;Aligned Representation Learning,0,11.498,0.000,,https://openreview.net/forum?id=wdBqDf3BZs,,offline_iclr,,"Integrating planning with reinforcement learning (RL) significantly improves problem-solving capabilities for sequential decision-making problems, particularly in sparse-reward, long-horizon tasks. Recently, it has been shown that discrete world models can be trained such that no model degradation o"
18,rSv6zRyfQf,Learning to Interact in World Latent for Team Coordination,,2026,ICLR 2026,main,Active,reinforcement learning,Multi-agent Reinforcement Learning,0,11.465,0.000,,https://openreview.net/forum?id=rSv6zRyfQf,,offline_iclr,,"This work presents a novel representation learning framework, interactive world latent (IWoL), to facilitate *team coordination* in multi-agent reinforcement learning (MARL). Building effective representation for team coordination is a challenging problem, due to the intricate dynamics emerging from"
19,61NVz82YHM,Envision the Future in Open-World Dynamic Tasks by a Hierarchical World Model with Residual Enhanced Foresight,,2026,ICLR 2026,main,Active,reinforcement learning,hierarchical world model;model based reinforcement learning;visually grounded task planning representation,0,11.447,0.000,,https://openreview.net/forum?id=61NVz82YHM,,offline_iclr,,"Interacting with dynamic objects and adversarial agents in open-world environments remains a major challenge for reinforcement learning, primarily due to the need for flexible, long-horizon task planning under high uncertainty.
Effective reasoning representations are critical in such settings. While"
20,MSL8gSuCj2,Can Weak Quantization Make World Models Physically Interpretable?,Zhenjiang Mao; Mrinall Eashaan Umasudhan; Ivan Ruchkin,2026,ICLR 2026,main,Withdraw,"applications to robotics, autonomy, planning",World Models;Physically Interpretable Representation Learning;Autoencoders,0,11.445,0.000,,https://openreview.net/forum?id=MSL8gSuCj2,,offline_iclr,,"Deep learning models are increasingly employed for perception, prediction, and control in autonomous systems. For achieving realistic and consistent outputs, it is crucial to embed physical knowledge into their learned representations. 
 However, doing so is difficult due to high-dimensional observa"
21,OFF38XvszP,Slot Structured World Models,Jonathan Collu; Riccardo Majellaro; Aske Plaat; Thomas M. Moerland,2024,ICLR 2024,main,Withdraw,reinforcement learning,world models;model-based reinforcement learning;object-centric representation learning,0,11.418,0.000,,https://openreview.net/forum?id=OFF38XvszP,,offline_iclr,,The ability to perceive and reason about individual objects enables humans to build a robust understanding of the environment and its dynamics. Replicating such abilities in artificial systems would represent a significant milestone toward building intelligent agents. Contrastive Learning of Structu
22,b2u1yrTwFK,Dyn-O: Building Structured World Models with Object-Centric Representations,Zizhao Wang; Kaixin Wang; Li Zhao; Peter Stone; Jiang Bian,2025,NIPS 2025,main,Poster,reinforcement_learning,World Model;Object-centric representation,0,11.401,0.000,,https://openreview.net/forum?id=b2u1yrTwFK,,offline_nips,,"World models aim to capture the dynamics of the environment, enabling agents to predict and plan for future states. In most scenarios of interest, the dynamics are highly centered on interactions among objects within the environment. This motivates the development of world models that operate on obj"
23,tpbtodnI1p,World Model Implanting for Test-time Adaptation of Embodied Agents,Minjong Yoo; Jinwoo Jang; Sihyung Yoon; Honguk Woo,2025,ICML 2025,main,Poster,reinforcement_learning->everything_else,Embodied AI;Model implanting;World models;Large language model,0,11.394,0.000,,https://icml.cc/virtual/2025/poster/43758,https://openreview.net/pdf?id=tpbtodnI1p,offline_icml,,"In embodied AI, a persistent challenge is enabling agents to robustly adapt to novel domains without requiring extensive data collection or retraining. To address this, we present a world model implanting framework (WorMI) that combines the reasoning capabilities of large language models (LLMs) with"
24,SJxrKgStDH,SCALOR: Generative World Models with Scalable Object Representations,Jindong Jiang*; Sepehr Janghorbani*; Gerard De Melo; Sungjin Ahn,2020,ICLR 2020,main,Poster,,,0,11.329,0.000,,https://openreview.net/forum?id=SJxrKgStDH,,offline_iclr,,"Scalability in terms of object density in a scene is a primary challenge in unsupervised sequential object-oriented representation learning. Most of the previous models have been shown to work only on scenes with a few objects. In this paper, we propose SCALOR, a probabilistic generative world model"
25,DeG07_TcZvT,Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task,Kenneth Li; Aspen K Hopkins; David Bau; Fernanda Viégas; Hanspeter Pfister,2023,ICLR 2023,main,Top-5%,,world representation;GPT,0,11.248,0.000,,https://iclr.cc/virtual/2023/poster/11827,https://openreview.net/pdf?id=DeG07_TcZvT,offline_iclr,,"Language models show a surprising range of capabilities, but the source of their apparent competence is unclear. Do these networks just memorize a collection of surface statistics, or do they rely on internal representations of the process that generates the sequences they see? We investigate this q"
26,6JJq5TW9Mc,Learning World Models with Identifiable Factorization,Yu-Ren Liu; Biwei Huang; Zhengmao Zhu; Honglong Tian; Mingming Gong,2023,NIPS 2023,main,Poster,,Model-based Reinforcement Learning; Causal Representation Learning;,0,11.240,0.000,,https://nips.cc/virtual/2023/poster/72763,https://openreview.net/pdf?id=6JJq5TW9Mc,offline_nips,,"Extracting a stable and compact representation of the environment is crucial for efficient reinforcement learning in high-dimensional, noisy, and non-stationary environments.  Different categories of information coexist in such environments -- how to effectively extract and disentangle the informati"
27,4E0lCxBD0U,Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World Models,Yang Zhang; Chenjia Bai; Bin Zhao; Junchi Yan; Xiu Li,2025,ICLR 2025,main,Reject,reinforcement learning,multi-agent reinforcement learning;world models;learning in imagination,0,11.237,0.000,,https://openreview.net/forum?id=4E0lCxBD0U,,offline_iclr,,"Learning a world model for model-free Reinforcement Learning (RL) agents can significantly improve the sample efficiency by learning policies in imagination. However, building a world model for Multi-Agent RL (MARL) can be particularly challenging due to the scalability issue in a centralized archit"
28,MdHDUsP2lt,Information-Theoretic World Model learning for Denoised Predictions,Vedant Dave; Elmar Rueckert,2024,ICLR 2024,main,Reject,"unsupervised, self-supervised, semi-supervised, and supervised representation learning",Representation Learning;Predictive Information;Information Bottleneck;SAC,0,11.202,0.000,,https://openreview.net/forum?id=MdHDUsP2lt,,offline_iclr,,"Humans excel at isolating relevant information from noisy data to predict the behavior of dynamic systems, effectively disregarding non-informative, temporally-correlated noise.  In contrast, existing reinforcement learning algorithms face challenges in generating noise-free predictions within high-"
29,c4D7NJGC6D,Contextual Latent World Models for Offline Meta Reinforcement Learning,,2026,ICLR 2026,main,Active,reinforcement learning,Meta Learning;Offline Reinforcement Learning;World Modeling;Representation Learning,0,11.191,0.000,,https://openreview.net/forum?id=c4D7NJGC6D,,offline_iclr,,"Offline meta-reinforcement learning seeks to overcome the challenges of poor generalization and expensive data collection by leveraging datasets for related tasks. Context encoding is a prevalent approach, where an encoder maps transition histories to a task representation. In parallel, latent world"
30,34218,DriveDreamer4D: World Models Are Effective Data Machines for 4D Driving Scene Representation,Guosheng Zhao; Chaojun Ni; Xiaofeng Wang; Zheng Zhu; Xueyang Zhang,2025,CVPR 2025,main,Poster,,,0,11.165,0.000,,https://cvpr.thecvf.com/virtual/2025/poster/34218,https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_DriveDreamer4D_World_Models_Are_Effective_Data_Machines_for_4D_Driving_CVPR_2025_paper.pdf,offline_cvpr,,"Closed-loop simulation is essential for advancing end-to-end autonomous driving systems. Contemporary sensor simulation methods, such as NeRF and 3DGS, rely predominantly on conditions closely aligned with training data distributions, which are largely confined to forward-driving scenarios. Conseque"
31,GfPwZwZ9xZ,VLASim: World Modelling via VLM-Directed Abstraction and Simulation from a Single Image,,2026,ICLR 2026,main,Active,"applications to computer vision, audio, language, and other modalities",world models;video models;physical simulation;code generation,0,11.155,0.000,,https://openreview.net/forum?id=GfPwZwZ9xZ,,offline_iclr,,"Generative video models, a leading approach to world modeling, face fundamental limitations. They often violate physical and logical rules, lack interactivity, and operate as opaque black boxes ill-suited for building structured, queryable worlds. To overcome these challenges, we propose a new parad"
32,994,DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving,Xiaofeng Wang*; Zheng Zhu; Guan Huang; Chen Xinze; Jiagang Zhu,2024,ECCV 2024,main,Poster,,,0,11.139,0.000,,https://eccv2024.ecva.net//virtual/2024/poster/994,https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/06416.pdf,offline_eccv,,"""World models, especially in autonomous driving, are trending and drawing extensive attention due to their capacity for comprehending driving environments. The established world model holds immense potential for the generation of high-quality driving videos, and driving policies for safe maneuvering"
33,yFGR36PLDJ,"Simple, Good, Fast: Self-Supervised World Models Free of Baggage",Jan Robine; Marc Höftmann; Stefan Harmeling,2025,ICLR 2025,main,Poster,reinforcement learning,Reinforcement learning;World models;Self-supervised learning;Atari 100k,0,11.110,0.000,,https://iclr.cc/virtual/2025/poster/27740,https://openreview.net/pdf?id=yFGR36PLDJ,offline_iclr,,"What are the essential components of world models? How far do we get with world models that are not employing RNNs, transformers, discrete representations, and image reconstructions? This paper introduces SGF, a Simple, Good, and Fast world model that uses self-supervised representation learning, ca"
34,dmCGjPFVhF,FACTS: A Factored State-Space Framework for World Modelling,Li Nanbo; Firas Laakom; Yucheng XU; Wenyi Wang; Jürgen Schmidhuber,2025,ICLR 2025,main,Poster,"unsupervised, self-supervised, semi-supervised, and supervised representation learning",sequence modelling;spatial-temporal modelling;world modelling;multivariate time-series forecasting;object-centric representation learning;unsupervised learning;self-supervised learning,0,11.108,0.000,,https://iclr.cc/virtual/2025/poster/28966,https://openreview.net/pdf?id=dmCGjPFVhF,offline_iclr,,"World modelling is essential for understanding and predicting the dynamics of complex systems by learning both spatial and temporal dependencies. However, current frameworks, such as Transformers and selective state-space models like Mambas, exhibit limitations in efficiently encoding spatial and te"
35,paper380,Considering  Constraint Monotonicity and Foundedness in Answer Set Programming,Yi-Dong Shen; Thomas Eiter,2022,IJCAI 2022,main,Poster,Knowledge Representation and Reasoning,Knowledge Representation and Reasoning: Logic Programming; Knowledge Representation and Reasoning: Non-monotonic Reasoning; Knowledge Representation and Reasoning: Reasoning about Knowledge and Belief,0,11.104,0.000,,https://www.ijcai.org/proceedings/2022/380,https://www.ijcai.org/proceedings/2022/0380.pdf,offline_ijcai,,Should the properties of constraint monotonicity and foundedness be mandatory requirements that every answer set and world view semantics must satisfy?  This question is challenging and has incurred a debate in answer set programming (ASP).  In this paper we address the question by introducing natur
36,2021.emnlp-main.81,Relational World Knowledge Representation in Contextual Language Models: A Review,Tara Safavi; Danai Koutra,2021,EMNLP 2021,main,Main,,,0,11.074,0.000,,https://aclanthology.org/2021.emnlp-main.81/,https://aclanthology.org/2021.emnlp-main.81.pdf,offline_emnlp,,"Relational knowledge bases (KBs) are commonly used to represent world knowledge in machines. However, while advantageous for their high degree of precision and interpretability, KBs are usually organized according to manually-defined schemas, which limit their expressiveness and require significant "
37,i8PjQT3Uig,Locality Sensitive Sparse Encoding for Learning World Models Online,Zichen Liu; Chao Du; Wee Sun Lee; Min Lin,2024,ICLR 2024,main,Poster,reinforcement learning,model-based rl;online learning;incremental learning;catastrophic forgetting,0,11.028,0.000,,https://iclr.cc/virtual/2024/poster/18076,https://openreview.net/pdf?id=i8PjQT3Uig,offline_iclr,,"Acquiring an accurate world model $\textit{online}$ for model-based reinforcement learning (MBRL) is challenging due to data nonstationarity, which typically causes catastrophic forgetting for neural networks (NNs). From the online learning perspective, a Follow-The-Leader (FTL) world model is desir"
38,vC6DGcAdWR,Social World Models: Universal Structured Representations for Social Reasoning,,2026,ICLR 2026,main,Active,"neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)",social intelligence; theory of mind; world model,0,10.986,0.000,,https://openreview.net/forum?id=vC6DGcAdWR,,offline_iclr,,"Humans intuitively navigate social interactions by simulating unspoken dynamics and reasoning about others' perspectives, even with limited information. In contrast, AI systems struggle to automatically structure and reason about these implicit social contexts, largely due to traditional input repre"
39,9340843,Graph-based Hierarchical Knowledge Representation for Robot Task Transfer from Virtual to Physical World,Zhenliang Zhang; Yixin Zhu; Song-Chun Zhu; Zhenliang Zhang; Yixin Zhu,2020,IROS 2020,main,Poster,,,0,10.979,0.000,,https://ieeexplore.ieee.org/document/9340843/,,offline_iros,,"We study the hierarchical knowledge transfer problem using a cloth-folding task, wherein the agent is first given a set of human demonstrations in the virtual world using an Oculus Headset, and later transferred and validated on a physical Baxter robot. We argue that such an intricate robot task tra"
40,NadTwTODgC,Diffusion for World Modeling: Visual Details Matter in Atari,Eloi Alonso; Adam Jelley; Vincent Micheli; Anssi Kanervisto; Amos Storkey,2024,NIPS 2024,main,Spotlight,reinforcement_learning,World models;diffusion models;reinforcement learning;generative models;Atari,0,10.972,0.000,,https://neurips.cc/virtual/2024/poster/95428,https://openreview.net/pdf?id=NadTwTODgC,offline_nips,,"World models constitute a promising approach for training reinforcement learning agents in a safe and sample-efficient manner. Recent world models predominantly operate on sequences of discrete latent variables to model environment dynamics. However, this compression into a compact discrete represen"
41,iktA2PtTRsK,Watching the World Go By: Representation Learning from Unlabeled Videos,Daniel Gordon; Kiana Ehsani; Dieter Fox; Ali Farhadi,2021,ICLR 2021,main,Reject,,Representation Learning;Unsupervised Learning;Video Analytics,0,10.964,0.000,,https://openreview.net/forum?id=iktA2PtTRsK,,offline_iclr,,Recent unsupervised representation learning techniques show remarkable success on many single image tasks by using instance discrimination:  learning to differentiate between two augmented versions of the same image and a large batch of unrelated images.  Prior work uses artificial data augmentation
42,YwDvofEWlEx,Learning Behaviors through Physics-driven Latent Imagination,Antoine Richard; Stephanie ARAVECCHIA; Matthieu Geist; Cédric Pradalier,2021,CORL 2021,main,Oral,,Model-Based Reinforcement Learning;Field Robotics;Latent Models,0,10.952,0.000,,https://openreview.net/forum?id=YwDvofEWlEx,,offline_corl,,"Model-based reinforcement learning (MBRL) consists in learning a so-called world model, a representation of the environment through interactions with it, then use it to train an agent. This approach is particularly interesting in the con-text of field robotics, as it alleviates the need to train onl"
43,9539,Structured World Belief for Reinforcement Learning in POMDP,Gautam Singh; Skand Peri; Junghyun Kim; Hyunseok Kim; Sungjin Ahn,2021,ICML 2021,main,Spotlight,,,0,10.948,0.000,,https://icml.cc/virtual/2021/poster/9539,http://proceedings.mlr.press/v139/singh21a/singh21a.pdf,offline_icml,,"Object-centric world models provide structured representation of the scene and can be an important backbone in reinforcement learning and planning. However, existing approaches suffer in partially-observable environments due to the lack of belief states. In this paper, we propose Structured World Be"
44,y9A2TpaGsE,Language Agents Meet Causality -- Bridging LLMs and Causal World Models,John Gkountouras; Matthias Lindemann; Phillip Lippe; Efstratios Gavves; Ivan Titov,2025,ICLR 2025,main,Poster,"applications to robotics, autonomy, planning",Large Language Models;Causality;Causal Representation Learning;Language Agents;Planning,0,10.936,0.000,,https://iclr.cc/virtual/2025/poster/27748,https://openreview.net/pdf?id=y9A2TpaGsE,offline_iclr,,"Large Language Models (LLMs) have recently shown great promise in planning and reasoning applications. These tasks demand robust systems, which arguably require a causal understanding of the environment. While LLMs can acquire and reflect common sense causal knowledge from their pretraining data, th"
45,24C3bSaH3F,Deep SPI: Safe Policy Improvement via World Models,,2026,ICLR 2026,main,Active,reinforcement learning,reinforcement learning;guarantees;representation learning;model-based,0,10.908,0.000,,https://openreview.net/forum?id=24C3bSaH3F,,offline_iclr,,"Safe policy improvement (SPI) offers theoretical control over policy updates, yet existing guarantees largely concern offline, tabular reinforcement learning (RL). We study SPI in general online settings, when combined with world model and representation learning. We develop a theoretical framework "
46,865,GWM: Towards Scalable Gaussian World Models for Robotic Manipulation,Guanxing Lu; Baoxiong Jia; Puhao Li; Yixin Chen; Ziwei Wang,2025,ICCV 2025,main,Poster,,,0,10.888,0.000,,https://iccv.thecvf.com/virtual/2025/poster/865,https://openaccess.thecvf.com/content/ICCV2025/papers/Lu_GWM_Towards_Scalable_Gaussian_World_Models_for_Robotic_Manipulation_ICCV_2025_paper.pdf,offline_iccv,,"Training robot policies within a learned world model is trending due to the inefficiency of real-world interactions. The established image-based world models and policies have shown prior success, but lack robust geometric information that requires consistent spatial and physical understanding of th"
47,,Benchmarking Representation Learning for Natural World Image Collections,Grant Van Horn; Elijah Cole; Sara Beery; Kimberly Wilber; Serge Belongie,2021,CVPR 2021,main,Poster,,,0,10.870,0.000,,,https://openaccess.thecvf.com/content/CVPR2021/papers/Van_Horn_Benchmarking_Representation_Learning_for_Natural_World_Image_Collections_CVPR_2021_paper.pdf,offline_cvpr,,"Recent progress in self-supervised learning has resulted in models that are capable of extracting rich representations from image collections without requiring any explicit label supervision. However, to date the vast majority of these approaches have restricted themselves to training on standard be"
48,a1ttBXvNCLO,Variational Causal Dynamics: Discovering Modular World Models from Interventions,Anson Lei; Bernhard Schölkopf; Ingmar Posner,2023,ICLR 2023,main,Reject,,,0,10.863,0.000,,https://openreview.net/forum?id=a1ttBXvNCLO,,offline_iclr,,"Latent world models allow agents to reason about complex environments with high-dimensional observations. However, adapting to new environments and effectively leveraging previous knowledge remain significant challenges. We present variational causal dynamics (VCD), a structured world model that exp"
49,18531,Latent World Models For Intrinsically Motivated Exploration,Aleksandr Ermolov; Nicu Sebe,2020,NIPS 2020,main,Spotlight,,,0,10.862,0.000,,https://nips.cc/virtual/2020/poster/18531,https://papers.nips.cc/paper_files/paper/2020/file/3c09bb10e2189124fdd8f467cc8b55a7-Paper.pdf,offline_nips,,"In this work we consider partially observable environments with sparse rewards. We present a self-supervised representation learning method for image-based observations, which arranges embeddings respecting temporal distance of observations. This representation is empirically robust to stochasticity"
50,YK9G4Htdew,Learning Transformer-based World Models with Contrastive Predictive Coding,Maxime Burchi; Radu Timofte,2025,ICLR 2025,main,Spotlight,reinforcement learning,model-based reinforcement learning;transformer network;contrastive predictive coding,0,10.834,0.000,,https://iclr.cc/virtual/2025/poster/29267,https://openreview.net/pdf?id=YK9G4Htdew,offline_iclr,,The DreamerV3 algorithm recently obtained remarkable performance across diverse environment domains by learning an accurate world model based on Recurrent Neural Networks (RNNs). Following the success of model-based reinforcement learning algorithms and the rapid adoption of the Transformer architec
51,W4915ssZ3c,Provably Learning Task-Relevant World Representation,,2026,ICLR 2026,main,Active,"unsupervised, self-supervised, semi-supervised, and supervised representation learning",latent variable model;representation learning,0,10.827,0.000,,https://openreview.net/forum?id=W4915ssZ3c,,offline_iclr,,"Identifying task-relevant latent representations $\mathbf{s}$ from observations $\mathbf{o} = f(\mathbf{s})$ is fundamental. Identifiability, the asymptotic guarantee of recovering the ground-truth representation, is critical because it sets the ultimate limit of any model, even with infinite data a"
52,pZuZWRuPyi,Learning to Be Uncertain: Pre-training World Models with Horizon-Calibrated Uncertainty,,2026,ICLR 2026,main,Active,reinforcement learning,World Models;Unsupervised Pre-training;Temporal Relative Embeddings;Horizon-Calibrated Uncertainty,0,10.787,0.000,,https://openreview.net/forum?id=pZuZWRuPyi,,offline_iclr,,"Pre-training world models on large, action-free video datasets offers a promising path toward generalist agents, but a fundamental flaw undermines this paradigm. Prevailing methods train models to predict a single, deterministic future, an objective that is ill-posed for inherently stochastic enviro"
53,Bkp_y7qxe,Unsupervised Deep Learning of State Representation Using Robotic Priors,Timothee LESORT; David FILLIAT,2017,ICLR 2017,main,Reject,,Deep learning;Computer vision;Unsupervised Learning,0,10.781,0.000,,https://openreview.net/forum?id=Bkp_y7qxe,,offline_iclr,This paper introduces a method for training a deep neural network to learn a  representation of a robot's environment state using a priori knowledge.,"Our understanding of the world depends highly on how we represent it.  Using background knowledge about its complex underlying physical rules, our brain can produce intuitive and simplified representations which it can easily use to solve problems. The approach of this paper aims to reproduce this s"
54,w50ICQC6QJ,Discovery of the Hidden World with Large Language Models,Chenxi Liu; Yongqiang Chen; Tongliang Liu; Mingming Gong; James Cheng,2024,NIPS 2024,main,Poster,causal_inference,Causal Discovery;Large Language Models;Causal Representation Learning,0,10.756,0.000,,https://neurips.cc/virtual/2024/poster/93175,https://openreview.net/pdf?id=w50ICQC6QJ,offline_nips,,"Revealing the underlying causal mechanisms in the real world is the key to the development of science. Despite the progress in the past decades, traditional causal discovery approaches (CDs) mainly rely on high-quality measured variables, usually given by human experts, to find causal relations. The"
55,2685,DriveX: Omni Scene Modeling for Learning Generalizable World Knowledge in Autonomous Driving,Chen Shi; Shaoshuai Shi; Kehua Sheng; Bo Zhang; Li Jiang,2025,ICCV 2025,main,Poster,,,0,10.754,0.000,,https://iccv.thecvf.com/virtual/2025/poster/2685,https://openaccess.thecvf.com/content/ICCV2025/papers/Shi_DriveX_Omni_Scene_Modeling_for_Learning_Generalizable_World_Knowledge_in_ICCV_2025_paper.pdf,offline_iccv,,"Data-driven learning has advanced autonomous driving, yet task-specific models struggle with out-of-distribution scenarios due to their narrow optimization objectives and reliance on costly annotated data. We present DriveX, a self-supervised world model that learns generalizable scene dynamics and "
56,VjeT8VFhHo,One-shot World Models Using a Transformer Trained on a Synthetic Prior,Fabio Ferreira; Moreno Schlageter; Raghu Rajan; André Biedenkapp; Frank Hutter,2025,ICLR 2025,main,Withdraw,"transfer learning, meta learning, and lifelong learning",World Models;Synthetic Pretraining;Reinforcement Learning;In-Context Learning,0,10.742,0.000,,https://openreview.net/forum?id=VjeT8VFhHo,,offline_iclr,,"A World Model is a compressed spatial and temporal representation of a real world environment that allows one to train an agent or execute planning methods. However, world models are typically trained on observations from the real world environment, and they usually do not enable learning policies f"
57,vJwjWyt4Ed,Learning View-invariant World Models for Visual Robotic Manipulation,Jing-Cheng Pang; Nan Tang; Kaiyuan Li; Yuting Tang; Xin-Qiang Cai,2025,ICLR 2025,main,Poster,reinforcement learning,Robotic manipulation;reinforcement learning;world model,0,10.737,0.000,,https://iclr.cc/virtual/2025/poster/27921,https://openreview.net/pdf?id=vJwjWyt4Ed,offline_iclr,,"Robotic manipulation tasks often rely on visual inputs from cameras to perceive the environment. However, previous approaches still suffer from performance degradation when the camera’s viewpoint changes during manipulation. In this paper, we propose ReViWo (Representation learning for View-invarian"
58,etPAH4xSUn,In-Context Symmetries: Self-Supervised Learning through Contextual World Models,Sharut Gupta; Chenyu Wang; Yifei Wang; Tommi Jaakkola; Stefanie Jegelka,2024,NIPS 2024,main,Poster,machine_vision,Self-Supervised Learning; Context; Equivariance,0,10.733,0.000,,https://neurips.cc/virtual/2024/poster/94239,https://openreview.net/pdf?id=etPAH4xSUn,offline_nips,,"At the core of self-supervised learning for vision is the idea of learning invariant or equivariant representations with respect to a set of data transformations. This approach, however, introduces strong inductive biases, which can render the representations fragile in downstream tasks that do not "
59,ULXYZCms41,Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling,,2026,ICLR 2026,main,Active,generative models,Generative Model; Video Generation; World Modeling,0,10.665,0.000,,https://openreview.net/forum?id=ULXYZCms41,,offline_iclr,,"Videos inherently represent 2D projections of a dynamic 3D world. However, our analysis suggests that video diffusion models trained solely on raw video data often fail to capture meaningful geometric-aware structure in their learned representations. To bridge this gap between video diffusion models"
60,OWkkFaq1IZ,From Observations to Events: Event-Aware World Models for Reinforcement Learning,,2026,ICLR 2026,main,Active,reinforcement learning,model-based reinforcement learning;online learning;reinforcement learning,0,10.660,0.000,,https://openreview.net/forum?id=OWkkFaq1IZ,,offline_iclr,,"While model-based reinforcement learning (MBRL) improves sample efficiency by learning world models from raw observations, existing methods struggle to generalize across structurally similar scenes and remain vulnerable to spurious variations such as textures or color shifts. From a cognitive scienc"
61,GD55nbjRaD,Terra: Explorable Native 3D World Model with Point Latents,Yuanhui Huang; Weiliang Chen; Wenzhao Zheng; Xin Tao; Pengfei Wan,2026,ICLR 2026,main,Withdraw,generative models,World Models,0,10.652,0.000,,https://openreview.net/forum?id=GD55nbjRaD,,offline_iclr,,"World models have garnered increasing attention for comprehensive modeling of the real world.
However, most existing methods still rely on pixel-aligned representations as the basis for world evolution, neglecting the inherent 3D nature of the physical world.
This could undermine the 3D consistency "
62,3RSLW9YSgk,Dream to Manipulate: Compositional World Models Empowering Robot Imitation Learning with Imagination,Leonardo Barcellona; Andrii Zadaianchuk; Davide Allegro; Samuele Papa; Stefano Ghidoni,2025,ICLR 2025,main,Poster,"applications to robotics, autonomy, planning",World model;  Imagination; Imitation Learning; Gaussian Splatting; Compositional; Physics-informed; Object-centric;,0,10.641,0.000,,https://iclr.cc/virtual/2025/poster/31075,https://openreview.net/pdf?id=3RSLW9YSgk,offline_iclr,,"A world model provides an agent with a representation of its environment, enabling it to predict the causal consequences of its actions. Current world models typically cannot directly and explicitly imitate the actual environment in front of a robot, often resulting in unrealistic behaviors and hall"
63,w8BnKGFIYN,Learning to Play Atari in a World of Tokens,Pranav Agarwal; Sheldon Andrews; Samira Ebrahimi Kahou,2024,ICML 2024,main,Poster,,,0,10.628,0.000,,https://icml.cc/virtual/2024/poster/32760,https://openreview.net/pdf?id=w8BnKGFIYN,offline_icml,,"Model-based reinforcement learning agents utilizing transformers have shown improved sample efficiency due to their ability to model extended context, resulting in more accurate world models. However, for complex reasoning and planning tasks, these methods primarily rely on continuous representation"
64,17443,Denoised MDPs: Learning World Models Better Than the World Itself,Tongzhou Wang; Simon Du; Antonio Torralba; Phillip Isola; Amy Zhang,2022,ICML 2022,main,Spotlight,,,0,10.622,0.000,,https://icml.cc/virtual/2022/poster/17443,https://proceedings.mlr.press/v162/wang22c/wang22c.pdf,offline_icml,,"The ability to separate signal from noise, and reason with clean abstractions, is critical to intelligence. With this ability, humans can efficiently perform real world tasks without considering all possible nuisance factors. How can artificial agents do the same? What kind of information can agents"
65,769,HERMES: A Unified Self-Driving World Model for Simultaneous 3D Scene Understanding and Generation,Xin Zhou; Dingkang Liang; Sifan Tu; Xiwu Chen; Yikang Ding,2025,ICCV 2025,main,Poster,,,0,10.582,0.000,,https://iccv.thecvf.com/virtual/2025/poster/769,https://openaccess.thecvf.com/content/ICCV2025/papers/Zhou_HERMES_A_Unified_Self-Driving_World_Model_for_Simultaneous_3D_Scene_ICCV_2025_paper.pdf,offline_iccv,,"Driving World Models (DWMs) have become essential for autonomous driving by enabling future scene prediction. However, existing DWMs are limited to scene generation and fail to incorporate scene understanding, which involves interpreting and reasoning about the driving environment. In this paper, we"
66,VoKut0M4bI,"Unified Stability Bounds for Structured World Models: Geometry, Equivariance, and Identifiability as Sufficient Conditions",,2026,ICLR 2026,main,Active,learning theory,world models;DreamerV3;representation learning;equivariance;Johnson–Lindenstrauss;bi-Lipschitz embedding;Bellman operator;nonlinear ICA,0,10.566,0.000,,https://openreview.net/forum?id=VoKut0M4bI,,offline_iclr,,"Representation learning for model-based RL offers sample efficiency yet raises a practical question, namely which properties of a learned representation govern downstream performance, and how to test them without re-running large-scale training? We present a \emph{unified stability bound} that decom"
67,oUzxhnnGVM,When do neural networks learn world models?,Tianren Zhang; Guanyu Chen; Feng Chen,2025,ICML 2025,main,Poster,theory->learning_theory,World Models;Representation Learning;Learning Theory;Neural Networks;Machine Learning,0,10.558,0.000,,https://icml.cc/virtual/2025/poster/44058,https://openreview.net/pdf?id=oUzxhnnGVM,offline_icml,,"Humans develop _world models_ that capture the underlying generation process of data. Whether neural networks can learn similar world models remains an open problem. In this work, we present the first theoretical results for this problem, showing that in a _multi-task_ setting, models with a _low-de"
68,FLArrpmsF1,Higher Embedding Dimension Creates a Stronger World Model for a Simple Sorting Task,,2026,ICLR 2026,main,Active,interpretability and explainable AI,embedding dimension;world model;transformer;reinforcement learning;sorting;PPO;interpretability;interpretation of learned representations,0,10.554,0.000,,https://openreview.net/forum?id=FLArrpmsF1,,offline_iclr,,"We study how embedding dimension affects the emergence of an internal ""world model"" in a transformer trained with reinforcement learning to perform bubble-sort-style adjacent swaps. While even very small embedding dimensions are sufficient for models to achieve high accuracy, larger dimensions yield"
69,EYxLmZRSK1,COME: Adding Scene-Centric Forecasting Control to Occupancy World Model,Yining Shi; Kun Jiang; Qiang Meng; Ke Wang; Jiabao Wang,2025,NIPS 2025,main,Poster,deep_learning,Generative Models;Autonomous Driving;Occupancy Forecasting;Occupancy Generation;ControlNet,0,10.547,0.000,,https://openreview.net/forum?id=EYxLmZRSK1,,offline_nips,,"World models are critical for autonomous driving to simulate environmental dynamics and generate synthetic data.
Existing methods struggle to disentangle ego-vehicle motion (perspective shifts) from scene evolvement (agent interactions), leading to suboptimal predictions. Instead, we propose to sepa"
70,hAHbo4EGQY,From Masks to Worlds: A Hitchhiker’s Guide to World Models,Jinbin Bai; Yu Lei; Hecong Wu; Yuchen Zhu; Zhuoran Zhao,2026,ICLR 2026,main,Withdraw,"other topics in machine learning (i.e., none of the above)",World Models;Position Paper,0,10.547,0.000,,https://openreview.net/forum?id=hAHbo4EGQY,,offline_iclr,,"This is not a typical survey of world models, it is a guide for those who want to build worlds. We do not aim to catalog every paper that has ever mentioned a ``world model"". Instead, we follow one clear road: from early masked models that unified representation learning across modalities, to unifie"
71,2H6KhX1kJr,Transformers and slot encoding for sample efficient physical world modelling,Francesco Petri; Luigi Asprino; Aldo Gangemi,2025,ICLR 2025,main,Reject,learning on time series and dynamical systems,Transformers;world modeling;slot attention,0,10.546,0.000,,https://openreview.net/forum?id=2H6KhX1kJr,,offline_iclr,,"World modelling, i.e. building a representation of the rules that govern the world so as to predict its evolution, is an essential ability for any agent interacting with the physical world. Recent applications of the Transformer architecture to the problem of world modelling from video input show no"
72,2062,Gaussian-based World Model: Gaussian Priors for Voxel-Based Occupancy Prediction and Future Motion Prediction,Tuo Feng; Wenguan Wang; Yi Yang,2025,ICCV 2025,main,Poster,,,0,10.527,0.000,,https://iccv.thecvf.com/virtual/2025/poster/2062,https://openaccess.thecvf.com/content/ICCV2025/papers/Feng_Gaussian-based_World_Model_Gaussian_Priors_for_Voxel-Based_Occupancy_Prediction_and_ICCV_2025_paper.pdf,offline_iccv,,"In autonomous driving, accurately predicting occupancy and motion is crucial for safe navigation within dynamic environments. However, existing methods often suffer from difficulties in handling complex scenes and uncertainty arising from sensor data. To address these issues, we propose a new Gaussi"
73,rRxFIOoEeF,Revisiting Multi-Agent World Modeling from a Diffusion-Inspired Perspective,Yang Zhang; Xinran Li; Jianing Ye; Shuang Qiu; Delin Qu,2025,NIPS 2025,main,Poster,reinforcement_learning,Multi-Agent Model-based Reinforcement Learning;World Models;Learning in Imaginations;Diffusion Models,0,10.508,0.000,,https://openreview.net/forum?id=rRxFIOoEeF,,offline_nips,,"World models have recently attracted growing interest in Multi-Agent Reinforcement Learning (MARL) due to their ability to improve sample efficiency for policy learning. However, accurately modeling environments in MARL is challenging due to the exponentially large joint action space and highly unce"
74,paper121,Region-Aware Metric Learning for Open World Semantic Segmentation via Meta-Channel Aggregation,Hexin Dong; Zifan Chen; Mingze Yuan; Yutong Xie; Jie Zhao,2022,IJCAI 2022,main,Poster,Computer Vision,"Computer Vision: Segmentation; Computer Vision: Representation Learning; Computer Vision: Transfer, low-shot, semi- and un- supervised learning",0,10.499,0.000,,https://www.ijcai.org/proceedings/2022/121,https://www.ijcai.org/proceedings/2022/0121.pdf,offline_ijcai,,"As one of the most challenging and practical segmentation tasks, open-world semantic segmentation requires the model to segment the anomaly regions in the images and incrementally learn to segment out-of-distribution (OOD) objects, especially under a few-shot condition. The current state-of-the-art "
75,IOqr2ZyXHz1,Continual Lifelong Causal Effect Inference with Real World Evidence,Zhixuan Chu; Stephen Rathbun; Sheng Li,2021,ICLR 2021,main,Reject,,continual learning;incremental learning;causal effect inference;representation learning;treatment effect estimation,0,10.484,0.000,,https://openreview.net/forum?id=IOqr2ZyXHz1,,offline_iclr,,"The era of real world evidence has witnessed an increasing availability of observational data, which much facilitates the development of causal effect inference. Although significant advances have been made to overcome the challenges in causal effect estimation, such as missing counterfactual outcom"
76,2tgeU3xO1r,Origins and roles of world representations in neural networks,,2026,ICLR 2026,main,Active,interpretability and explainable AI,World Representation;Fine-tuning;Generalization;Transformers,0,10.478,0.000,,https://openreview.net/forum?id=2tgeU3xO1r,,offline_iclr,,"While neural representations have been extensively studied in large practical models, the controlled conditions that govern their emergence and their downstream role in model adaptation remain poorly understood. In this work, we develop a framework separating the underlying world, the data generatio"
77,ucxQrked0d,Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement Learning,Qi Wang; Junming Yang; Yunbo Wang; Xin Jin; Wenjun Zeng,2024,NIPS 2024,main,Poster,reinforcement_learning,World models;reinforcement learning;visual control,0,10.454,0.000,,https://neurips.cc/virtual/2024/poster/93263,https://openreview.net/pdf?id=ucxQrked0d,offline_nips,,"Training offline RL models using visual inputs poses two significant challenges, *i.e.*, the overfitting problem in representation learning and the overestimation bias for expected future rewards. Recent work has attempted to alleviate the overestimation bias by encouraging conservative behaviors. T"
78,qkeYxpB9w0,Neurosymbolic World Models for Sequential Decision Making,Leonardo Hernandez Cano; Maxine Perroni-Scharf; Neil Dhir; Arun Ramamurthy; Armando Solar-Lezama,2025,ICML 2025,main,Poster,general_machine_learning->unsupervised_and_semisupervised_learning,world models;finite state machines;structure learning;model-based reinforcement learning;neurosymbolic,0,10.452,0.000,,https://icml.cc/virtual/2025/poster/43925,https://openreview.net/pdf?id=qkeYxpB9w0,offline_icml,,"We present Structured World Modeling for Policy Optimization (SWMPO), a framework for unsupervised learning of neurosymbolic Finite State Machines (FSM) that capture environmental structure for policy optimization. Traditional unsupervised world modeling methods rely on unstructured representations,"
79,mWoMyDEfbM,ViMo: A Generative Visual GUI World Model for App Agents,,2026,ICLR 2026,main,Active,"applications to computer vision, audio, language, and other modalities",World Model;GUI Generation;App Agent,0,10.444,0.000,,https://openreview.net/forum?id=mWoMyDEfbM,,offline_iclr,,"App agents, which autonomously operate mobile Apps through GUIs, have gained significant interest in real-world applications. Yet, they often struggle with long-horizon planning, failing to find the optimal actions for complex tasks with longer steps. To address this, world models are used to predic"
80,za9Jx8yqUA,GenRL: Multimodal-foundation world models for generalization in embodied agents,Pietro Mazzaglia; Tim Verbelen; Bart Dhoedt; Aaron Courville; Sai Rajeswar,2024,NIPS 2024,main,Poster,reinforcement_learning,world models;foundations models;reinforcement learning;multitask generalization,0,10.400,0.000,,https://neurips.cc/virtual/2024/poster/92947,https://openreview.net/pdf?id=za9Jx8yqUA,offline_nips,,"Learning generalist embodied agents, able to solve multitudes of tasks in different domains is a long-standing problem. Reinforcement learning (RL) is hard to scale up as it requires a complex reward design for each task. In contrast, language can specify tasks in a more natural way. Current foundat"
81,OcHQVmfLn2c,Prototypical Context-aware Dynamics Generalization for High-dimensional Model-based Reinforcement Learning,Junjie Wang; Yao Mu; Dong Li; qichao Zhang; Dongbin Zhao,2023,ICLR 2023,main,Withdraw,,model-based reinforcement learning;dynamics generalization;prototypical representation learning;latent world model,0,10.373,0.000,,https://openreview.net/forum?id=OcHQVmfLn2c,,offline_iclr,We present a prototypical Context-aware Dynamics (ProtoCAD) model to capture the local dynamics by time consistent latent context.,"The latent world model provides a promising way to learn policies in a compact latent space for tasks with high-dimensional observations, however, its generalization across diverse environments with unseen dynamics remains challenging. Although the recurrent structure utilized in current advances he"
82,AhcxMGfqQn,Collaborative World Models: An Online-Offline Transfer RL Approach,Qi Wang; Junming Yang; Yunbo Wang; Xin Jin; Wenjun Zeng,2024,ICLR 2024,main,Reject,reinforcement learning,World models;reinforcement learning;visual control;transfer learning,0,10.339,0.000,,https://openreview.net/forum?id=AhcxMGfqQn,,offline_iclr,,Training offline reinforcement learning (RL) models with visual inputs is challenging due to the coupling of overfitting issue in representation learning and the risk of overestimating true value functions. Recent work has attempted to alleviate the overestimation bias by encouraging conservative be
83,qmEyJadwHA,Object-Centric World Models from Few-Shot Annotations for Sample-Efficient Reinforcement Learning,,2026,ICLR 2026,main,Active,reinforcement learning,Model-based RL;Object-centric RL;Video object segmentation;Atari;Hollow Knight,0,10.327,0.000,,https://openreview.net/forum?id=qmEyJadwHA,,offline_iclr,,"While deep reinforcement learning (DRL) from pixels has achieved remarkable success, its sample inefficiency remains a critical limitation for real-world applications. Model-based RL (MBRL) addresses this by learning a world model to generate simulated experience, but standard approaches that rely o"
84,21516,Persistent Nature: A Generative Model of Unbounded 3D Worlds,Lucy Chai; Richard Tucker; Zhengqi Li; Phillip Isola; Noah Snavely,2023,CVPR 2023,main,Poster,,,0,10.242,0.000,,https://cvpr.thecvf.com/virtual/2023/poster/21516,https://openaccess.thecvf.com/content/CVPR2023/papers/Chai_Persistent_Nature_A_Generative_Model_of_Unbounded_3D_Worlds_CVPR_2023_paper.pdf,offline_cvpr,,"Despite increasingly realistic image quality, recent 3D image generative models often operate on 3D volumes of fixed extent with limited camera motions. We investigate the task of unconditionally synthesizing unbounded nature scenes, enabling arbitrarily large camera motion while maintaining a persi"
85,I9pQJJWKn3,Dreamland: Hybrid World Creation with Simulator and Generative Models,,2026,ICLR 2026,main,Active,generative models,Computer Vision;Generative Models;World Model,0,10.230,0.000,,https://openreview.net/forum?id=I9pQJJWKn3,,offline_iclr,,"Large-scale video generative models can synthesize diverse and realistic visual content for dynamic world creation, but they often lack element-wise controllability, hindering their use in editing scenes and training embodied AI agents. We propose Dreamland, a hybrid world generation framework that "
86,mtk8tTKWs0,PIGDreamer: Privileged Information Guided World Models for Safe Partially Observable Reinforcement Learning,Dongchi Huang; Jiaqi WANG; Yang Li; Chunhe Xia; Tianle Zhang,2025,ICML 2025,main,Poster,reinforcement_learning->deep_rl,Reinforcement Learning; World Models; Safe Reinforcement Learning; Model-based Reinforcement Learning; Privileged Learning,0,10.178,0.000,,https://icml.cc/virtual/2025/poster/44134,https://openreview.net/pdf?id=mtk8tTKWs0,offline_icml,,"Partial observability presents a significant challenge for safe reinforcement learning, as it impedes the identification of potential risks and rewards. Leveraging specific types of privileged information during training to mitigate the effects of partial observability has yielded notable empirical "
87,article-27921,Hyp-OW: Exploiting Hierarchical Structure Learning with Hyperbolic Distance Enhances Open World Object Detection,Thang Doan; Xin Li; Sima Behpour; Wenbin He; Liang Gou,2024,AAAI 2024,main,Technical,computer vision i,,0,10.160,0.000,,https://ojs.aaai.org/index.php/AAAI/article/view/27921,https://ojs.aaai.org/index.php/AAAI/article/view/27921/27864,offline_aaai,,"Open World Object Detection (OWOD) is a challenging and realistic task that extends beyond the scope of standard Object Detection task. It involves detecting both known and unknown objects while integrating learned knowledge for future tasks. However, the level of ""unknownness"" varies significantly "
88,RD7Fo7RezT,Model-Based Transfer RL with Task-Agnostic Offline Pretraining,Minting Pan; Yitao Zheng; Haijian Chen; Yumeng He; Yunbo Wang,2024,ICLR 2024,main,Withdraw,reinforcement learning,World model;visual reinforcement learning;transfer learning,0,10.141,0.000,,https://openreview.net/forum?id=RD7Fo7RezT,,offline_iclr,,"Pretraining RL models on offline datasets is a promising way to improve their training efficiency in online tasks, but challenging due to the inherent mismatch in dynamics and behaviors across tasks or data domains. We present Vid2Act, a model-based RL method that learns to transfer potentially usef"
89,nPcrFDHL2i,WonderZoom: Multi-Scale 3D World Generation,Jin Cao; Hong-Xing Yu; Jiajun Wu,2026,ICLR 2026,main,Withdraw,"applications to computer vision, audio, language, and other modalities",Multi-scale generation;3D world generation,0,10.139,0.000,,https://openreview.net/forum?id=nPcrFDHL2i,,offline_iclr,,"We present WonderZoom, a novel approach to generating 3D scenes with contents across multiple spatial scales from a single image. Existing 3D world generation models remain limited to single-scale synthesis and cannot produce coherent scene contents at varying granularities. The fundamental challeng"
90,K8wCGMzeuY,Internalizing World Models via Self-Play Finetuning for Agentic RL,,2026,ICLR 2026,main,Active,"foundation or frontier models, including LLMs",Large language models;agents;world model,0,10.139,0.000,,https://openreview.net/forum?id=K8wCGMzeuY,,offline_iclr,,"Large Language Models (LLMs) as agents often struggle in out-of-distribution (OOD) scenarios. Real-world environments are complex and dynamic, governed by task-specific rules and stochasticity, which makes it difficult for LLMs to ground internal knowledge in those dynamics. Under such OOD condition"
91,GKt3VRaCU1,seq-JEPA: Autoregressive Predictive Learning of Invariant-Equivariant World Models,Hafez Ghaemi; Eilif Benjamin Muller; Shahab Bakhtiari,2025,NIPS 2025,main,Poster,deep_learning,Self-supervised learning;world models;computer vision;equivariance,0,10.135,0.000,,https://openreview.net/forum?id=GKt3VRaCU1,,offline_nips,,"Joint-embedding self-supervised learning (SSL) commonly relies on transformations such as data augmentation and masking to learn visual representations, a task achieved by enforcing invariance or equivariance with respect to these transformations applied to two views of an image. This dominant two-v"
92,2000,Global and Local Entailment Learning for Natural World Imagery,Srikumar Sastry; Aayush Dhakal; Eric Xing; Subash Khanal; Nathan Jacobs,2025,ICCV 2025,main,Poster,,,0,10.135,0.000,,https://iccv.thecvf.com/virtual/2025/poster/2000,https://openaccess.thecvf.com/content/ICCV2025/papers/Sastry_Global_and_Local_Entailment_Learning_for_Natural_World_Imagery_ICCV_2025_paper.pdf,offline_iccv,,"Learning the hierarchical structure of data in vision-language models is a significant challenge. Previous works have attempted to address this challenge by employing entailment learning. However, these approaches fail to model the transitive nature of entailment explicitly, which establishes the re"
93,10161534,Open-vocabulary Queryable Scene Representations for Real World Planning,Boyuan Chen; Fei Xia; Brian Ichter; Kanishka Rao; Keerthana Gopalakrishnan,2023,ICRA 2023,main,Poster,,,0,10.116,0.000,,https://ieeexplore.ieee.org/document/10161534/,,offline_icra,,"Large language models (LLMs) have unlocked new capabilities of task planning from human instructions. However, prior attempts to apply LLMs to real-world robotic tasks are limited by the lack of grounding in the surrounding scene. In this paper, we develop NLMap, an open-vocabulary and queryable sce"
94,sdK5Ufoo2d,Curious Causality-Seeking Agents Learn Meta Causal World,Zhiyu Zhao; Haoxuan Li; Haifeng Zhang; Jun Wang; Francesco Faccio,2025,NIPS 2025,main,Poster,probabilistic_methods,Causality;World Model,0,10.034,0.000,,https://openreview.net/forum?id=sdK5Ufoo2d,,offline_nips,,"When building a world model, a common assumption is that the environment has a single, unchanging underlying causal rule, like applying Newton's laws to every situation. However, in truly open-ended environments, the apparent causal mechanism may drift over time because the agent continually encount"
95,0klioDjSVM,Delta-Triplane Transformers as Occupancy World Models,,2026,ICLR 2026,main,Active,optimization,occupancy world models;triplane;multi-scale;autoregression,0,10.020,0.000,,https://openreview.net/forum?id=0klioDjSVM,,offline_iclr,,"Occupancy World Models (OWMs) aim to predict future scenes via 3D voxelized representations of the environment to support intelligent motion planning. Existing approaches typically generate full future occupancy states from VAE-style latent encodings. In contrast, we propose Delta-Triplane Transform"
96,10802278,DHP-Mapping: A Dense Panoptic Mapping System with Hierarchical World Representation and Label Optimization Techniques,Tianshuai Hu; Jianhao Jiao; Yucheng Xu; Hongji Liu; Sheng Wang,2024,IROS 2024,main,Poster,,,0,10.018,0.000,,https://ieeexplore.ieee.org/document/10802278/,,offline_iros,,"Maps provide robots with crucial environmental knowledge, thereby enabling them to perform interactive tasks effectively. Easily accessing accurate abstract-to-detailed geometric and semantic concepts from maps is crucial for robots to make informed and efficient decisions. To comprehensively model "
97,SbfdxWibDn,C-NAV: Towards Self-Evolving Continual Object Navigation in Open World,MingMing Yu; Fei Zhu; wenzhuo liu; Yirong Yang; Qunbo Wang,2025,NIPS 2025,main,Poster,applications,Embodied AI;Continue Learning,0,10.004,0.000,,https://openreview.net/forum?id=SbfdxWibDn,,offline_nips,,"Embodied agents are expected to perform object navigation in dynamic, open-world environments. However, existing approaches typically rely on static trajectories and a fixed set of object categories during training, overlooking the real-world requirement for continual adaptation to evolving scenario"
98,cH4VTcCVYs,Policy optimization emerges from noisy representation learning,Jonah Brenner; Chenguang Li; Gabriel Kreiman,2025,ICLR 2025,main,Reject,reinforcement learning,natural intelligence;reinforcement learning;representation learning;noise,0,9.970,0.000,,https://openreview.net/forum?id=cH4VTcCVYs,,offline_iclr,,Nervous systems learn representations of the world and policies to act within it. We present a framework that uses reward-dependent noise to facilitate policy optimization in representation learning networks. These networks balance extracting normative features and task-relevant information to solve
99,9812429,Learning to Fill the Seam by Vision: Sub-millimeter Peg-in-hole on Unseen Shapes in Real World,Liang Xie; Hongxiang Yu; Yinghao Zhao; Haodong Zhang; Zhongxiang Zhou,2022,ICRA 2022,main,Poster,,,0,9.929,0.000,,https://ieeexplore.ieee.org/document/9812429/,,offline_icra,,"In the peg insertion task, human pays attention to the seam between the peg and the hole and tries to fill it continuously with visual feedback. By imitating the human's behavior, we design architectures with position and orientation estimators based on the seam representation for pose alignment, wh"
100,2025.acl-long.1540,Deliberate Reasoning in Language Models as Structure-Aware Planning with an Accurate World Model,Siheng Xiong; Ali Payani; Yuan Yang; Faramarz Fekri,2025,ACL 2025,main,Long,,,0,9.918,0.000,,https://aclanthology.org/2025.acl-long.1540/,https://aclanthology.org/2025.acl-long.1540.pdf,offline_acl,,"Enhancing the reasoning capabilities of language models (LMs) remains a key challenge, especially for tasks that require complex, multi-step decision-making where existing Chain-of-Thought (CoT) approaches struggle with consistency and verification. In this paper, we propose a novel reasoning framew"
101,yVGGtsOgc7,Disentangling Representations through Multi-task Learning,Pantelis Vafidis; Aman Bhargava; Antonio Rangel,2025,ICLR 2025,main,Poster,interpretability and explainable AI,zero-shot generalization;disentanglement;interpretability;world models;multi-task learning;computational neuroscience;neuroAI;evidence accumulation;cognitive maps;continuous attractors;RNNs;transformers,0,9.828,0.000,,https://iclr.cc/virtual/2025/poster/27723,https://openreview.net/pdf?id=yVGGtsOgc7,offline_iclr,,"Intelligent perception and interaction with the world hinges on internal representations that capture its underlying structure (""disentangled"" or ""abstract"" representations). Disentangled representations serve as world models, isolating latent factors of variation in the world along approximately or"
102,NQq9JLMfNN,Unified 3D Scene Understanding Through Physical World Modeling,,2026,ICLR 2026,main,Active,"unsupervised, self-supervised, semi-supervised, and supervised representation learning",3D Scene Undertstanding;Visual World Models,0,9.824,0.000,,https://openreview.net/forum?id=NQq9JLMfNN,,offline_iclr,,"Understanding 3D scenes requires flexible combinations of visual reasoning tasks, including depth estimation, novel view synthesis, and object manipulation, all of which are essential for perception and interaction. Existing approaches have typically addressed these tasks in isolation, preventing th"
103,DwOUndjwiV,Multi-View Masked World Models for Visual Robotic Manipulation,Younggyo Seo; Junsu Kim; Stephen James; Kimin Lee; Jinwoo Shin,2023,ICML 2023,main,Poster,,,0,9.820,0.000,,https://icml.cc/virtual/2023/poster/25176,https://openreview.net/pdf?id=DwOUndjwiV,offline_icml,,"Visual robotic manipulation research and applications often use multiple cameras, or views, to better perceive the world. How else can we utilize the richness of multi-view data? In this paper, we investigate how to learn good representations with multi-view data and utilize them for visual robotic "
104,29346,MoST: Multi-Modality Scene Tokenization for Motion Prediction,Norman Mu; Jingwei Ji; Zhenpei Yang; Nate Harada; Haotian Tang,2024,CVPR 2024,main,Poster,,,0,9.813,0.000,,https://cvpr.thecvf.com/virtual/2024/poster/29346,https://openaccess.thecvf.com/content/CVPR2024/papers/Mu_MoST_Multi-Modality_Scene_Tokenization_for_Motion_Prediction_CVPR_2024_paper.pdf,offline_cvpr,,Many existing motion prediction approaches rely on symbolic perception outputs to generate agent trajectories such as bounding boxes road graph information and traffic lights. This symbolic representation is a high-level abstraction of the real world which may render the motion prediction model vuln
105,oegbNuUrXV,Generalizable Dynamic Radiance Field in Egocentric View,DI QI; Tong Yang; Beining Wang; Xiangyu Zhang; Wenqiang Zhang,2025,ICLR 2025,main,Reject,"other topics in machine learning (i.e., none of the above)",generalized dynamic view synthesis;NeRF;computer vision,0,9.785,0.000,,https://openreview.net/forum?id=oegbNuUrXV,,offline_iclr,,"We present a novel framework for generalizable dynamic radiance field in egocentric view. Our approach can predict a 3D representation of the physical world at a given time based on a monocular video without test-time training. To this end, we use a contracted triplane as the 3D representation of ph"
106,9196783,Learning Affordance Space in Physical World for Vision-based Robotic Object Manipulation,Huadong Wu; Zhanpeng Zhang; Hui Cheng; Kai Yang; Jiaming Liu,2020,ICRA 2020,main,Poster,,,0,9.778,0.000,,https://ieeexplore.ieee.org/document/9196783/,,offline_icra,,"What is a proper representation for objects in manipulation? What would human try to perceive when manipulating a new object in a new environment? In fact, instead of focusing on the texture and illumination, human can infer the ""affordance"" [36] of the objects from vision. Here ""affordance"" describ"
107,M0Gv07MUMU,Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving,Thomas Tian; Boyi Li; Xinshuo Weng; Yuxiao Chen; Edward Schmerling,2024,CORL 2024,main,Poster,,Multi-modal LLM;Autonomous Driving;Representation Alignment,0,9.768,0.000,,https://openreview.net/forum?id=M0Gv07MUMU,,offline_corl,,"The autonomous driving industry is increasingly adopting end-to-end learning from sensory inputs to minimize human biases in system design. Traditional end-to-end driving models, however, suffer from long-tail events due to rare or unseen inputs within their training distributions. To address this, "
108,TuYC5Fpp7M,What drives success in physical planning with Joint-Embedding Predictive World Models?,,2026,ICLR 2026,main,Active,causal reasoning,Deep learning;world models;JEPA;robotics,0,9.726,0.000,,https://openreview.net/forum?id=TuYC5Fpp7M,,offline_iclr,,"A long-standing challenge in AI is to develop agents capable of solving a wide range of physical tasks and generalizing to new, unseen tasks and environments. A popular recent approach involves training a world model from state-action trajectories and subsequently use it with a planning algorithm to"
109,RSvfY6dRVN,Learning and Reusing Abstract Latent Actions in a Hippocampal-Entorhinal-Inspired World Model,,2026,ICLR 2026,main,Active,applications to neuroscience & cognitive science,brain-inspired model;hippocampal-entorhinal coupling;inverse model;latent action;structural generalization;self-supervised learning,0,9.726,0.000,,https://openreview.net/forum?id=RSvfY6dRVN,,offline_iclr,,"Humans are capable of abstracting dynamic experiences into structured representations, facilitating both the inference of shared patterns by observing similar transition dynamics and the transfer of these structures across varied contexts. The hippocampal-entorhinal circuit, widely known for its rol"
110,qVc7NWYTRZ6,An Unbiased Look at Datasets for Visuo-Motor Pre-Training,Sudeep Dasari; Mohan Kumar Srirama; Unnat Jain; Abhinav Gupta,2023,CORL 2023,main,Poster,,Visual Representation Learning;Datasets;Manipulation,0,9.690,0.000,,https://openreview.net/forum?id=qVc7NWYTRZ6,,offline_corl,"Our data-centric analysis busts some common myths in visuo-motor pre-training! We find that old-school datasets (e.g. ImageNet) outcompete SOTA robotics baselines (trained on 5x more Ego4D data), and offer simple guidelines for improving evaluation.","Visual representation learning hold great promise for robotics, but is severely hampered by the scarcity and homogeneity of robotics datasets. Recent works address this problem by pre-training visual representations on large-scale but out-of-domain data (e.g., videos of egocentric interactions) and "
111,lgOylcEZQgr,Online Unsupervised Learning of Visual Representations and Categories,Mengye Ren; Tyler R. Scott; Michael Louis Iuzzolino; Michael Curtis Mozer; Richard Zemel,2022,ICLR 2022,main,Reject,,Unsupervised learning;self-supervised learning;few-shot learning;visual representation learning;visual category learning,0,9.686,0.000,,https://openreview.net/forum?id=lgOylcEZQgr,,offline_iclr,,"Real world learning scenarios involve a nonstationary distribution of classes with sequential dependencies among the samples, in contrast to the standard machine learning formulation of drawing samples independently from a fixed, typically uniform distribution. Furthermore, real world interactions d"
112,4W1wTg7q9o,UrbanWorld: An Urban World Model for 3D City Generation,Yu Shang; Yuming Lin; Yu Zheng; Fan Hangyu; Jingtao Ding,2025,ICLR 2025,main,Reject,"applications to computer vision, audio, language, and other modalities",Urban world model;3D city generation,0,9.670,0.000,,https://openreview.net/forum?id=4W1wTg7q9o,,offline_iclr,,"Cities, as the essential environment of human life, encompass diverse physical elements such as buildings, roads and vegetation, which continuously interact with dynamic entities like people and vehicles. Crafting realistic, interactive 3D urban environments is essential for nurturing AGI systems an"
113,Bf6on28H0Jv,Masked World Models for Visual Control,Younggyo Seo; Danijar Hafner; Hao Liu; Fangchen Liu; Stephen James,2022,CORL 2022,main,Poster,,Visual model-based RL;World models,0,9.669,0.000,,https://openreview.net/forum?id=Bf6on28H0Jv,,offline_corl,We present a visual model-based RL framework that decouples visual representation learning and dynamics learning.,"Visual model-based reinforcement learning (RL) has the potential to enable sample-efficient robot learning from visual observations. Yet the current approaches typically train a single model end-to-end for learning both visual representations and dynamics, making it difficult to accurately model the"
114,fY4proGNFD,Reframing attention as a reinforcement learning problem for causal discovery,,2026,ICLR 2026,main,Active,causal reasoning,Causal World Models;Causal Reinforcement Learning;Causal Processes;Causal Representation Learning,0,9.573,0.000,,https://openreview.net/forum?id=fY4proGNFD,,offline_iclr,,"Formal frameworks of causality have operated largely parallel to modern trends in deep reinforcement learning (RL). However, there has been a revival of interest in formally grounding the representations learned by neural networks in causal concepts. Yet, most attempts at neural models of causality "
115,2efbfb1ddc,AtlantaNet: Inferring the 3D Indoor Layout from a Single 360(∘) Image beyond the Manhattan World Assumption,Giovanni Pintore; Marco Agus; Enrico Gobbetti,2020,ECCV 2020,main,Poster,,,0,9.562,0.000,,https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/604_ECCV_2020_paper.php,https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123530426.pdf,offline_eccv,,"We introduce a novel end-to-end approach to predict a 3D room layout from a single panoramic image. Compared to recent state-of-the-art works, our method is not limited to Manhattan World environments, and can reconstruct rooms bounded by vertical walls that do not form right angles or are curved --"
116,PPTrmvEnpW,Emergent World Models and Latent Variable Estimation in Chess-Playing Language Models,Adam Karvonen,2024,COLM 2024,main,Poster,,GPT;large language model;interpretability;world model,0,9.555,0.000,,https://openreview.net/forum?id=PPTrmvEnpW,,offline_colm,,"Language models have shown unprecedented capabilities, sparking debate over the source of their performance. Is it merely the outcome of learning syntactic patterns and surface level statistics, or do they extract semantics and a world model from the text? Prior work by Li et al. investigated this b"
117,GDYuzX0rwj,"Facing Off World Model Backbones: RNNs, Transformers, and S4",Fei Deng; Junyeong Park; Sungjin Ahn,2023,NIPS 2023,main,Poster,,world models;structured state space sequence models;S4;long-term memory;model-based reinforcement learning,0,9.538,0.000,,https://nips.cc/virtual/2023/poster/72223,https://openreview.net/pdf?id=GDYuzX0rwj,offline_nips,,"World models are a fundamental component in model-based reinforcement learning (MBRL). To perform temporally extended and consistent simulations of the future in partially observable environments, world models need to possess long-term memory. However, state-of-the-art MBRL agents, such as Dreamer, "
118,QQegZj99sk,AdaWorld: Learning Adaptable World Models with Latent Actions,Shenyuan Gao; Siyuan Zhou; Yilun Du; Jun Zhang; Chuang Gan,2025,ICML 2025,main,Poster,applications,World Model;Latent Action;Video Generation;Diffusion Model;Decision Making;Embodied AI,0,9.524,0.000,,https://icml.cc/virtual/2025/poster/45343,https://openreview.net/pdf?id=QQegZj99sk,offline_icml,,"World models aim to learn action-controlled future prediction and have proven essential for the development of intelligent agents. However, most existing world models rely heavily on substantial action-labeled data and costly training, making it challenging to adapt to novel environments with hetero"
119,10801627,A Framework for Neurosymbolic Goal-Conditioned Continual Learning in Open World Environments,Pierrick Lorang; Shivam Goel; Yash Shukla; Patrik Zips; Matthias Scheutz,2024,IROS 2024,main,Poster,,,0,9.517,0.000,,https://ieeexplore.ieee.org/document/10801627/,,offline_iros,,"In dynamic open-world environments, agents continually face new challenges due to sudden and unpredictable novelties, hindering Task and Motion Planning (TAMP) in autonomous systems. We introduce a novel TAMP architecture that integrates symbolic planning with reinforcement learning to enable autono"
120,M44RvNMZs4,Vector Quantization in the Brain: Grid-like Codes in World Models,Xiangyuan Peng; Xingsi Dong; Si Wu,2025,NIPS 2025,main,Spotlight,neuroscience_and_cognitive_science,Vector quantization;Brain inspired AI;Grid-like code;World models,0,9.506,0.000,,https://openreview.net/forum?id=M44RvNMZs4,,offline_nips,,"We propose Grid-like Code Quantization (GCQ), a brain-inspired method for compressing observation-action sequences into discrete representations using grid-like patterns in attractor dynamics. Unlike conventional vector quantization approaches that operate on static inputs, GCQ performs spatiotempor"
121,0Qyxw0cCuu,CONTROL: A Contrastive Learning Framework for Open World Semi-Supervised Learning,Jingyi Cui; Yi-Ge Zhang; Yisen Wang,2024,ICLR 2024,main,Reject,"unsupervised, self-supervised, semi-supervised, and supervised representation learning",Contrastive Learning; Semi-Supervised Learning,0,9.498,0.000,,https://openreview.net/forum?id=0Qyxw0cCuu,,offline_iclr,,"In recent years, open-world semi-supervised Learning has received tremendous attention. This is largely due to the fact that unlabeled real-world data often encompasses unseen classes -- those that are not represented in labeled datasets. Such classes can adversely affect the performance of traditio"
122,0GNBqoYcAP,Context and Diversity Matter: The Emergence of In-Context Learning in World Models,,2026,ICLR 2026,main,Active,"transfer learning, meta learning, and lifelong learning",In-Context Learning; World Models,0,9.473,0.000,,https://openreview.net/forum?id=0GNBqoYcAP,,offline_iclr,,The capability of predicting environmental dynamics underpins both biological neural systems and general embodied AI in adapting to their surroundings. Yet prevailing approaches rest on static world models that falter when confronted with novel or rare configurations. We investigate in-context learn
123,qR2TjMZ10B,On the Representation Degradation in Vision-Language-Action Models,,2026,ICLR 2026,main,Active,"applications to robotics, autonomy, planning",robot policy learning;vision-language-action models;representation learning,0,9.469,0.000,,https://openreview.net/forum?id=qR2TjMZ10B,,offline_iclr,,"Vision-Language-Action (VLA) models have become a promising paradigm for robotic decision-making, yet their application remains limited by generalization bottlenecks. In this paper, we conduct a layer-wise representation analysis and uncover a previously overlooked phenomenon of representation degra"
124,Ph7EfTA2DI,Homomorphism AutoEncoder --- Learning Group Structured Representations from Observed Transitions,Hamza Keurti; Hsiao-Ru Pan; Michel Besserve; Benjamin F Grewe; Bernhard Schölkopf,2023,ICML 2023,main,Poster,,,0,9.465,0.000,,https://icml.cc/virtual/2023/poster/24754,https://openreview.net/pdf?id=Ph7EfTA2DI,offline_icml,,"How can agents learn internal models that veridically represent interactions with the real world is a largely open question. As machine learning is moving towards representations containing not just observational but also interventional knowledge, we study this problem using tools from representatio"
125,JGr4Qv9vbz,Open-Ended 3D Metric-Semantic Representation Learning via Semantic-Embedded Gaussian Splatting,Yucheng Yan; Chen Liang; Wenguan Wang; Yi Yang,2025,ICLR 2025,main,Withdraw,"applications to computer vision, audio, language, and other modalities",3D Gaussian Splatting;Dense Visual SLAM;3D Scene Representation;Contrastive Learning,0,9.455,0.000,,https://openreview.net/forum?id=JGr4Qv9vbz,,offline_iclr,,"This work answers the question of whether it is feasible to create a comprehensive metric-semantic 3D virtual world using everyday devices equipped with multi-view stereo. We propose an open-ended metric-semantic representation learning framework based on 3D Gaussians, which distills open-set semant"
126,o24k_XfIe6_,Learning Knowledge Graph-based World Models of Textual Environments,Prithviraj Ammanabrolu; Mark Riedl,2021,NIPS 2021,main,Poster,,world models;text games;knowledge graphs;natural language processing,0,9.432,0.000,,https://nips.cc/virtual/2021/poster/28729,https://openreview.net/pdf?id=o24k_XfIe6_,offline_nips,"We teach agents to simultaneously model and act in interactive, situated, textual worlds by mapping them with knowledge graphs.","World models improve a learning agent's ability to efficiently operate in interactive and situated environments. This work focuses on the task of building world models of text-based game environments. Text-based games, or interactive narratives, are reinforcement learning environments in which agent"
127,pDDqLAaNWM,NeoWorld: Neural Simulation of Explorable Virtual Worlds via Progressive 3D Unfolding,,2026,ICLR 2026,main,Active,generative models,Interactive scene generation;hybrid 2D-3D representation,0,9.431,0.000,,https://openreview.net/forum?id=pDDqLAaNWM,,offline_iclr,,"We introduce NeoWorld, a deep learning framework for generating interactive 3D virtual worlds from a single input image. Inspired by the on-demand worldbuilding concept in the science fiction novel Simulacron-3 (1964), our system constructs expansive environments where only the regions actively expl"
128,E0cjqfM55C,Learning Interactive World Model for Object-Centric Reinforcement Learning,Fan Feng; Phillip Lippe; Sara Magliacane,2025,NIPS 2025,main,Poster,reinforcement_learning,World Model;Object-Centric RL,0,9.428,0.000,,https://openreview.net/forum?id=E0cjqfM55C,,offline_nips,,"Agents that understand objects and their interactions can learn policies that are more robust and transferable. However, most object-centric RL methods factor state by individual objects while leaving interactions implicit. We introduce the Factored Interactive Object-Centric World Model (FIOC-WM), "
129,aYqTwcDlCG,Learning World Models for Unconstrained Goal Navigation,Yuanlin Duan; Wensen Mao; He Zhu,2024,NIPS 2024,main,Poster,reinforcement_learning,World Models;Reinforcement Learning;Goal-Conditioned Reinforcement Learning;Model-Based Reinforcement Learning;Exploration Strategies,0,9.410,0.000,,https://neurips.cc/virtual/2024/poster/94541,https://openreview.net/pdf?id=aYqTwcDlCG,offline_nips,,"Learning world models offers a promising avenue for goal-conditioned reinforcement learning with sparse rewards. By allowing agents to plan actions or exploratory goals without direct interaction with the environment, world models enhance exploration efficiency. The quality of a world model hinges o"
130,xtlixzbcfV,Novelty Detection in Reinforcement Learning with World Models,Geigh Zollicoffer; Kenneth Eaton; Jonathan C Balloch; Julia Kim; Wei Zhou,2025,ICML 2025,main,Spotlight,reinforcement_learning->online,Anomaly Detection;Safety Mechanisms,0,9.404,0.000,,https://icml.cc/virtual/2025/poster/43561,https://openreview.net/pdf?id=xtlixzbcfV,offline_icml,,"Reinforcement learning (RL) using world models has found significant recent successes.
However, when a sudden change to world mechanics or properties occurs then agent performance and reliability can dramatically decline.
We refer to the sudden change in visual properties or state transitions as nov"
131,s3K0arSRl4d,TransDreamer: Reinforcement Learning with Transformer World Models,Chang Chen; Jaesik Yoon; Yi-Fu Wu; Sungjin Ahn,2022,ICLR 2022,main,Withdraw,,Model-Based Reinforcement Learning;Transformer World Models,0,9.403,0.000,,https://openreview.net/forum?id=s3K0arSRl4d,,offline_iclr,,"The Dreamer agent provides various benefits of Model-Based Reinforcement Learning (MBRL) such as sample efficiency, reusable knowledge, and safe planning. However, its world model and policy networks inherit the limitations of recurrent neural networks and thus an important question is how an MBRL f"
132,AEq0onGrN2,Physically Embodied Gaussian Splatting: A Visually Learnt and Physically Grounded 3D Representation for Robotics,Jad Abou-Chakra; Krishan Rana; Feras Dayoub; Niko Suenderhauf,2024,CORL 2024,main,Poster,,3D Representation;Gaussian Splatting;Robotics;Tracking;Physics,0,9.401,0.000,,https://openreview.net/forum?id=AEq0onGrN2,,offline_corl,,"For robots to robustly understand and interact with the physical world, it is highly beneficial to have a comprehensive representation  -- modelling geometry, physics, and visual observations -- that informs perception, planning, and control algorithms. We propose a novel dual ""Gaussian-Particle"" re"
133,6064,Active World Model Learning with Progress Curiosity,Kuno Kim; Megumi Sano; Julian De Freitas; Nick Haber; Daniel Yamins,2020,ICML 2020,main,Poster,,,0,9.378,0.000,,https://icml.cc/virtual/2020/poster/6064,http://proceedings.mlr.press/v119/kim20e/kim20e.pdf,offline_icml,,"World models are self-supervised predictive models of how the world evolves. Humans learn world models by curiously exploring their environment, in the process acquiring compact abstractions of high bandwidth sensory inputs, the ability to plan across long temporal horizons, and an understanding of "
134,FmBegXJToY,Procedural generalization by planning with self-supervised world models,Ankesh Anand; Jacob C Walker; Yazhe Li; Eszter Vértes; Julian Schrittwieser,2022,ICLR 2022,main,Poster,,Self-Supervised Learning;Model-Based RL;Generalization in RL,0,9.370,0.000,,https://iclr.cc/virtual/2022/poster/7007,https://openreview.net/pdf?id=FmBegXJToY,offline_iclr,,"One of the key promises of model-based reinforcement learning is the ability to generalize using an internal model of the world to make predictions in novel environments and tasks. However, the generalization ability of model-based agents is not well understood because existing work has focused on m"
135,CAz7UGRdLs,Raw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2),Zhenjie Yang; Xiaosong Jia; Qifeng Li; Xue Yang; Maoqing Yao,2025,NIPS 2025,main,Poster,applications,End-to-End Autonomous Driving;Reinforcement Learning,0,9.366,0.000,,https://openreview.net/forum?id=CAz7UGRdLs,,offline_nips,,"Reinforcement Learning (RL) can mitigate the causal confusion and distribution shift inherent to imitation learning (IL). However, applying RL to end-to-end autonomous driving (E2E-AD) remains an open problem for its training difficulty, and IL is still the mainstream paradigm in both academia and i"
136,SH3DTbytqF,Whale-X: Learning Scalable Embodied World Models with Enhanced Generalizability,Zhilong Zhang; Ruifeng Chen; Junyin Ye; Yihao Sun; Pengyuan Wang,2025,ICLR 2025,main,Withdraw,reinforcement learning,world model;sequential decision-making;embodied control,0,9.366,0.000,,https://openreview.net/forum?id=SH3DTbytqF,,offline_iclr,,"World models play a crucial role in decision-making within embodied environments, enabling cost-free explorations that would otherwise be expensive in the real world. However, to support faithful imagination in out-of-distribution (OOD) regions, world models must possess significant generalizability"
137,XTTbzC7O2T,Learning 3D Persistent Embodied World Models,Siyuan Zhou; Yilun Du; Yuncong Yang; Lei Han; Peihao Chen,2025,NIPS 2025,main,Poster,applications,Embodied World Models,0,9.364,0.000,,https://openreview.net/forum?id=XTTbzC7O2T,,offline_nips,,"The ability to simulate the effects of future actions on the world is a crucial ability of intelligent embodied agents, enabling agents to anticipate the effects of their actions and make plans accordingly. While a large body of existing work has explored how to construct such world models using vid"
138,article-35502,Acting Beyond Learning: Imagination-Assisted Decision-Making in the Visual-based Multi-Agent Cooperative Scenarios,Huanhuan Yang; Dianxi Shi; Songchang Jin; Guojun Xie; Yang Chen,2025,AAAI 2025,main,Technical,machine learning vi,,0,9.352,0.000,,https://ojs.aaai.org/index.php/AAAI/article/view/35502,https://ojs.aaai.org/index.php/AAAI/article/view/35502/37657,offline_aaai,,"Learning optimal policies in multi-agent cooperative settings with visual observations is significant and challenging. Agents must first perform state representation learning for their image observations and then learn policies in the abstracted state space. Aiming at this problem, we propose a nove"
139,n5dkjiplv4,Reinforcement Learning with Inverse Rewards for World Model Post-training,,2026,ICLR 2026,main,Active,generative models,world model;reinforcement learning;post-training,0,9.332,0.000,,https://openreview.net/forum?id=n5dkjiplv4,,offline_iclr,,"World models simulate dynamic environments, enabling agents to interact with diverse input modalities. Although recent advances have improved the visual quality and temporal consistency of video world models, their ability of accurately modeling human-specified actions remains underexplored. Reinfor"
140,Ix4Ytiwor4U,DITTO: Offline Imitation Learning with World Models,Branton DeMoss; Paul Duckworth; Nick Hawes; Ingmar Posner,2023,ICLR 2023,main,Reject,,world models;imitation learning;reinforcement learning,0,9.319,0.000,,https://openreview.net/forum?id=Ix4Ytiwor4U,,offline_iclr,"Completely offline imitation learning with world models, using RL on a latent matching objective in the model.","We propose DITTO, a fully offline approach to imitation learning which addresses the problem of covariate shift without access to an oracle or any additional online interactions. By unrolling agent policies in the latent space of a learned world model and penalizing drift from expert demonstrations,"
141,X_qYPtJLaX8,PRISM: Probabilistic Real-Time Inference in Spatial World Models,Atanas Mirchev; Baris Kayalibay; Ahmed Agha; Patrick van der Smagt; Daniel Cremers,2022,CORL 2022,main,Oral,,generative model;SLAM;Bayes filter;uncertainty;differentiable rendering,0,9.318,0.000,,https://openreview.net/forum?id=X_qYPtJLaX8,,offline_corl,We propose a probabilistic real-time filtering inference for a generative spatial model that features rendering and dynamics.,"We introduce PRISM, a method for real-time filtering in a probabilistic generative model of agent motion and visual perception. Previous approaches either lack uncertainty estimates for the map and agent state, do not run in real-time, do not have a dense scene representation or do not model agent d"
142,17501,Bisimulation Makes Analogies in Goal-Conditioned Reinforcement Learning,Philippe Hansen-Estruch; Amy Zhang; Ashvin Nair; Patrick Yin; Sergey Levine,2022,ICML 2022,main,Spotlight,,,0,9.314,0.000,,https://icml.cc/virtual/2022/poster/17501,https://proceedings.mlr.press/v162/hansen-estruch22a/hansen-estruch22a.pdf,offline_icml,,"Building generalizable goal-conditioned agents from rich observations is a key to reinforcement learning (RL) solving real world problems. Traditionally in goal-conditioned RL, an agent is provided with the exact goal they intend to reach. However, it is often not realistic to know the configuration"
143,GE0UKtI6Lf,WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents,Siyu Zhou; Tianyi Zhou; Yijun Yang; Guodong Long; Deheng Ye,2025,ICLR 2025,main,Desk Reject,"foundation or frontier models, including LLMs",world model;embodied agent;neurosymbolic;large language model,0,9.310,0.000,,https://openreview.net/forum?id=GE0UKtI6Lf,,offline_iclr,,"Can large language models (LLMs) directly serve as powerful world models for model-based agents? While the gaps between the prior knowledge of LLMs and the specified environment's dynamics do exist, our study reveals that the gaps can be bridged by aligning an LLM with its deployed environment and s"
144,33590,DrivingSphere: Building a High-fidelity 4D World for Closed-loop Simulation,Tianyi Yan; Dongming Wu; Wencheng Han; Junpeng Jiang; Xia Zhou,2025,CVPR 2025,main,Poster,,,0,9.296,0.000,,https://cvpr.thecvf.com/virtual/2025/poster/33590,https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_DrivingSphere_Building_a_High-fidelity_4D_World_for_Closed-loop_Simulation_CVPR_2025_paper.pdf,offline_cvpr,,"Autonomous driving evaluation requires simulation environments that closely replicate actual road conditions, including real-world sensory data and responsive feedback loops. However, many existing simulations need to predict waypoints along fixed routes on public datasets or synthetic photorealisti"
145,Ro2eG1RRde,Towards Policy-Aware World Models,,2026,ICLR 2026,main,Active,reinforcement learning,world models;reinforcement learning,0,9.295,0.000,,https://openreview.net/forum?id=Ro2eG1RRde,,offline_iclr,,"World models have received significant attention from the robotics and computer vision community, both of whom have started scaling to networks comprising billions of parameters in the hope of unlocking new robot skills. In this paradigm, models are pre-trained on internet-scale data and then fine-t"
146,8aae7a0b2b,Constrained Contrastive Reinforcement Learning,Haoyu Wang; Xinrui Yang; Yuhang Wang; Lan Xuguang,2022,ACML 2022,main,Poster,,,0,9.262,0.000,,https://proceedings.mlr.press/v189/wang23a.html,https://proceedings.mlr.press/v189/wang23a/wang23a.pdf,offline_acml,,"Learning to control from complex observations
 remains a major challenge in the application of
 model-based reinforcement learning (MBRL). Existing
 MBRL methods apply contrastive learning to replace
 pixel-level reconstruction, improving the
 performance of the latent world model. However,
 previou"
147,DotYL0mTxZ,Stealthy World Model Manipulation via Data Poisoning,,2026,ICLR 2026,main,Active,"alignment, fairness, safety, privacy, and societal considerations",World Model;Data Poisoning;Optimization,0,9.259,0.000,,https://openreview.net/forum?id=DotYL0mTxZ,,offline_iclr,,"Model-based learning agents that use a world model to predict and plan have shown impressive success in solving diverse, complex tasks and adapting to new environments. However, the process of exploring open environments and updating the model with collected experience also exposes them to adversari"
148,06550,Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples,Wei Duan; Junyu Xuan; Maoying Qiao; Jie Lu,2022,AAAI 2022,main,Technical,Machine Learning I,,0,9.250,0.000,,https://aaai.org/papers/06550-learning-from-the-dark-boosting-graph-convolutional-neural-networks-with-diverse-negative-samples/,https://cdn.aaai.org/ojs/20608/20608-13-24621-1-2-20220628.pdf,offline_aaai,,Graph Convolutional Neural Networks (GCNs) have been generally accepted to be an effective tool for node representations learning. An interesting way to understand GCNs is to think of them as a message passing mechanism where each node updates its representation by accepting information from its nei
149,I3DFJJbBMn,A Unified Understanding and Generation Framework for Ego-Centric Tracing in Dynamic World,Guangyao Li; Xin Wang; Tongtong Feng; Ren Wang; Yu-Wei Zhan,2026,ICLR 2026,main,Withdraw,"applications to computer vision, audio, language, and other modalities",Scene Understanding;Ego-Centric Tracing,0,9.240,0.000,,https://openreview.net/forum?id=I3DFJJbBMn,,offline_iclr,,"Ego-centric tracing with sparse yet informative cues is a fundamental capability of embodied agents operating in complex and dynamic environments. However, existing approaches typically address cue understanding and cue generation in isolation, which limits their synergy and significantly constrains"
150,jH6K80njJA,Structured RAG for Answering Aggregative Questions,,2026,ICLR 2026,main,Active,"applications to computer vision, audio, language, and other modalities",NLP;RAG;Question Answering;LLM;Aggregative Questions;Reasoning;Structured Representation,0,9.222,0.000,,https://openreview.net/forum?id=jH6K80njJA,,offline_iclr,,"Retrieval-Augmented Generation (RAG) has become the dominant approach for answering questions over large corpora. However, current datasets and methods are highly focused on cases where only a small part of the corpus (usually a few paragraphs) is relevant per query, and fail to capture the rich wor"
151,2022.emnlp-main.86,PLM-based World Models for Text-based Games,Minsoo Kim; Yeonjoon Jung; Dohyeon Lee; Seung-won Hwang,2022,EMNLP 2022,main,Main,,,0,9.210,0.000,,https://aclanthology.org/2022.emnlp-main.86/,https://aclanthology.org/2022.emnlp-main.86.pdf,offline_emnlp,,"World models have improved the ability of reinforcement learning agents to operate in a sample efficient manner, by being trained to predict plausible changes in the underlying environment. As the core tasks of world models are future prediction and commonsense understanding, our claim is that pre-t"
152,article-30282,Continual Learning in an Open and Dynamic World,Yunhui Guo,2024,AAAI 2024,new faculty highlights,Technical,,,0,9.208,0.000,,https://ojs.aaai.org/index.php/AAAI/article/view/30282,https://ojs.aaai.org/index.php/AAAI/article/view/30282/32279,offline_aaai,,"Building autonomous agents that can process massive amounts of real-time sensor-captured data is essential for many real-world applications including autonomous vehicles, robotics and AI in medicine. As the agent often needs to explore in a dynamic environment, it is thus a desirable as well as chal"
153,q57PbzNl1c,Visual Smell: Learning Olfactory Representations for the Natural World,Ege Ozguroglu; Junbang Liang; Ruoshi Liu; Mia Chiquier; Michael DeTienne,2026,ICLR 2026,main,Withdraw,"applications to computer vision, audio, language, and other modalities",olfaction;cross-modal learning;computer vision,0,9.206,0.000,,https://openreview.net/forum?id=q57PbzNl1c,,offline_iclr,,"Olfaction---the ability to sense volatile molecules in the air---is a key way that animals, and to a lesser extent humans, perceive the world. However, this rich ``chemical world,'' is largely imperceptible to machines. One of the major obstacles to applying this approach to olfaction is the lack of"
154,YXRyYkb1im,COMBO: Compositional World Models for Embodied Multi-Agent Cooperation,Hongxin Zhang; Zeyuan Wang; Qiushi Lyu; Zheyuan Zhang; Sunli Chen,2025,ICLR 2025,main,Poster,"applications to robotics, autonomy, planning",Embodied AI; Multi-agent Planning; Compositional World Model,0,9.200,0.000,,https://iclr.cc/virtual/2025/poster/29260,https://openreview.net/pdf?id=YXRyYkb1im,offline_iclr,,"In this paper, we investigate the problem of embodied multi-agent cooperation, where decentralized agents must cooperate given only egocentric views of the world. To effectively plan in this setting, in contrast to learning world dynamics in a single-agent scenario, we must simulate world dynamics c"
155,3Cr6C2zNKw,Uncovering Untapped Potential in Sample-Efficient World Model Agents,Lior Cohen; Kaixin Wang; Bingyi Kang; Uri Gadot; Shie Mannor,2025,NIPS 2025,main,Reject,reinforcement_learning,world models;deep reinforcement learning;intrinsic motivation;sample efficiency,0,9.198,0.000,,https://openreview.net/forum?id=3Cr6C2zNKw,,offline_nips,,"World model (WM) agents enable sample-efficient reinforcement learning by learning policies entirely from simulated experience.
However, existing token-based world models (TBWMs) are limited to visual inputs and discrete actions, restricting their adoption and applicability. Moreover, although both "
156,WwwXi3rkUW,Co-Evolving Latent Action World Models,,2026,ICLR 2026,main,Active,reinforcement learning,Latent Action Model;World Model;Reinforcement Learning;Video Generation Model,0,9.197,0.000,,https://openreview.net/forum?id=WwwXi3rkUW,,offline_iclr,,"Adapting pre-trained video generation models into controllable world models via *latent actions* is a promising step towards creating generalist world models. The dominant paradigm adopts a two-stage approach that trains the latent action model (LAM) and the world model separately, resulting in redu"
157,0oabwyZbOu,Mastering Atari with Discrete World Models,Danijar Hafner; Timothy P Lillicrap; Mohammad Norouzi; Jimmy Ba,2021,ICLR 2021,main,Poster,,Atari;world models;model-based reinforcement learning;reinforcement learning;planning;actor critic,0,9.197,0.000,,https://iclr.cc/virtual/2021/poster/2742,https://openreview.net/pdf?id=0oabwyZbOu,offline_iclr,,Intelligent agents need to generalize from past experience to achieve goals in complex environments. World models facilitate such generalization and allow learning behaviors from imagined outcomes to increase sample-efficiency. While learning world models from image inputs has recently become feasib
158,LmOF7UAOZ7,A Planar-Symmetric SO(3) Representation for Learning Grasp Detection,Tianyi Ko; Takuya Ikeda; Hiroya Sato; Koichi Nishiwaki,2024,CORL 2024,main,Poster,,Grasp Detection;Rotation Representation;Parallel Gripper,0,9.190,0.000,,https://openreview.net/forum?id=LmOF7UAOZ7,,offline_corl,,"Planar-symmetric hands, such as parallel grippers, are widely adopted in both research and industrial fields.
Their symmetry, however, introduces ambiguity and discontinuity in the SO(3) representation, which hinders both the training and inference of neural network-based grasp detectors.
We propose"
159,eTXTOUrrhY,FOLIAGE: a Latent World Model for Accretive Surface Growth,,2026,ICLR 2026,main,Active,"applications to computer vision, audio, language, and other modalities",Surface Growth;World Model;Multimodal,0,9.186,0.000,,https://openreview.net/forum?id=eTXTOUrrhY,,offline_iclr,,"Accretive surfaces grow by adding material and changing rest metrics, producing emergent, complex, and changing morphologies. We introduce FOLIAGE, a geometry-centric latent world model that infers a deployable state from heterogeneous, partial sensors and predicts its action-conditioned evolution. "
160,29691,Neural Underwater Scene Representation,Yunkai Tang; Chengxuan Zhu; Renjie Wan; Chao Xu; Boxin Shi,2024,CVPR 2024,main,Poster,,,0,9.164,0.000,,https://cvpr.thecvf.com/virtual/2024/poster/29691,https://openaccess.thecvf.com/content/CVPR2024/papers/Tang_Neural_Underwater_Scene_Representation_CVPR_2024_paper.pdf,offline_cvpr,,Among the numerous efforts towards digitally recovering the physical world Neural Radiance Fields (NeRFs) have proved effective in most cases. However underwater scene introduces unique challenges due to the absorbing water medium the local change in lighting and the dynamic contents in the scene. W
161,cydXirmduY,Emergent World Representations in OpenVLA,,2026,ICLR 2026,main,Active,interpretability and explainable AI,World Representation;Policy Based RL;VLA;SAE,0,9.160,0.000,,https://openreview.net/forum?id=cydXirmduY,,offline_iclr,,"Vision Language Action models (VLAs) exhibit complex control behaviors without explicitly modeling environmental dynamics. However, it remains unclear whether VLAs implicitly learn world models, a hallmark of model-based RL. We propose an experimental methodology using embedding arithmetic on state "
162,34450,Neural Motion Simulator Pushing the Limit of World Models in Reinforcement Learning,Chenjie Hao; Weyl Lu; Yifan Xu; Yubei Chen,2025,CVPR 2025,main,Poster,,,0,9.160,0.000,,https://cvpr.thecvf.com/virtual/2025/poster/34450,https://openaccess.thecvf.com/content/CVPR2025/papers/Hao_Neural_Motion_Simulator_Pushing_the_Limit_of_World_Models_in_CVPR_2025_paper.pdf,offline_cvpr,,"An embodied system must not only model the patterns of the external world but also understand its own motion dynamics. A motion dynamic model is essential for efficient skill acquisition and effective planning. In this work, we introduce the neural motion simulator (MoSim), a world model that predic"
163,3CnxNqmklv,DreamGen: Unlocking Generalization in Robot Learning through Video World Models,Joel Jang; Seonghyeon Ye; Zongyu Lin; Jiannan Xiang; Johan Bjorck,2025,CORL 2025,main,Poster,,Video World Models;Synthetic Data;Behavior Generalization;Environment Generalization,0,9.157,0.000,,https://openreview.net/forum?id=3CnxNqmklv,,offline_corl,,"In this work, we unlock new capabilities in robot learning from neural trajectories, synthetic robot data generated from video world models. Our proposed recipe is simple, but powerful: we take the most recent state-of-the-art video generative models (world models), adapt them to the target robot em"
164,uFTLo48OHF,Social World Model-Augmented Mechanism Design Policy Learning,Xiaoyuan Zhang; Yizhe Huang; Chengdong Ma; Zhixun Chen; Long Ma,2025,NIPS 2025,main,Poster,reinforcement_learning,Model-based Reinforcement Learning,0,9.152,0.000,,https://openreview.net/forum?id=uFTLo48OHF,,offline_nips,,"Designing adaptive mechanisms to align individual and collective interests remains a central challenge in artificial social intelligence. Existing methods often struggle with modeling heterogeneous agents possessing persistent latent traits (e.g., skills, preferences) and dealing with complex multi-"
165,18211,Toward Compositional Generalization in Object-Oriented World Modeling,Linfeng Zhao; Lingzhi Kong; Robin Walters; Lawson L.S. Wong,2022,ICML 2022,main,Oral,,,0,9.137,0.000,,https://icml.cc/virtual/2022/poster/18211,https://proceedings.mlr.press/v162/zhao22b/zhao22b.pdf,offline_icml,,Compositional generalization is a critical ability in learning and decision-making. We focus on the setting of reinforcement learning in object-oriented environments to study compositional generalization in world modeling. We (1) formalize the compositional generalization problem with an algebraic a
166,5j6wtOO6Fk,Hieros: Hierarchical Imagination on Structured State Space Sequence World Models,Paul Mattes; Rainer Schlosser; Ralf Herbrich,2024,ICLR 2024,main,Reject,reinforcement learning,Reinforcement Learning;Hierarchical Models;Deep Learning;Structured State Space Model,0,9.129,0.000,,https://openreview.net/forum?id=5j6wtOO6Fk,,offline_iclr,,"One of the biggest challenges to modern deep reinforcement learning (DRL) algorithms is sample efficiency. Many approaches learn a world model in order to train an agent entirely in imagination, eliminating the need for direct environment interaction during training. However, these methods often suf"
167,paper244,Belief Merging Operators as Maximum Likelihood Estimators,Patricia Everaere; Sebastien Konieczny; Pierre Marquis,2020,IJCAI 2020,main,Poster,Knowledge Representation and Reasoning,"Knowledge Representation and Reasoning: Belief Change, Belief Merging",0,9.121,0.000,,https://www.ijcai.org/proceedings/2020/244,https://www.ijcai.org/proceedings/2020/0244.pdf,offline_ijcai,,"We study how belief merging operators can be considered as maximum likelihood estimators, i.e., we assume that there exists a (unknown) true state of the world and that each agent participating in the merging process receives a noisy signal of it, characterized by a noise model. The objective is the"
168,CQKxhmLobo,Uncertainty-aware Latent Safety Filters for Avoiding Out-of-Distribution Failures,Junwon Seo; Kensuke Nakamura; Andrea Bajcsy,2025,CORL 2025,main,Poster,,safe control;uncertainty quantification;world models,0,9.107,0.000,,https://openreview.net/forum?id=CQKxhmLobo,,offline_corl,,"Recent advances in generative world models have enabled classical safe control methods, such as Hamilton-Jacobi (HJ) reachability, to generalize to complex robotic systems operating directly from high-dimensional sensor observations. However, obtaining comprehensive coverage of all safety-critical s"
169,dlIoumNiXt,General agents need world models,Jonathan Richens; Tom Everitt; David Abel,2025,ICML 2025,main,Poster,theory->reinforcement_learning_and_planning,Agents;world models;reinforcement learning;goal-conditioned reinforcement learning;causality;generalization,0,9.104,0.000,,https://icml.cc/virtual/2025/poster/44620,https://openreview.net/pdf?id=dlIoumNiXt,offline_icml,,"Are world models a necessary ingredient for flexible, goal-directed behaviour, or is model-free learning sufficient?
We provide a formal answer to this question, showing that any agent capable of generalizing to multi-step goal-directed tasks must have learned a predictive model of its environment.
"
170,Peg7mkjzvyP,iPTR: Learning a representation for interactive program translation retrieval,Binger Chen; Ziawasch Abedjan,2021,ICLR 2021,main,Reject,,,0,9.099,0.000,,https://openreview.net/forum?id=Peg7mkjzvyP,,offline_iclr,,"Program translation contributes to many real world scenarios, such as porting codebases written in an obsolete or deprecated language to a modern one or re-implementing existing projects in one's preferred programming language. Existing data-driven approaches either require large amounts of training"
171,2gTEW29qsM,Masked Generative Priors Improve World Models Sequence Modelling Capabilities,Cristian Meo; Mircea Tudor Lică; Zarif Ikram; Akihiro Nakano; Vedant Shah,2025,ICLR 2025,main,Reject,reinforcement learning,World Modeling;Model based RL,0,9.095,0.000,,https://openreview.net/forum?id=2gTEW29qsM,,offline_iclr,,"Deep Reinforcement Learning (RL) has become the leading approach for creating artificial agents in complex environments. Model-based approaches, which are RL methods with world models that predict environment dynamics, are among the most promising directions for improving data efficiency, forming a "
172,9561385,Ego-centric Stereo Navigation Using Stixel World,Shiyu Feng; Fanzhe Lyu; Jin Ha Hwang; Patricio A. Vela; Shiyu Feng,2021,ICRA 2021,main,Poster,,,0,9.089,0.000,,https://ieeexplore.ieee.org/document/9561385/,,offline_icra,,"This paper explores the use of passive, stereo sensing for vision-based navigation. The traditional approach uses dense depth algorithms, which can be computationally costly or potentially inaccurate. These drawbacks compound when including the additional computational demands associated to the sens"
173,wbZCBBrq3W,RoboScape: Physics-informed Embodied World Model,Yu Shang; Xin Zhang; Yinzhou Tang; Lei Jin; Chen Gao,2025,NIPS 2025,main,Spotlight,deep_learning,Embodied World model;Physics Priors;Video Generation,0,9.087,0.000,,https://openreview.net/forum?id=wbZCBBrq3W,,offline_nips,,"World models have become indispensable tools for embodied intelligence, serving as powerful simulators capable of generating realistic robotic videos while addressing critical data scarcity challenges. However, current embodied world models exhibit limited physical awareness, particularly in modelin"
174,rMQvbxxmLe,From Forecasting to Planning: Policy World Model for Collaborative State-Action Prediction,Zhida Zhao; Talas Fu; Yifan Wang; Lijun Wang; Huchuan Lu,2025,NIPS 2025,main,Poster,deep_learning,World model;Autonomous driving;End-to-end planning,0,9.069,0.000,,https://openreview.net/forum?id=rMQvbxxmLe,,offline_nips,,"Despite remarkable progress in driving world models, their potential for autonomous systems remains largely untapped: the world models are mostly learned for world simulation and decoupled from trajectory planning. While recent efforts aim to unify world modeling and planning in a single framework, "
175,RnJY9WcpA3,Sensor-Invariant Tactile Representation,Harsh Gupta; Yuchen Mo; Shengmiao Jin; Wenzhen Yuan,2025,ICLR 2025,main,Poster,"applications to computer vision, audio, language, and other modalities",Tactile sensing;representation learning,0,9.049,0.000,,https://iclr.cc/virtual/2025/poster/29640,https://openreview.net/pdf?id=RnJY9WcpA3,offline_iclr,,"High-resolution tactile sensors have become critical for embodied perception and robotic manipulation. 
However, a key challenge in the field is the lack of transferability between sensors due to design and manufacturing variations, which result in significant differences in tactile signals. 
This l"
176,1O8Jye1P0k,TWISTED: Enhancing Transformer World Models with Spatio-Temporal Encoding and Graph-Based Optimal Decoding,,2026,ICLR 2026,main,Active,reinforcement learning,model-based rl;vision-based rl;transformer world model,0,9.043,0.000,,https://openreview.net/forum?id=1O8Jye1P0k,,offline_iclr,,"Model-based reinforcement learning improves sample efficiency by using learned world models to simulate experiences for training agents.
Recent world models that leverage transformers demonstrate high quality simulations, leading to better agent performance.
However, transformer world models underut"
177,QklhZ70C49,PO-Dreamer: Memory Guided World Models for Partially Observable Reinforcement Learning,,2026,ICLR 2026,main,Active,reinforcement learning,reinforcement learning;world model,0,9.038,0.000,,https://openreview.net/forum?id=QklhZ70C49,,offline_iclr,,"World models predict future states and rewards by learning compact state representations of the environment, thereby enabling efficient policy optimization. World-model-based reinforcement learning (RL) algorithms have demonstrated significant advantages in complex tasks. However the scenarios in re"
178,21987,Self-Supervised Representation Learning for CAD,Benjamin T. Jones; Michael Hu; Milin Kodnongbua; Vladimir G. Kim; Adriana Schulz,2023,CVPR 2023,main,Poster,,,0,9.034,0.000,,https://cvpr.thecvf.com/virtual/2023/poster/21987,https://openaccess.thecvf.com/content/CVPR2023/papers/Jones_Self-Supervised_Representation_Learning_for_CAD_CVPR_2023_paper.pdf,offline_cvpr,,"Virtually every object in the modern world was created, modified, analyzed and optimized using computer aided design (CAD) tools. An active CAD research area is the use of data-driven machine learning methods to learn from the massive repositories of geometric and program representations. However, t"
179,CLpxpXqqBV,Learning State Representations via Retracing in Reinforcement Learning,Changmin Yu; Dong Li; Jianye HAO; Jun Wang; Neil Burgess,2022,ICLR 2022,main,Poster,,Representation learning;model-based reinforcement learning,0,9.033,0.000,,https://iclr.cc/virtual/2022/poster/7072,https://openreview.net/pdf?id=CLpxpXqqBV,offline_iclr,,"We propose learning via retracing, a novel self-supervised approach for learning the state representation (and the associated dynamics model) for reinforcement learning tasks. In addition to the predictive (reconstruction) supervision in the forward direction, we propose to include ""retraced"" transi"
180,kjZlzuVJF0,Boosting Multi-Agent Reinforcement Learning via Transition-Informed Representations,Mingxiao Feng; Wengang Zhou; Yaodong Yang; Houqiang Li,2024,ICLR 2024,main,Reject,reinforcement learning,SSL;MARL,0,9.033,0.000,,https://openreview.net/forum?id=kjZlzuVJF0,,offline_iclr,,"Effective coordination among agents in a multi-agent system necessitates an understanding of the underlying dynamics of the environment. 
However, in the context of multi-agent reinforcement learning (MARL), agent partially observed information leads to a lack of consideration for agent interactions"
181,,Learning Triadic Belief Dynamics in Nonverbal Communication From Videos,Lifeng Fan; Shuwen Qiu; Zilong Zheng; Tao Gao; Song-Chun Zhu,2021,CVPR 2021,main,Poster,,,0,9.029,0.000,,,https://openaccess.thecvf.com/content/CVPR2021/papers/Fan_Learning_Triadic_Belief_Dynamics_in_Nonverbal_Communication_From_Videos_CVPR_2021_paper.pdf,offline_cvpr,,"Humans possess a unique social cognition capability; nonverbal communication can convey rich social information among agents. In contrast, such crucial social characteristics are mostly missing in the existing scene understanding literature. In this paper, we incorporate different nonverbal communic"
182,3rSeDrPj4B,AsymDreamer: Safe Reinforcement Learning From Pixels with Privileged World Models,Dongchi Huang; Kaige Zhang; Yang Li; Yi Zhan; Chunhe Xia,2025,ICLR 2025,main,Reject,reinforcement learning,Safe Reinforcement Learing; World Model,0,9.025,0.000,,https://openreview.net/forum?id=3rSeDrPj4B,,offline_iclr,,"Safe Reinforcement Learning from partial observations frequently struggles with rapid performance degradation and often fails to satisfy safety constraints. Upon deeper analysis, we attribute this problem to the lack of necessary information in partial observations and inadequate sample efficiency. "
183,TjCDNssXKU,Learning Hierarchical World Models with Adaptive Temporal Abstractions from Discrete Latent Dynamics,Christian Gumbsch; Noor Sajid; Georg Martius; Martin V. Butz,2024,ICLR 2024,main,Spotlight,reinforcement learning,world models;temporal abstraction;hierarchical learning;model-based reinforcement learning;hierarchical planning,0,9.025,0.000,,https://iclr.cc/virtual/2024/poster/18558,https://openreview.net/pdf?id=TjCDNssXKU,offline_iclr,,"Hierarchical world models can significantly improve model-based reinforcement learning (MBRL) and planning by enabling reasoning across multiple time scales. Nonetheless, the majority of state-of-the-art MBRL methods employ flat, non-hierarchical models. We propose Temporal Hierarchies from Invarian"
184,2025.findings-acl.1337,Text2World: Benchmarking Large Language Models for Symbolic World Model Generation,Mengkang Hu; Tianxing Chen; Yude Zou; Yuheng Lei; Qiguang Chen,2025,ACL 2025,main,finding,,,0,9.025,0.000,,https://aclanthology.org/2025.findings-acl.1337/,https://aclanthology.org/2025.findings-acl.1337.pdf,offline_acl,,"Recently, there has been growing interest in leveraging large language models (LLMs) to generate symbolic world models from textual descriptions. Although LLMs have been extensively explored in the context of world modeling, prior studies encountered several challenges, including evaluation randomne"
185,jpiSagi8aV,RLVR-World: Training World Models with Reinforcement Learning,Jialong Wu; Shaofeng Yin; Ningya Feng; Mingsheng Long,2025,NIPS 2025,main,Poster,deep_learning,world models;reinforcement learning with verifiable rewards,0,9.022,0.000,,https://openreview.net/forum?id=jpiSagi8aV,,offline_nips,,"World models predict state transitions in response to actions and are increasingly developed across diverse modalities. However, standard training objectives such as maximum likelihood estimation (MLE) often misalign with task-specific goals of world models, i.e., transition prediction metrics like "
186,FzfYoUp8F1,Learning World Models for Interactive Video Generation,Taiye Chen; Xun Hu; Zihan Ding; Chi Jin,2025,NIPS 2025,main,Poster,deep_learning,world model;diffusion model;video generation,0,9.016,0.000,,https://openreview.net/forum?id=FzfYoUp8F1,,offline_nips,,"Foundational world models must be both interactive and preserve spatialtemporal coherence to enable effective future planning with different action choices. However, present models for long video generation have limited inherent world modeling capabilities due to two main challenges: compounding err"
187,ph68z0OGHX,Value-aligned World Model Regularization for Model-based Reinforcement Learning,,2026,ICLR 2026,main,Active,reinforcement learning,Model-based reinforcement learning; World model; Value-aligned Regularization,0,9.014,0.000,,https://openreview.net/forum?id=ph68z0OGHX,,offline_iclr,,"Model-based reinforcement learning (MBRL) aims to construct world models for imagined interactions to enable efficient sampling. Based on training strategy, current mainstream algorithms can be categorized into two types: maximum likelihood and value-aware world models. The former adopts structured "
188,hOELrZfg0J,PWM: Policy Learning with Multi-Task World Models,Ignat Georgiev; Varun Giridhar; Nicklas Hansen; Animesh Garg,2025,ICLR 2025,main,Poster,reinforcement learning,reinforcement learning;model-based reinforcement learning;continuous control;world models,0,9.007,0.000,,https://iclr.cc/virtual/2025/poster/28766,https://openreview.net/pdf?id=hOELrZfg0J,offline_iclr,,Reinforcement Learning (RL) has made significant strides in complex tasks but struggles in multi-task settings with different embodiments. World model methods offer scalability by learning a simulation of the environment but often rely on inefficient gradient-free optimization methods for policy ext
189,Osh7u2E1kC,Leveraging Separated World Model for Exploration in Visually Distracted Environments,Kaichen Huang; Shenghua Wan; Minghao Shao; Hai-Hang Sun; Le Gan,2024,NIPS 2024,main,Poster,reinforcement_learning,unsupervised RL;separate world model;visual inputs with distractors;minimax optimization,0,9.002,0.000,,https://neurips.cc/virtual/2024/poster/95344,https://openreview.net/pdf?id=Osh7u2E1kC,offline_nips,,"Model-based unsupervised reinforcement learning (URL) has gained prominence for reducing environment interactions and learning general skills using intrinsic rewards. However, distractors in observations can severely affect intrinsic reward estimation, leading to a biased exploration process, especi"
190,obwRcksFZw,PoE-World: Compositional World Modeling with Products of Programmatic Experts,Wasu Top Piriyakulkij; Yichao Liang; Hao Tang; Adrian Weller; Marta Kryven,2025,NIPS 2025,main,Spotlight,general_machine_learning,program synthesis;compositionality;world modeling,0,8.998,0.000,,https://openreview.net/forum?id=obwRcksFZw,,offline_nips,,"Learning how the world works is central to building AI agents that can adapt to complex environments. 
Traditional world models based on deep-learning demand vast amounts of training data, and do not flexibly update their knowledge from sparse observations. 
Recent advances in program synthesis usin"
191,06499c66ea,Relative Contrastive Loss for Unsupervised Representation Learning,Shixiang Tang; Feng Zhu; Lei Bai; Rui Zhao; Wanli Ouyang,2022,ECCV 2022,main,Poster,,,0,8.988,0.000,,https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/6602_ECCV_2022_paper.php,https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136870001.pdf,offline_eccv,,"""Defining positive and negative samples is critical for learning visual variations of the semantic classes in an unsupervised manner. Previous methods either construct positive sample pairs as different data augmentations on the same image (i.e., single-instance-positive) or estimate a class prototy"
192,HyxjNyrtPr,RGBD-GAN: Unsupervised 3D Representation Learning From Natural Image Datasets via RGBD Image Synthesis,Atsuhiro Noguchi; Tatsuya Harada,2020,ICLR 2020,main,Poster,,image generation;3D vision;unsupervised representation learning,0,8.985,0.000,,https://openreview.net/forum?id=HyxjNyrtPr,,offline_iclr,RGBD image generation for unsupervised camera parameter conditioning,"Understanding three-dimensional (3D) geometries from two-dimensional (2D) images without any labeled information is promising for understanding the real world without incurring annotation cost. We herein propose a novel generative model, RGBD-GAN, which achieves unsupervised 3D representation learni"
193,RN7RzMxwjC,Harmony World Models: Boosting Sample Efficiency for Model-based Reinforcement Learning,Haoyu Ma; Jialong Wu; Ningya Feng; Jianmin Wang; Mingsheng Long,2024,ICLR 2024,main,Reject,reinforcement learning,model-based reinforcemet learning;world model,0,8.984,0.000,,https://openreview.net/forum?id=RN7RzMxwjC,,offline_iclr,,"Model-based reinforcement learning (MBRL) holds the promise of sample-efficient learning by utilizing a world model, which models how the environment works and typically encompasses components for two tasks: observation modeling and reward modeling. In this paper, through a dedicated empirical inves"
194,31676,Do Vision and Language Encoders Represent the World Similarly?,Mayug Maniparambil; Raiymbek Akshulakov; Yasser Abdelaziz Dahou Djilali; Mohamed El Amine Seddik; Sanath Narayan,2024,CVPR 2024,main,Poster,,,0,8.976,0.000,,https://cvpr.thecvf.com/virtual/2024/poster/31676,https://openaccess.thecvf.com/content/CVPR2024/papers/Maniparambil_Do_Vision_and_Language_Encoders_Represent_the_World_Similarly_CVPR_2024_paper.pdf,offline_cvpr,,Aligned text-image encoders such as CLIP have become the de-facto model for vision-language tasks. Furthermore modality-specific encoders achieve impressive performances in their respective domains. This raises a central question: does an alignment exist between uni-modal vision and language encoder
195,WxnrX42rnS,STORM: Efficient Stochastic Transformer based World Models for Reinforcement Learning,Weipu Zhang; Gang Wang; Jian Sun; Yetian Yuan; Gao Huang,2023,NIPS 2023,main,Poster,,deep learning;reinforcement learning;model-based reinforcement learning;world model;learning in imagination;transformer;variational autoencoders;sequence modeling,0,8.971,0.000,,https://nips.cc/virtual/2023/poster/71385,https://openreview.net/pdf?id=WxnrX42rnS,offline_nips,,"Recently, model-based reinforcement learning algorithms have demonstrated remarkable efficacy  in visual input environments. These approaches begin by constructing a parameterized simulation world model of the real environment through self-supervised learning. By leveraging the imagination of the wo"
196,,Self-Supervised Representation Learning From Flow Equivariance,Yuwen Xiong; Mengye Ren; Wenyuan Zeng; Raquel Urtasun,2021,ICCV 2021,main,Poster,,,0,8.959,0.000,,,https://openaccess.thecvf.com/content/ICCV2021/papers/Xiong_Self-Supervised_Representation_Learning_From_Flow_Equivariance_ICCV_2021_paper.pdf,offline_iccv,,"Self-supervised representation learning is able to learn semantically meaningful features; however, much of its recent success relies on multiple crops of an image with very few objects. Instead of learning view-invariant representation from simple images, humans learn representations in a complex w"
197,znNmsN_O7Sh,Object Scene Representation Transformer,Mehdi S. M. Sajjadi; Daniel Duckworth; Aravindh Mahendran; Sjoerd van Steenkiste; Filip Pavetic,2022,NIPS 2022,main,Accept,,novel view synthesis;scene decomposition;transformer;slot attention;unsupervised decomposition;representation learning;neural rendering;scene representations,0,8.956,0.000,,https://nips.cc/virtual/2022/poster/55325,https://openreview.net/pdf?id=znNmsN_O7Sh,offline_nips,"We propose Object Scene Representation Transformer (OSRT), a highly efficient 3D-centric model in which individual object representations naturally emerge through novel view synthesis.",A compositional understanding of the world in terms of objects and their geometry in 3D space is considered a cornerstone of human cognition. Facilitating the learning of such a representation in neural networks holds promise for substantially improving labeled data efficiency. As a key step in this
198,rCX9l4OTCT,Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving,Xiang Li; Pengfei Li; Yupeng Zheng; Wei Sun; Yan Wang,2025,ICLR 2025,main,Poster,"applications to computer vision, audio, language, and other modalities",Autonomous Driving; Occupancy; World Model,0,8.953,0.000,,https://iclr.cc/virtual/2025/poster/28213,https://openreview.net/pdf?id=rCX9l4OTCT,offline_iclr,,"Understanding world dynamics is crucial for planning in autonomous driving. Recent methods attempt to achieve this by learning a 3D occupancy world model that forecasts future surrounding scenes based on current observation. However, 3D occupancy labels are still required to produce promising result"
199,DorAT49sxj,WALL-E: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents,Siyu Zhou; Tianyi Zhou; Yijun Yang; Guodong Long; Deheng Ye,2025,NIPS 2025,main,Poster,deep_learning,world model;embodied agent;large language model;neurosymbolic learning,0,8.951,0.000,,https://openreview.net/forum?id=DorAT49sxj,,offline_nips,,"Can we build accurate world models out of large language models (LLMs)? How can world models benefit LLM agents? The gap between the prior knowledge of LLMs and the specified environment's dynamics usually bottlenecks LLMs' performance as world models. To bridge the gap, we propose a training-free """
200,aVyJwS1fqQ,Mani-WM: An Interactive World Model for Real-Robot Manipulation,Fangqi Zhu; Hongtao Wu; Song Guo; Yuxiao Liu; Chilam Cheang,2025,ICLR 2025,main,Withdraw,"applications to robotics, autonomy, planning",World Model;Video Generation;Robot Manipulation,0,8.947,0.000,,https://openreview.net/forum?id=aVyJwS1fqQ,,offline_iclr,,"Scalable robot learning in the real world is limited by the cost and safety issues of real robots. In addition, rolling out robot trajectories in the real world can be time-consuming and labor-intensive. In this paper, we propose to learn an interactive world model for robot manipulation as an alter"
201,,Learning To Drive From a World on Rails,Dian Chen; Vladlen Koltun; Philipp Krähenbühl,2021,ICCV 2021,main,Poster,,,0,8.943,0.000,,,https://openaccess.thecvf.com/content/ICCV2021/papers/Chen_Learning_To_Drive_From_a_World_on_Rails_ICCV_2021_paper.pdf,offline_iccv,,"We learn an interactive vision-based driving policy from pre-recorded driving logs via a model-based approach. A forward model of the world supervises a driving policy that predicts the outcome of any potential driving trajectory. To support learning from pre-recorded logs, we assume that the world "
202,ph1V6n7BSv,EDELINE: Enhancing Memory in Diffusion-based World Models via Linear-Time Sequence Modeling,Jia-Hua Lee; Bor-Jiun Lin; Wei-Fang Sun; Chun-Yi Lee,2025,NIPS 2025,main,Spotlight,reinforcement_learning,Model-based Reinforcement Learning;Atari 100k;Doom;Crafter;MAMBA;Diffusion;World Model,0,8.941,0.000,,https://openreview.net/forum?id=ph1V6n7BSv,,offline_nips,,"World models represent a promising approach for training reinforcement learning agents with significantly improved sample efficiency. While most world model methods primarily rely on sequences of discrete latent variables to model environment dynamics, this compression often neglects critical visual"
203,BkgRe1SFDS,Learning World Graph Decompositions To Accelerate Reinforcement Learning,Wenling Shang; Alex Trott; Stephan Zheng; Caiming Xiong; Richard Socher,2020,ICLR 2020,main,Reject,,environment decomposition;subgoal discovery;generative modeling;reinforcement learning;unsupervised learning,0,8.940,0.000,,https://openreview.net/forum?id=BkgRe1SFDS,,offline_iclr,We learn a task-agnostic world graph abstraction of the environment and show how using it for structured exploration can significantly accelerate downstream task-specific RL.,"Efficiently learning to solve tasks in complex environments is a key challenge for reinforcement learning (RL) agents.  We propose to decompose a complex environment using a task-agnostic world graphs, an abstraction that accelerates learning by enabling agents to focus exploration on a subspace of "
204,HHwGfLOKxq,Scaling Laws for Pre-training Agents and World Models,Tim Pearce; Tabish Rashid; David Bignell; Raluca Georgescu; Sam Devlin,2025,ICML 2025,main,Poster,deep_learning->generative_models_and_autoencoders,world modeling;imitation learning;scaling laws,0,8.940,0.000,,https://icml.cc/virtual/2025/poster/45787,https://openreview.net/pdf?id=HHwGfLOKxq,offline_icml,,"The performance of embodied agents has been shown to improve by increasing model parameters, dataset size, and compute. This has been demonstrated in domains from robotics to video games, when generative learning objectives on offline datasets (pre-training) are used to model an agent's behavior (im"
205,9636830,OPEn: An Open-ended Physics Environment for Learning Without a Task,Chuang Gan; Abhishek Bhandwaldar; Antonio Torralba; Joshua B. Tenenbaum; Phillip Isola,2021,IROS 2021,main,Poster,,,0,8.938,0.000,,https://ieeexplore.ieee.org/document/9636830/,,offline_iros,,"Humans have mental models that allow them to plan, experiment, and reason in the physical world. How should an intelligent agent go about learning such models? In this paper, we will study if models of the world learned in an open-ended physics environment, without any specific tasks, can be reused "
206,30I17s8gjs,Policy-Driven World Model Adaptation for Robust Offline Model-based Reinforcement Learning,,2026,ICLR 2026,main,Active,reinforcement learning,Offline Reinforcement Learning;Model-based Reinforcement Learning;Robustness;Stackelberg games,0,8.937,0.000,,https://openreview.net/forum?id=30I17s8gjs,,offline_iclr,,"Offline reinforcement learning (RL) offers a powerful paradigm for data-driven control. Compared to model-free approaches, offline model-based RL (MBRL) explicitly learns a world model from a static dataset and uses it as a surrogate simulator, improving data efficiency and enabling potential genera"
207,,Open World Compositional Zero-Shot Learning,Massimiliano Mancini; Muhammad Ferjad Naeem; Yongqin Xian; Zeynep Akata,2021,CVPR 2021,main,Poster,,,0,8.936,0.000,,,https://openaccess.thecvf.com/content/CVPR2021/papers/Mancini_Open_World_Compositional_Zero-Shot_Learning_CVPR_2021_paper.pdf,offline_cvpr,,"Compositional Zero-Shot learning (CZSL) requires to recognize state-object compositions unseen during training. In this work, instead of assuming prior knowledge about the unseen compositions, we operate in the open world setting, where the search space includes a large number of unseen compositions"
208,9196582,Adversarial Skill Networks: Unsupervised Robot Skill Learning from Video,Oier Mees; Markus Merklinger; Gabriel Kalweit; Wolfram Burgard; Oier Mees,2020,ICRA 2020,main,Poster,,,0,8.931,0.000,,https://ieeexplore.ieee.org/document/9196582/,,offline_icra,,"Key challenges for the deployment of reinforcement learning (RL) agents in the real world are the discovery, representation and reuse of skills in the absence of a reward function. To this end, we propose a novel approach to learn a task-agnostic skill embedding space from unlabeled multi-view video"
209,Tw9nfNyOMy,Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability,Shenyuan Gao; Jiazhi Yang; Li Chen; Kashyap Chitta; Yihang Qiu,2024,NIPS 2024,main,Poster,machine_vision,World Model;Autonomous Driving;Video Prediction,0,8.928,0.000,,https://neurips.cc/virtual/2024/poster/95003,https://openreview.net/pdf?id=Tw9nfNyOMy,offline_nips,,"World models can foresee the outcomes of different actions, which is of paramount importance for autonomous driving. Nevertheless, existing driving world models still have limitations in generalization to unseen environments, prediction fidelity of critical details, and action controllability for fl"
210,COYDmKkQH4,AutoCast++: Enhancing World Event Prediction with Zero-shot Ranking-based Context Retrieval,Qi Yan; Raihan Seraj; Jiawei He; Lili Meng; Tristan Sylvain,2024,ICLR 2024,main,Poster,"representation learning for computer vision, audio, language, and other modalities",world event prediction;large language models,0,8.926,0.000,,https://iclr.cc/virtual/2024/poster/19174,https://openreview.net/pdf?id=COYDmKkQH4,offline_iclr,,"Machine-based prediction of real-world events is garnering attention due to its potential for informed decision-making. Whereas traditional forecasting predominantly hinges on structured data like time-series, recent breakthroughs in language models enable predictions using unstructured text. In par"
211,tsE5HLYtYg,SafeDreamer: Safe Reinforcement Learning with World Models,Weidong Huang; Jiaming Ji; Chunhe Xia; Borong Zhang; Yaodong Yang,2024,ICLR 2024,main,Poster,reinforcement learning,Safe Reinforcement Learning;SafeRL;World Model,0,8.922,0.000,,https://iclr.cc/virtual/2024/poster/17597,https://openreview.net/pdf?id=tsE5HLYtYg,offline_iclr,,"The deployment of Reinforcement Learning (RL) in real-world applications is constrained by its failure to satisfy safety criteria.
Existing Safe Reinforcement Learning (SafeRL) methods, which rely on cost functions to enforce safety, often fail to achieve zero-cost performance in complex scenarios, "
212,29523,Learning Triangular Distribution in Visual World,Ping Chen; Xingpeng Zhang; Chengtao Zhou; dichao Fan; Peng Tu,2024,CVPR 2024,main,Poster,,,0,8.920,0.000,,https://cvpr.thecvf.com/virtual/2024/poster/29523,https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_Learning_Triangular_Distribution_in_Visual_World_CVPR_2024_paper.pdf,offline_cvpr,,
213,b4Tr8NWTDt,Co-Learning Empirical Games and World Models,Max Olan Smith; Michael P. Wellman,2023,NIPS 2023,main,Reject,,Multiagent learning;Empirical Game Theory;Model-Based Reinforcement Learning;Transfer Learning,0,8.919,0.000,,https://openreview.net/forum?id=b4Tr8NWTDt,,offline_nips,,"Game-based decision-making involves reasoning over both world dynamics and strategic interactions among the agents. Typically, empirical models capturing these respective aspects are learned and used separately. We investigate the potential gain from co-learning these elements: a world model for dyn"
214,TyZhiK6fDf,Co-Learning Empirical Games & World Models,Max Olan Smith; Michael P. Wellman,2024,ICLR 2024,main,Reject,reinforcement learning,Multiagent learning;Empirical Game Theory;Model-Based Reinforcement Learning;Transfer Learning,0,8.919,0.000,,https://openreview.net/forum?id=TyZhiK6fDf,,offline_iclr,,"Game-based decision-making involves reasoning over both world dynamics and strategic interactions among the agents.
Typically, empirical models capturing these respective aspects are learned and used separately.
We investigate the potential gain from co-learning these elements: a world model for dyn"
215,D5RNACOZEI,DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning,Gaoyue Zhou; Hengkai Pan; Yann LeCun; Lerrel Pinto,2025,ICML 2025,main,Poster,applications->robotics,World Models;Planning;Representation Learning,0,8.906,0.000,,https://icml.cc/virtual/2025/poster/46026,https://openreview.net/pdf?id=D5RNACOZEI,offline_icml,,"The ability to predict future outcomes given control actions is fundamental for physical reasoning. However, such predictive models, often called world models, remain challenging to learn and are typically developed for task-specific solutions with online policy learning. To unlock world models' tru"
216,paper263,Using Platform Models for a Guided Explanatory Diagnosis Generation for Mobile Robots,Daniel Habering; Till Hofmann; Gerhard Lakemeyer,2021,IJCAI 2021,main,Poster,Knowledge Representation and Reasoning,Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning; Robotics: Cognitive Robotics; Robotics: Dependable Robots,0,8.905,0.000,,https://www.ijcai.org/proceedings/2021/263,https://www.ijcai.org/proceedings/2021/0263.pdf,offline_ijcai,,"Plan execution on a mobile robot is inherently error-prone, as the robot
needs to act in a physical world which can never be completely
controlled by the robot. If an error occurs during execution, the true
world state is unknown, as a failure may have unobservable consequences.
One approach to "
217,article-25416,Self-Supervised Video Representation Learning via Latent Time Navigation,Di Yang; Yaohui Wang; Quan Kong; Antitza Dantcheva; Lorenzo Garattoni,2023,AAAI 2023,main,Technical,computer vision iii,,0,8.894,0.000,,https://ojs.aaai.org/index.php/AAAI/article/view/25416,https://ojs.aaai.org/index.php/AAAI/article/view/25416/25188,offline_aaai,,"Self-supervised video representation learning aimed at maximizing similarity between different temporal segments of one video, in order to enforce feature persistence over time. This leads to loss of pertinent information related to temporal relationships, rendering actions such as `enter' and `leav"
218,sEKJYLnTpA,HELIOS: Hierarchical Exploration for Language-grounded Interaction in Open Scenes,,2026,ICLR 2026,main,Active,"applications to robotics, autonomy, planning",active perception;mobile manipulation;3D Gaussian splatting,0,8.893,0.000,,https://openreview.net/forum?id=sEKJYLnTpA,,offline_iclr,,"Language-specified mobile manipulation tasks in novel environments simultaneously face challenges interacting with a scene which is only partially observed, grounding semantic information from language instructions to the partially observed scene, and actively updating knowledge of the scene with ne"
219,W5e6Kkr2WN,Adapting World Models with Latent-State Dynamics Residuals,JB Lanier; Kyungmin Kim; Armin Karamzade; Yifei Liu; Ankita sinha,2026,ICLR 2026,main,Withdraw,reinforcement learning,Reinforcement Learning;World Models;Latent-State World Models;Sim-to-Real;Simulation to Real Transfer;Sim2Real;Transfer Learning;Adaptation;Model-Based Reinforcement Learning,0,8.882,0.000,,https://openreview.net/forum?id=W5e6Kkr2WN,,offline_iclr,,"Simulation-to-reality reinforcement learning (RL) faces the challenge of reconciling discrepancies between simulated and real-world dynamics, which can degrade agent performance. When real data is scarce, a promising approach involves learning corrections to simulator forward dynamics represented as"
220,vHaShO76T8,Zero-shot World Models via Search in Memory,Federico Malato; Ville Hautamaki,2025,NIPS 2025,main,Poster,reinforcement_learning,world models;reinforcement learning;imitation learning;similarity search,0,8.877,0.000,,https://openreview.net/forum?id=vHaShO76T8,,offline_nips,,"World Models have vastly permeated the field of Reinforcement Learning. Their ability to model the transition dynamics of an environment have led to tremendous improvements in sample efficiency for online RL. Among them, the most notorious example is Dreamer, a model that learns to act in a diverse "
221,0za6569Jqd,Representation Finetuning for Continual Learning,,2026,ICLR 2026,main,Active,applications to neuroscience & cognitive science,continual learning;reft;finetuning,0,8.875,0.000,,https://openreview.net/forum?id=0za6569Jqd,,offline_iclr,,"The world is inherently dynamic, and continual learning aims to enable models to adapt to ever-evolving data streams. Pre-trained models has shown powerful performance in continual learning. However, since pre-trained models acquire knowledge from static datasets, they still require finetuning to ad"
222,vhFu1Acb0xb,Transformers are Sample-Efficient World Models,Vincent Micheli; Eloi Alonso; François Fleuret,2023,ICLR 2023,main,Top-5%,,deep learning;reinforcement learning;model-based reinforcement learning;world models;learning in imagination;transformers;discrete autoencoders;generative modeling;sequence modeling,0,8.873,0.000,,https://iclr.cc/virtual/2023/poster/11839,https://openreview.net/pdf?id=vhFu1Acb0xb,offline_iclr,We introduce a data-efficient agent that learns in a world model composed of a discrete autoencoder and an autoregressive Transformer.,"Deep reinforcement learning agents are notoriously sample inefficient, which considerably limits their application to real-world problems. Recently, many model-based methods have been designed to address this issue, with learning in the imagination of a world model being one of the most prominent ap"
223,fY7dShbtmo,Multi Time Scale World Models,Vaisakh Shaj; Saleh GHOLAM ZADEH; Ozan Demir; Luiz Ricardo Douat; Gerhard Neumann,2023,NIPS 2023,main,Spotlight,,Hierarchical Models; Multi Time Scale Learning; World Models,0,8.870,0.000,,https://nips.cc/virtual/2023/poster/70901,https://openreview.net/pdf?id=fY7dShbtmo,offline_nips,,Intelligent agents use internal world models to reason and make predictions about different courses of their actions at many scales. Devising learning paradigms and architectures that allow machines to learn world models that operate at multiple levels of temporal abstractions while dealing with com
224,,Learning Continuous Image Representation With Local Implicit Image Function,Yinbo Chen; Sifei Liu; Xiaolong Wang,2021,CVPR 2021,main,Poster,,,0,8.870,0.000,,,https://openaccess.thecvf.com/content/CVPR2021/papers/Chen_Learning_Continuous_Image_Representation_With_Local_Implicit_Image_Function_CVPR_2021_paper.pdf,offline_cvpr,,"How to represent an image? While the visual world is presented in a continuous manner, machines store and see the images in a discrete way with 2D arrays of pixels. In this paper, we seek to learn a continuous representation for images. Inspired by the recent progress in 3D reconstruction with impli"
225,G1tsqarGAw,WorldGPT: Empowering LLM as Multimodal World Model,Zhiqi Ge; Hongzhe Huang; Mingze Zhou; Juncheng Li; Guoming Wang,2024,ACMMM 2024,main,Oral,[Content] Vision and Language,,0,8.863,0.000,,,,offline_acmmm,,
226,XZ0pRezf4O,PointWorld: Scaling 3D World Models for In-The-Wild Robotic Manipulation,Wenlong Huang; Yu-Wei Chao; Arsalan Mousavian; Ming-Yu Liu; Dieter Fox,2026,ICLR 2026,main,Withdraw,"applications to robotics, autonomy, planning",World Modeling;Dynamics Modeling;Robotic Manipulation,0,8.862,0.000,,https://openreview.net/forum?id=XZ0pRezf4O,,offline_iclr,,"Humans anticipate, from a glance and a contemplated action of their bodies, how the
3D world will respond. This predictive ability is equally vital for enabling robots
to manipulate and interact with the physical world. We introduce PointWorld,
a foundation 3D world model that unifies state and acti"
227,guUrm5IRQS,4DNeX: Feed-Forward 4D Generative Modeling Made Easy,Zhaoxi Chen; Tianqi Liu; Long Zhuo; Jiawei Ren; Zeng Tao,2026,ICLR 2026,main,Withdraw,generative models,Image-to-4D Modeling;Generative 4D World Models;4D Dataset,0,8.859,0.000,,https://openreview.net/forum?id=guUrm5IRQS,,offline_iclr,,"We present 4DNeX, the first feed-forward framework for generating 4D (i.e., dynamic 3D) scene representations from a single image. In contrast to existing methods that rely on computationally intensive optimization or require multi-frame video inputs, 4DNeX enables efficient, end-to-end image-to-4D "
228,kbBjVMcJ7G,Operator World Models for Reinforcement Learning,Pietro Novelli; Marco Pratticò; Massimiliano Pontil; Carlo Ciliberto,2024,NIPS 2024,main,Poster,reinforcement_learning,Reinforcement Learning;Transfer Operators;World Models;Policy Gradient;Conditional Mean Embeddings;Mirror Descent,0,8.859,0.000,,https://neurips.cc/virtual/2024/poster/93875,https://openreview.net/pdf?id=kbBjVMcJ7G,offline_nips,,"Policy Mirror Descent (PMD) is a powerful and theoretically sound methodology for sequential decision-making. However, it is not directly applicable to Reinforcement Learning (RL) due to the inaccessibility of explicit action-value functions. We address this challenge by introducing a novel approach"
229,NEu8wgPctU,AdaWM: Adaptive World Model based Planning for Autonomous Driving,Hang Wang; Xin Ye; Feng Tao; Chenbin Pan; Abhirup Mallik,2025,ICLR 2025,main,Poster,"applications to robotics, autonomy, planning",World Model;Autonomous Driving;Reinforcement Learning,0,8.853,0.000,,https://iclr.cc/virtual/2025/poster/29891,https://openreview.net/pdf?id=NEu8wgPctU,offline_iclr,,"World model based reinforcement learning (RL) has emerged as a promising approach for autonomous driving, which learns a latent dynamics model and uses it to train a   planning policy. To speed up the learning process, the pretrain-finetune paradigm is often used, where online RL is initialized by a"
230,aVK4JFpegy,Evaluating the World Model Implicit in a Generative Model,Keyon Vafa; Justin Y. Chen; Ashesh Rambachan; Jon Kleinberg; Sendhil Mullainathan,2024,NIPS 2024,main,Spotlight,evaluation,world models;large language models;evaluation,0,8.853,0.000,,https://neurips.cc/virtual/2024/poster/94550,https://openreview.net/pdf?id=aVK4JFpegy,offline_nips,,Recent work suggests that large language models may implicitly learn world models. How should we assess this possibility? We formalize this question for the case where the underlying reality is governed by a deterministic finite automaton. This includes problems as diverse as simple logical reasonin
231,faxcxKINBC,Sparse Imagination for Efficient Visual World Model Planning,,2026,ICLR 2026,main,Active,"applications to robotics, autonomy, planning",World Model;Planning;Computational Efficiency;Model Predictive Control;Vision Transformer,0,8.853,0.000,,https://openreview.net/forum?id=faxcxKINBC,,offline_iclr,,"World model based planning has significantly improved decision-making in complex environments by enabling agents to simulate future states and make informed choices.
This computational burden is particularly restrictive in robotics, where resources are severely constrained.
To address this limitatio"
232,FLa1RPjpm2L,ED2: An Environment Dynamics Decomposition Framework for World Model Construction,Cong Wang; Tianpei Yang; Jianye HAO; YAN ZHENG; Hongyao Tang,2022,ICLR 2022,main,Reject,,model based reinforcement learning,0,8.844,0.000,,https://openreview.net/forum?id=FLa1RPjpm2L,,offline_iclr,,"Model-based reinforcement learning methods achieve significant sample efficiency in many tasks, but their performance is often limited by the existence of the model error. To reduce the model error, previous works use a single well-designed network to fit the entire environment dynamics, which treat"
233,2025.coling-main.503,Making Large Language Models into World Models with Precondition and Effect Knowledge,Kaige Xie; Ian Yang; John Gunerli; Mark Riedl,2025,COLING 2025,main,Main,,,0,8.843,0.000,,https://aclanthology.org/2025.coling-main.503/,https://aclanthology.org/2025.coling-main.503.pdf,offline_coling,,"World models, which encapsulate the dynamics of how actions affect environments, are foundational to the functioning of intelligent agents. In this work, we explore the potential of Large Language Models (LLMs) to operate as world models. Although LLMs are not inherently designed to model real-world"
234,1cA6OYsfoJ,From Real World to Logic and Back: Learning Generalizable Relational Concepts For Long Horizon Robot Planning,Naman Shah; Jayesh Nagpal; Siddharth Srivastava,2025,CORL 2025,main,Poster,,Learnng symbolic abstractions;Symbolic world model learning;Learning for task and motion planning;learning for planning,0,8.841,0.000,,https://openreview.net/forum?id=1cA6OYsfoJ,,offline_corl,,"Humans efficiently generalize from limited demonstrations, but robots still struggle to transfer learned knowledge to complex, unseen tasks with longer horizons and increased complexity. 
We propose the first known method enabling robots to autonomously invent relational concepts directly from small"
235,mQeZEsdODh,Continual Reinforcement Learning by Planning with Online World Models,Zichen Liu; Guoji Fu; Chao Du; Wee Sun Lee; Min Lin,2025,ICML 2025,main,Spotlight,reinforcement_learning,model-based rl;continual rl;online learning;incremental learning;catastrophic forgetting,0,8.839,0.000,,https://icml.cc/virtual/2025/poster/44151,https://openreview.net/pdf?id=mQeZEsdODh,offline_icml,,"Continual reinforcement learning (CRL) refers to a naturalistic setting where an agent needs to endlessly evolve, by trial and error, to solve multiple tasks that are presented sequentially. One of the largest obstacles to CRL is that the agent may forget how to solve previous tasks when learning a "
236,owEhpoKBKC,Reward-free World Models for Online Imitation Learning,Shangzhe Li; Zhiao Huang; Hao Su,2025,ICML 2025,main,Poster,reinforcement_learning,world models;imitation learning,0,8.834,0.000,,https://icml.cc/virtual/2025/poster/44035,https://openreview.net/pdf?id=owEhpoKBKC,offline_icml,,"Imitation learning (IL) enables agents to acquire skills directly from expert demonstrations, providing a compelling alternative to reinforcement learning. However, prior online IL approaches struggle with complex tasks characterized by high-dimensional inputs and complex dynamics. In this work, we "
237,xjTrTlBbrc,Graph World Model,Tao Feng; Yexin Wu; Guanyu Lin; Jiaxuan You,2025,ICML 2025,main,Poster,deep_learning->graph_neural_networks,Graph world model;Unstructured data;Multi-modal information;Zero-shot/few-shot capabilities,0,8.833,0.000,,https://icml.cc/virtual/2025/poster/43569,https://openreview.net/pdf?id=xjTrTlBbrc,offline_icml,,"World models (WMs) demonstrate strong capabilities in prediction, generation, and planning tasks.
Existing WMs primarily focus on unstructured data while cannot leverage the ubiquitous structured data, often represented as graphs, in the digital world. While multiple graph foundation models have bee"
238,35248,Seeing A 3D World in A Grain of Sand,Yufan Zhang; Yu Ji; Yu Guo; Jinwei Ye,2025,CVPR 2025,main,Poster,,,0,8.831,0.000,,https://cvpr.thecvf.com/virtual/2025/poster/35248,https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Seeing_A_3D_World_in_A_Grain_of_Sand_CVPR_2025_paper.pdf,offline_cvpr,,"We present a snapshot imaging technique for recovering 3D surrounding views of miniature scenes. Due to their intricacy, miniature scenes with objects sized in millimeters are difficult to reconstruct, yet miniatures are common in life and their 3D digitalization is desirable. We design a catadioptr"
239,9561877,Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes,Martin Sundermeyer; Arsalan Mousavian; Rudolph Triebel; Dieter Fox; Martin Sundermeyer,2021,ICRA 2021,main,Poster,,,0,8.831,0.000,,https://ieeexplore.ieee.org/document/9561877/,,offline_icra,,"Grasping unseen objects in unconstrained, cluttered environments is an essential skill for autonomous robotic manipulation. Despite recent progress in full 6-DoF grasp learning, existing approaches often consist of complex sequential pipelines that possess several potential failure points and run-ti"
240,WSkU78RTGC,EvoAgent: Self-evolving Agent with Continual World Model for Long-Horizon Tasks,,2026,ICLR 2026,main,Active,"applications to robotics, autonomy, planning",World Model;Long-Horizon Tasks;Self-evolving,0,8.830,0.000,,https://openreview.net/forum?id=WSkU78RTGC,,offline_iclr,,"Completing Long-Horizon (LH) tasks in open-ended worlds is an important yet difficult problem for embodied agents. Existing approaches suffer from two key challenges: (1) they heavily rely on experiences obtained from human-created data or curricula, failing to autonomously update and select multimo"
241,e5mTvjXG9u,Dreamweaver: Learning Compositional World Models from Pixels,Junyeob Baek; Yi-Fu Wu; Gautam Singh; Sungjin Ahn,2025,ICLR 2025,main,Poster,"unsupervised, self-supervised, semi-supervised, and supervised representation learning",compositional world models;unsupervised object-centric learning,0,8.828,0.000,,https://iclr.cc/virtual/2025/poster/28949,https://openreview.net/pdf?id=e5mTvjXG9u,offline_iclr,,"Humans have an innate ability to decompose their perceptions of the world into objects and their attributes, such as colors, shapes, and movement patterns. This cognitive process enables us to imagine novel futures by recombining familiar concepts. However, replicating this ability in artificial int"
242,hidBHy1CAw,WorldGym: World Model as An Environment for Policy Evaluation,,2026,ICLR 2026,main,Active,reinforcement learning,World model;video generation;policy evaluation;generative simulators,0,8.826,0.000,,https://openreview.net/forum?id=hidBHy1CAw,,offline_iclr,,"Evaluating robot control policies is difficult: real-world testing is costly, and handcrafted simulators require manual effort to improve in realism and generality. We propose a world-model-based policy evaluation environment (WorldGym), an autoregressive, action-conditioned video generation model w"
243,8GuEVzAUQS,Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning,Jialong Wu; Haoyu Ma; Chaoyi Deng; Mingsheng Long,2023,NIPS 2023,main,Poster,,Model-based reinforcement learning;world model;pre-training,0,8.823,0.000,,https://nips.cc/virtual/2023/poster/72660,https://openreview.net/pdf?id=8GuEVzAUQS,offline_nips,,Unsupervised pre-training methods utilizing large and diverse datasets have achieved tremendous success across a range of domains. Recent work has investigated such unsupervised pre-training methods for model-based reinforcement learning (MBRL) but is limited to domain-specific or simulated data. In
244,UQ36IrVCw2,One Life to Learn: Inferring Symbolic World Models for Stochastic Environments from Unguided Exploration,,2026,ICLR 2026,main,Active,"foundation or frontier models, including LLMs",world modeling;programmatic RL;probabilistic program;symbolic rule learning;intrinsically motivated and open-ended learning,0,8.821,0.000,,https://openreview.net/forum?id=UQ36IrVCw2,,offline_iclr,,"Symbolic world modeling is the task of inferring and representing the transitional dynamics of an environment as an executable program. Previous research
on symbolic world modeling has focused on simple, deterministic environments
with abundant data and human-provided guidance. We address the more r"
245,2023.acl-short.57,Learning Neuro-Symbolic World Models with Conversational Proprioception,Don Joven Agravante; Daiki Kimura; Michiaki Tatsubori; Asim Munawar; Alexander Gray,2023,ACL 2023,main,Short,,,0,8.812,0.000,,https://aclanthology.org/2023.acl-short.57/,https://aclanthology.org/2023.acl-short.57.pdf,offline_acl,,"The recent emergence of Neuro-Symbolic Agent (NeSA) approaches to natural language-based interactions calls for the investigation of model-based approaches. In contrast to model-free approaches, which existing NeSAs take, learning an explicit world model has an interesting potential especially in th"
246,nlpCeFsSYJ,DOME: Taming Diffusion Model into High-Fidelity Controllable Occupancy World Model,songen gu; Wei Yin; Bu Jin; Xiaoyang Guo; Junming Wang,2025,ICLR 2025,main,Reject,"applications to robotics, autonomy, planning",World Model;Diffusion Model;Occupancy;Generative Model,0,8.812,0.000,,https://openreview.net/forum?id=nlpCeFsSYJ,,offline_iclr,,"We propose DOME, a diffusion-based world model that predicts future occupancy frames based on past occupancy observations. The ability of this world model to capture the evolution of the environment is crucial for planning in autonomous driving. Compared to 2D video-based world models, the occupancy"
247,Psl75UCoZM,Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion,Lunjun Zhang; Yuwen Xiong; Ze Yang; Sergio Casas; Rui Hu,2024,ICLR 2024,main,Poster,"applications to robotics, autonomy, planning",discrete diffusion; world model; autonomous driving,0,8.811,0.000,,https://iclr.cc/virtual/2024/poster/18691,https://openreview.net/pdf?id=Psl75UCoZM,offline_iclr,,"Learning world models can teach an agent how the world works in an unsupervised manner. Even though it can be viewed as a special case of sequence modeling, progress for scaling world models on robotic applications such as autonomous driving has been somewhat less rapid than scaling language models "
248,99f6c7b8cc,Directed Hypergraph Representation Learning for Link Prediction,Zitong Ma; Wenbo Zhao; Zhe Yang,2024,AISTATS 2024,main,Poster,,,0,8.806,0.000,,https://proceedings.mlr.press/v238/ma24b.html,https://proceedings.mlr.press/v238/ma24b/ma24b.pdf,offline_aistats,,"Link prediction is a critical problem in network structure processing. With the prevalence of deep learning, graph-based learning pattern in link prediction has been well-proven to successfully apply. However, existing representation-based computing paradigms retain some lack in processing complex n"
249,sLzD2rw9Ce,DrivingDojo Dataset: Advancing Interactive and Knowledge-Enriched Driving World Model,Yuqi Wang; Ke Cheng; Jiawei He; Qitai Wang; Hengchen Dai,2024,NIPS 2024,Datasets & Benchmarks,Poster,,Dataset;World model;Autonomous Driving;Interactive Behavior,0,8.805,0.000,,https://neurips.cc/virtual/2024/poster/97477,https://openreview.net/pdf?id=sLzD2rw9Ce,offline_nips,,"Driving world models have gained increasing attention due to their ability to model complex physical dynamics. However, their superb modeling capability is yet to be fully unleashed due to the limited video diversity in current driving datasets. We introduce DrivingDojo, the first dataset tailor-mad"
250,ASPC2Ut0CB,Spatiotemporal Forecasting as Planning: A Model-Based Reinforcement Learning Approach with Generative World Models,,2026,ICLR 2026,main,Active,"applications to physical sciences (physics, chemistry, biology, etc.)",World Model;Spatio-temporal data mining,0,8.802,0.000,,https://openreview.net/forum?id=ASPC2Ut0CB,,offline_iclr,,"Physical spatiotemporal forecasting poses a dual challenge: The inherent stochasticity of physical systems makes it difficult to capture extreme or rare events, especially under \textit{data scarcity}. Moreover, many critical domain-specific metrics are \textit{non-differentiable}, precluding their "
251,tmIRNo66Rg,TriVLA: A Triple-System-Based Unified Vision-Language-Action Model with Episodic World Modeling for General Robot Control,,2026,ICLR 2026,main,Active,"applications to robotics, autonomy, planning",Vision-Language-Action Model (VLA);Episodic World Model;General Robot Control;Imitation Learning,0,8.801,0.000,,https://openreview.net/forum?id=tmIRNo66Rg,,offline_iclr,,"Recent advances in vision–language models (VLMs) have enabled robots to follow open-ended instructions and demonstrate impressive commonsense reasoning. However, current vision–language–action (VLA) frameworks primarily rely on static representations and limited temporal context, restricting agents "
252,DR9e3M9Y3y,Coupled Distributional Random Expert Distillation for World Model Online Imitation Learning,Shangzhe Li; Zhiao Huang; Hao Su,2026,ICLR 2026,main,Withdraw,reinforcement learning,imitation learning;world models,0,8.798,0.000,,https://openreview.net/forum?id=DR9e3M9Y3y,,offline_iclr,,"Imitation Learning (IL) has achieved remarkable success across various domains, including robotics, autonomous driving, and healthcare, by enabling agents to learn complex behaviors from expert demonstrations. However, existing IL methods often face instability challenges, particularly when relying "
253,eWLOoaShEH,Learning to Model the World with Language,Jessy Lin; Yuqing Du; Olivia Watkins; Danijar Hafner; Pieter Abbeel,2024,ICLR 2024,main,Reject,reinforcement learning,reinforcement learning;language and rl;language grounding;world models;multi-modal world models,0,8.784,0.000,,https://openreview.net/forum?id=eWLOoaShEH,,offline_iclr,,"To interact with humans and act in the world, agents need to understand the range of language that people use and relate it to the visual world. While current agents learn to execute simple language instructions, we aim to build agents that leverage diverse language—language like “this button turns "
254,uS5ch7GjZ4,SPARTAN: A Sparse Transformer World Model Attending to What Matters,Anson Lei; Bernhard Schölkopf; Ingmar Posner,2025,NIPS 2025,main,Poster,deep_learning,World Models;Attention;Local Causal Model,0,8.775,0.000,,https://openreview.net/forum?id=uS5ch7GjZ4,,offline_nips,,Capturing the interactions between entities in a structured way plays a central role in world models that flexibly adapt to changes in the environment. Recent works motivate the benefits of models that explicitly represent the structure of interactions and formulate the problem as discovering local 
255,PK07eretkF,DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge,Wenyao Zhang; Hongsi Liu; Zekun Qi; Yunnan Wang; XinQiang Yu,2025,NIPS 2025,main,Poster,other,Vision language action models;comprehensive knowledge forecasting;robot learning,0,8.770,0.000,,https://openreview.net/forum?id=PK07eretkF,,offline_nips,,"Recent advances in vision-language-action (VLA) models have shown promise in integrating image generation with action prediction to improve generalization and reasoning in robot manipulation. However, existing methods are limited to challenging image-based forecasting, which suffers from redundant i"
256,xFmxnyNYZJ,Learning Task-Sufficient World Models via Intervention-Curriculum Co-Design,,2026,ICLR 2026,main,Active,reinforcement learning,World Model; Latent Variable Models,0,8.770,0.000,,https://openreview.net/forum?id=xFmxnyNYZJ,,offline_iclr,,"We study how agents learn world models with latent representations that are task-specific, minimal, and sufficient for sequential decision making. Rather than predicting pixels or relying on generic embeddings, we aim to learn representations that retain exactly the information needed for control ac"
257,2053,Learning 4D Embodied World Models,Haoyu Zhen; Qiao Sun; Hongxin Zhang; Junyan Li; Siyuan Zhou,2025,ICCV 2025,main,Poster,,,0,8.762,0.000,,https://iccv.thecvf.com/virtual/2025/poster/2053,https://openaccess.thecvf.com/content/ICCV2025/papers/Zhen_Learning_4D_Embodied_World_Models_ICCV_2025_paper.pdf,offline_iccv,,"This paper presents an effective approach for learning novel 4D embodied world models, which predict the dynamic evolution of 3D scenes over time in response to an embodied agent's actions, providing both spatial and temporal consistency. We propose to learn a 4D world model by training on RGB-DN (R"
258,eEdukIJ7lQ,Remote Sensing-Oriented World Model,,2026,ICLR 2026,main,Active,datasets and benchmarks,World Model;Remote Sensing,0,8.759,0.000,,https://openreview.net/forum?id=eEdukIJ7lQ,,offline_iclr,,"World models have shown potential in artificial intelligence by predicting and reasoning about world states beyond direct observations. However, existing approaches are predominantly evaluated in synthetic environments or constrained scene settings, limiting their validation in real-world contexts w"
259,10063,World Model as a Graph: Learning Latent Landmarks for Planning,Lunjun Zhang; Ge Yang; Bradly C Stadie,2021,ICML 2021,main,Oral,,,0,8.757,0.000,,https://icml.cc/virtual/2021/poster/10063,http://proceedings.mlr.press/v139/zhang21x/zhang21x.pdf,offline_icml,,"Planning, the ability to analyze the structure of a problem in the large and decompose it into interrelated subproblems, is a hallmark of human intelligence. While deep reinforcement learning (RL) has shown great promise for solving relatively straightforward control tasks, it remains an open proble"
260,mumEBl0arj,Thinker: Learning to Plan and Act,Stephen Chung; Ivan Anokhin; David Krueger,2023,NIPS 2023,main,Poster,,Reinforcement learning;model-based reinforcement learning;planning;Monte Carlo Tree Search;Markov Decision Process,0,8.753,0.000,,https://nips.cc/virtual/2023/poster/70532,https://openreview.net/pdf?id=mumEBl0arj,offline_nips,,"We propose the Thinker algorithm, a novel approach that enables reinforcement learning agents to autonomously interact with and utilize a learned world model. The Thinker algorithm wraps the environment with a world model and introduces new actions designed for interacting with the world model. Thes"
261,JqqSTgdQ85F,Visuo-Tactile Transformers for Manipulation,Yizhou Chen; Mark Van der Merwe; Andrea Sipos; Nima Fazeli,2022,CORL 2022,main,Poster,,Multimodal Learning;Reinforcement Learning;Manipulation,0,8.750,0.000,,https://openreview.net/forum?id=JqqSTgdQ85F,,offline_corl,VTT uses multimodal feedback together with self and cross-modal attention to build latent heatmap representations that seamlessly integrate vision and touch,"Learning representations in the joint domain of vision and touch can improve manipulation dexterity, robustness, and sample-complexity by exploiting mutual information and complementary cues. Here, we present Visuo-Tactile Transformers (VTTs), a novel multimodal representation learning approach suit"
262,W0AY49wWrb,Learning Primitive Embodied World Models: Towards Scalable Robotic Learning,,2026,ICLR 2026,main,Active,"applications to robotics, autonomy, planning",World Models;Video Diffusion Models;Zero-Shot Policy Learning;Embodied Intelligence,0,8.749,0.000,,https://openreview.net/forum?id=W0AY49wWrb,,offline_iclr,,"While video-generation-based embodied world models have gained increasing
attention, their reliance on large-scale embodied interaction data remains a key
bottleneck. The scarcity, difficulty of collection, and high dimensionality of em-
bodied data fundamentally limit the alignment granularity betw"
263,Nu1D2IsmWH,ViTacFormer: Learning Cross-Modal Representation for Visuo-Tactile Dexterous Manipulation,Liang Heng; Haoran Geng; Kaifeng Zhang; Pieter Abbeel; Jitendra Malik,2026,ICLR 2026,main,Withdraw,"applications to robotics, autonomy, planning",dexterous manipulation;imitation learning;visuo-tactile representation,0,8.744,0.000,,https://openreview.net/forum?id=Nu1D2IsmWH,,offline_iclr,,"Dexterous manipulation is a cornerstone capability for robotic systems aiming to interact with the physical world in a human-like manner. Although vision-based methods have advanced rapidly, tactile sensing remains crucial for fine-grained control—particularly in unstructured or visually occluded se"
264,FsfJ3lJhMJ,Bootstrapping World Models from Dynamics Models in Multimodal Foundation Models,,2026,ICLR 2026,main,Active,"applications to computer vision, audio, language, and other modalities",language grounding;world model;inverse dynamics model;VLM,0,8.736,0.000,,https://openreview.net/forum?id=FsfJ3lJhMJ,,offline_iclr,,"To what extent do vision-and-language foundation models possess a realistic world model (observation $\times$ action $\rightarrow$ observation) and a dynamics model (observation $\times$ observation $\rightarrow$ action), when actions are expressed through language? While open-source foundation mode"
265,QWsLks8LCO,Grounded Answers for Multi-agent Decision-making Problem through Generative World Model,Zeyang Liu; Xinrui Yang; Shiguang Sun; Long Qian; Lipeng Wan,2024,NIPS 2024,main,Poster,generative_models,World Model;Multi-agent Reinforcement Learning;Model-based Reinforcement Learning,0,8.734,0.000,,https://neurips.cc/virtual/2024/poster/95231,https://openreview.net/pdf?id=QWsLks8LCO,offline_nips,,"Recent progress in generative models has stimulated significant innovations in many fields, such as image generation and chatbots. Despite their success, these models often produce sketchy and misleading solutions for complex multi-agent decision-making problems because they miss the trial-and-error"
266,CwNevJONgq,Simplifying Latent Dynamics with Softly State-Invariant World Models,Tankred Saanum; Peter Dayan; Eric Schulz,2024,NIPS 2024,main,Poster,reinforcement_learning,World model;latent dynamics;reinforcement learning;compression,0,8.729,0.000,,https://neurips.cc/virtual/2024/poster/96114,https://openreview.net/pdf?id=CwNevJONgq,offline_nips,,"To solve control problems via model-based reasoning or planning, an agent needs to know how its actions affect the state of the world. The actions an agent has at its disposal often change the state of the environment in systematic ways. However, existing techniques for world modelling do not guaran"
267,aVwhTcSMl4,World Models Should Prioritize the Unification of Physical and Social Dynamics,Xiaoyuan Zhang; Chengdong Ma; Yizhe Huang; Weidong Huang; Siyuan Qi,2025,NIPS 2025,Position,Poster,,World Model,0,8.719,0.000,,https://openreview.net/forum?id=aVwhTcSMl4,,offline_nips,,"World models, which explicitly learn environmental dynamics to lay the foundation for planning, reasoning, and decision-making, are rapidly advancing in predicting both physical dynamics and aspects of social behavior, yet predominantly in separate silos. This division results in a systemic failure "
268,1941,Open-World Dynamic Prompt and Continual Visual Representation Learning,Youngeun Kim; Jun Fang*; Qin Zhang; Zhaowei Cai; Yantao Shen,2024,ECCV 2024,main,Poster,,,0,8.716,0.000,,https://eccv2024.ecva.net//virtual/2024/poster/1941,https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/06595.pdf,offline_eccv,,"""The open world is inherently dynamic, characterized by ever-evolving concepts and distributions. Continual learning (CL) in this dynamic open-world environment presents a significant challenge in effectively generalizing to unseen test-time classes. To address this challenge, we introduce a new pra"
269,DJw1JBTmuk,Pre-Training Robo-Centric World Models For Efficient Visual Control,Long Qian; Ziru Wang; Sizhe Wang; Lipeng Wan; Zeyang Liu,2025,ICLR 2025,main,Reject,reinforcement learning,Pretraining World Models;Model-based Reinforcement Learning;Visual Robot Control,0,8.715,0.000,,https://openreview.net/forum?id=DJw1JBTmuk,,offline_iclr,,Humans can accurately anticipate their movements to behave as expected in various manipulation tasks. We are inspired to propose that integrating prior knowledge of robot dynamics into world models can effectively improve the sample efficiency of model-based reinforcement learning (MBRL) in visual r
270,KfaZaYYCvt,Semantic World Models,,2026,ICLR 2026,main,Active,"applications to robotics, autonomy, planning",Robotics;World Models;Vision Language Models,0,8.712,0.000,,https://openreview.net/forum?id=KfaZaYYCvt,,offline_iclr,,"Planning with world models offers a powerful paradigm for robotic control. Conventional approaches train a model to predict future frames conditioned on current frames and actions, which can then be used for planning. However, the objective of predicting future pixels is often at odds with the actua"
271,Bzlt5tPFT6,DMWM: Dual-Mind World Model with Long-Term Imagination,Lingyi Wang; Rashed Shelim; Walid Saad; Naren Ramakrishnan,2025,NIPS 2025,main,Spotlight,reinforcement_learning,World model;long-term planning;logical reasoning;dual-process system,0,8.706,0.000,,https://openreview.net/forum?id=Bzlt5tPFT6,,offline_nips,,"Imagination in world models is crucial for enabling agents to learn long-horizon policy in a sample-efficient manner. Existing recurrent state-space model (RSSM)-based world models depend on single-step statistical inference to capture the environment dynamics, and, hence, they are unable to perform"
272,0z0xhXRbN3,"Dyna-Think: Synergizing Reasoning, Acting, and World Model Simulation in AI Agents",,2026,ICLR 2026,main,Active,"applications to computer vision, audio, language, and other modalities",AI agents;computer-use;planning,0,8.706,0.000,,https://openreview.net/forum?id=0z0xhXRbN3,,offline_iclr,,"Recent progress in reasoning with large language models (LLMs), such as DeepSeek-R1, demonstrates impressive capabilities in domains like mathematics and coding, by exhibiting complex cognitive behaviors such as verification, goal decomposition, and self-reflection. However, it is unclear what behav"
273,TdBaDGCpjly,Transformer-based World Models Are Happy With 100k Interactions,Jan Robine; Marc Höftmann; Tobias Uelwer; Stefan Harmeling,2023,ICLR 2023,main,Poster,,Model-based Reinforcement Learning;World Models;Transfomers;Atari 100k benchmark,0,8.697,0.000,,https://iclr.cc/virtual/2023/poster/10946,https://openreview.net/pdf?id=TdBaDGCpjly,offline_iclr,,"Deep neural networks have been successful in many reinforcement learning settings. However, compared to human learners they are overly data hungry. To build a sample-efficient world model, we apply a transformer to real-world episodes in an autoregressive manner: not only the compact latent states a"
274,3FOfBcEEy1,Action-Conditioned Transformers for Decentralized Multi-Agent World Models,,2026,ICLR 2026,main,Active,reinforcement learning,Multi-Agent Reinforcement Learning;Reinforcement Learning;Contrastive Learning;World Model,0,8.695,0.000,,https://openreview.net/forum?id=3FOfBcEEy1,,offline_iclr,,"Multi-agent reinforcement learning (MARL) has achieved strong results on large-scale decision making, yet most methods are model-free, limiting sample efficiency and stability under non-stationary teammates. Model-based reinforcement learning (MBRL) can reduce data usage, but planning and search sca"
275,PA47sKU8CU,Image as a World: Generating Interactive World from Single Image via Panoramic Video Generation,Dongnan Gui; Xun Guo; Wengang Zhou; Yan Lu,2025,NIPS 2025,main,Poster,applications,visual world model;panoramic video generation,0,8.691,0.000,,https://openreview.net/forum?id=PA47sKU8CU,,offline_nips,,"Generating an interactive visual world from a single image is both challenging and practically valuable, as single-view inputs are easy to acquire and align well with prompt-driven applications such as gaming and virtual reality. This paper introduces a novel unified framework, Image as a World (**I"
276,31391,Volumetric Environment Representation for Vision-Language Navigation,Rui Liu; Wenguan Wang; Yi Yang,2024,CVPR 2024,main,Highlight,,,0,8.689,0.000,,https://cvpr.thecvf.com/virtual/2024/poster/31391,https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_Volumetric_Environment_Representation_for_Vision-Language_Navigation_CVPR_2024_paper.pdf,offline_cvpr,,Vision-language navigation (VLN) requires an agent to navigate through an 3D environment based on visual observations and natural language instructions. It is clear that the pivotal factor for successful navigation lies in the comprehensive scene understanding. Previous VLN agents employ monocular f
277,,Learning and Leveraging World Models in Visual Representation Learning,Quentin Garrido; Mahmoud Assran; Nicolas Ballas; Adrien Bardes; Laurent Najman,2024,CoRR,,,,,0,0.000,0.000,10.48550/ARXIV.2403.00504,https://dblp.org/rec/journals/corr/abs-2403-00504,,dblp,,
278,,CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning,Yang Yue; Yulin Wang 0002; Chenxin Tao; Pan Liu; Shiji Song,2025,CVPR,,,,,0,0.000,0.000,10.1109/CVPR52734.2025.01935,https://dblp.org/rec/conf/cvpr/YueWTLS025,,dblp,,
279,,Learning Visual Representation for Autonomous Drone Navigation via a Contrastive World Model,Jiang Zhao; Yibo Wang; Zhihao Cai; Ningjun Liu; Kun Wu,2024,IEEE Trans. Artif. Intell.,,,,,0,0.000,0.000,10.1109/TAI.2023.3283488,https://dblp.org/rec/journals/tai/ZhaoWCLWW24,,dblp,,
280,,ReCoRe: Regularized Contrastive Representation Learning of World Model,Rudra P. K. Poudel; Harit Pandya; Stephan Liwicki; Roberto Cipolla,2024,CVPR,,,,,0,0.000,0.000,10.1109/CVPR52733.2024.02161,https://dblp.org/rec/conf/cvpr/PoudelPLC24,,dblp,,
281,,Learning Latent Dynamic Robust Representations for World Models,Ruixiang Sun 0003; Hongyu Zang; Xin Li 0033; Riashat Islam,2024,ICML,,,,,0,0.000,0.000,,https://dblp.org/rec/conf/icml/SunZ0I24,,dblp,,
282,,CURLing the Dream: Contrastive Representations for World Modeling in Reinforcement Learning,Victor Augusto Kich; Jair Augusto Bottega; Raul Steinmetz; Ricardo Bedin Grando; Ayanori Yorozu,2024,CoRR,,,,,0,0.000,0.000,10.48550/ARXIV.2408.05781,https://dblp.org/rec/journals/corr/abs-2408-05781,,dblp,,
283,,Sparse representation learning with modified q-VAE towards minimal realization of world model,Taisuke Kobayashi; Ryoma Watanuki,2023,Adv. Robotics,,,,,0,0.000,0.000,10.1080/01691864.2023.2221715,https://dblp.org/rec/journals/ar/KobayashiW23,,dblp,,
284,,CupMar: A deep learning model for personalized news recommendation based on contextual user-profile and multi-aspect article representation,Dai Hoang Tran; Quan Z. Sheng; Wei Emma Zhang; Nguyen Hoang Tran; Nguyen Lu Dang Khoa,2023,World Wide Web,,,,,0,0.000,0.000,10.1007/S11280-022-01059-6,https://dblp.org/rec/journals/www/TranSZTK23,,dblp,,
285,,"One Person, One Model, One World: Learning Continual User Representation without Forgetting",Fajie Yuan; Guoxiao Zhang; Alexandros Karatzoglou; Joemon M. Jose; Beibei Kong,2021,SIGIR,,,,,0,0.000,0.000,10.1145/3404835.3462884,https://dblp.org/rec/conf/sigir/YuanZKJKL21,,dblp,,
286,,PreLAR: World Model Pre-training with Learnable Action Representation,Lixuan Zhang; Meina Kan; Shiguang Shan; Xilin Chen,2024,European Conference on Computer Vision,,,,,7,0.000,0.000,10.1007/978-3-031-73337-6_11,https://www.semanticscholar.org/paper/f5ef05e654d4555704f180ad7436f6afb72fb343,,semantic_scholar,,
287,,OccLLaMA: An Occupancy-Language-Action Generative World Model for Autonomous Driving,Julong Wei; Shanshuai Yuan; Pengfei Li; Qingda Hu; Zhongxue Gan,2024,arXiv.org,,,,,56,0.000,0.000,10.48550/arXiv.2409.03272,https://www.semanticscholar.org/paper/5f1e3af95db955328e016870e6c89ee70e172538,,semantic_scholar,,"The rise of multi-modal large language models(MLLMs) has spurred their applications in autonomous driving. Recent MLLM-based methods perform action by learning a direct mapping from perception to action, neglecting the dynamics of the world and the relations between action and world dynamics. In con"
288,,GAIA-1: A Generative World Model for Autonomous Driving,Anthony Hu; Lloyd Russell; Hudson Yeo; Zak Murez; George Fedoseev,2023,arXiv.org,,,,,404,0.000,0.000,10.48550/arXiv.2309.17080,https://www.semanticscholar.org/paper/98478ac589e5b40a20630ff54bb4eec4ab4c5f6b,https://arxiv.org/pdf/2309.17080,semantic_scholar,,"Autonomous driving promises transformative improvements to transportation, but building systems capable of safely navigating the unstructured complexity of real-world scenarios remains challenging. A critical problem lies in effectively predicting the various potential outcomes that may emerge in re"
289,,Meta-DT: Offline Meta-RL as Conditional Sequence Modeling with World Model Disentanglement,Zhi Wang; Li Zhang; Wenhao Wu; Yuanheng Zhu; Dongbin Zhao,2024,Neural Information Processing Systems,,,,,15,0.000,0.000,10.48550/arXiv.2410.11448,https://www.semanticscholar.org/paper/80eb4e5ab0e2d675afffd715be059e0bd3748970,,semantic_scholar,,A longstanding goal of artificial general intelligence is highly capable generalists that can learn from diverse experiences and generalize to unseen tasks. The language and vision communities have seen remarkable progress toward this trend by scaling up transformer-based models trained on massive d
290,,FLIP: Flow-Centric Generative Planning as General-Purpose Manipulation World Model,Chongkai Gao; Haozhuo Zhang; Zhixuan Xu; Zhehao Cai; Lin Shao,2024,,,,,,22,0.000,0.000,,https://www.semanticscholar.org/paper/ce1120ec964019399d17a103e0431e2ddda3a43c,,semantic_scholar,,"We aim to develop a model-based planning framework for world models that can be scaled with increasing model and data budgets for general-purpose manipulation tasks with only language and vision inputs. To this end, we present FLow-centric generative Planning (FLIP), a model-based planning algorithm"
291,,HJE: Joint Convolutional Representation Learning for Knowledge Hypergraph Completion,Zhao Li; Chenxu Wang; Xin Wang; Zirui Chen; Jianxin Li,2024,IEEE Transactions on Knowledge and Data Engineering,,,,,26,0.000,0.000,10.1109/TKDE.2024.3365727,https://www.semanticscholar.org/paper/29a7de7c9d5f4918f6f8460129f39973f4a8400d,,semantic_scholar,,"<italic>Knowledge hypergraph representation learning</italic>, which projects entities and <inline-formula><tex-math notation=""LaTeX"">$n$</tex-math><alternatives><mml:math><mml:mi>n</mml:mi></mml:math><inline-graphic xlink:href=""wang-ieq1-3365727.gif""/></alternatives></inline-formula>-ary relations "
292,,DisenSemi: Semi-Supervised Graph Classification via Disentangled Representation Learning,Yifan Wang; Xiao Luo; Chong Chen; Xian-Sheng Hua; Ming Zhang,2024,IEEE Transactions on Neural Networks and Learning Systems,,,,,36,0.000,0.000,10.1109/TNNLS.2024.3431871,https://www.semanticscholar.org/paper/143e658616c36f16295298320a25f982d53fb036,http://arxiv.org/pdf/2407.14081,semantic_scholar,,"Graph classification is a critical task in numerous multimedia applications, where graphs are employed to represent diverse types of multimedia data, including images, videos, and social networks. Nevertheless, in the real world, labeled graph data are always limited or scarce. To address this issue"
293,,Geometric Prior Guided Feature Representation Learning for Long-Tailed Classification,Yanbiao Ma; Licheng Jiao; F. Liu; Shuyuan Yang; Xu Liu,2024,International Journal of Computer Vision,,,,,21,0.000,0.000,10.1007/s11263-024-01983-2,https://www.semanticscholar.org/paper/1f50ee8a2ef64b94a840835c5d621dd15cdfdfdc,https://arxiv.org/pdf/2401.11436,semantic_scholar,,"Real-world data are long-tailed, the lack of tail samples leads to a significant limitation in the generalization ability of the model. Although numerous approaches of class re-balancing perform well for moderate class imbalance problems, additional knowledge needs to be introduced to help the tail "
294,,Contextuality Helps Representation Learning for Generalized Category Discovery,Tingzhang Luo; Mingxuan Du; Jiatao Shi; Xinxiang Chen; Bingchen Zhao,2024,International Conference on Information Photonics,,,,,15,0.000,0.000,10.48550/arXiv.2407.19752,https://www.semanticscholar.org/paper/8dd32c39e86a9af90ff532bea86e692ffa05087b,,semantic_scholar,,This paper introduces a novel approach to Generalized Category Discovery (GCD) by leveraging the concept of contextuality to enhance the identification and classification of categories in unlabeled datasets. Drawing inspiration from human cognition's ability to recognize objects within their context
295,,Self-Supervised Electroencephalogram Representation Learning for Automatic Sleep Staging: Model Development and Evaluation Study,Chaoqi Yang; MSc Cao Xiao; PhD M Brandon Westover; PhD Jimeng Sun,2021,JMIR AI,,,,,38,0.000,0.000,10.2196/46769,https://www.semanticscholar.org/paper/acfeea47264dd89948853201191332e1780bea1c,https://jmir.org/api/download?alt_name=ai_v2i1e46769_app1.pdf&filename=703d74c7bcae829b52b2676935da644d.pdf,semantic_scholar,,"Background Deep learning models have shown great success in automating tasks in sleep medicine by learning from carefully annotated electroencephalogram (EEG) data. However, effectively using a large amount of raw EEG data remains a challenge. Objective In this study, we aim to learn robust vector r"
296,,Fuzzy Representation Learning on Dynamic Graphs,Hong-Yu Yao; Yuanlong Yu; Chun-Yang Zhang; Yue-Na Lin; Shang-Jia Li,2024,"IEEE Transactions on Systems, Man, and Cybernetics: Systems",,,,,10,0.000,0.000,10.1109/TSMC.2023.3320749,https://www.semanticscholar.org/paper/48373e4e5216a44349d5f023ddd27b1efce5ce69,,semantic_scholar,,"Exploring dynamic patterns from complex and large-scale networks is a significant and challenging task in graph analysis. One of the most advanced solutions is dynamic graph representation learning, which embeds structural and temporal correlations into a representative vector for each node or subgr"
297,,Sphere2Vec: A General-Purpose Location Representation Learning over a Spherical Surface for Large-Scale Geospatial Predictions,Gengchen Mai; Yao Xuan; Wen-Hang Zuo; Yutong He; Jiaming Song,2023,Isprs Journal of Photogrammetry and Remote Sensing,,,,,54,0.000,0.000,10.48550/arXiv.2306.17624,https://www.semanticscholar.org/paper/ff3ebc15fdfe92c04b9e3c02e8aa873a507814cd,http://arxiv.org/pdf/2306.17624,semantic_scholar,,"Generating learning-friendly representations for points in space is a fundamental and long-standing problem in ML. Recently, multi-scale encoding schemes (such as Space2Vec and NeRF) were proposed to directly encode any point in 2D/3D Euclidean space as a high-dimensional vector, and has been succes"
298,,Fair Graph Representation Learning via Diverse Mixture-of-Experts,Zheyuan Liu; Chunhui Zhang; Yijun Tian; Erchi Zhang; Chao Huang,2023,The Web Conference,,,,,37,0.000,0.000,10.1145/3543507.3583207,https://www.semanticscholar.org/paper/524e61fa746af5b1b0b8b10d8557e93e82aac005,,semantic_scholar,,"Graph Neural Networks (GNNs) have demonstrated a great representation learning capability on graph data and have been utilized in various downstream applications. However, real-world data in web-based applications (e.g., recommendation and advertising) always contains bias, preventing GNNs from lear"
299,,Unicom: Universal and Compact Representation Learning for Image Retrieval,Xiang An; Jiankang Deng; Kaicheng Yang; Jaiwei Li; Ziyong Feng,2023,International Conference on Learning Representations,,,,,44,0.000,0.000,10.48550/arXiv.2304.05884,https://www.semanticscholar.org/paper/4274922ab64d4d994fe1ecc5d58e0b6c6c53d35b,http://arxiv.org/pdf/2304.05884,semantic_scholar,,"Modern image retrieval methods typically rely on fine-tuning pre-trained encoders to extract image-level descriptors. However, the most widely used models are pre-trained on ImageNet-1K with limited classes. The pre-trained feature representation is therefore not universal enough to generalize well "
300,,Leveraging sparse and shared feature activations for disentangled representation learning,Marco Fumero; F. Wenzel; L. Zancato; A. Achille; Emanuele Rodolà,2023,Neural Information Processing Systems,,,,,32,0.000,0.000,10.48550/arXiv.2304.07939,https://www.semanticscholar.org/paper/d32a0fdd974badf6bf5529e11c6776fe5ed88d00,http://arxiv.org/pdf/2304.07939,semantic_scholar,,"Recovering the latent factors of variation of high dimensional data has so far focused on simple synthetic settings. Mostly building on unsupervised and weakly-supervised objectives, prior work missed out on the positive implications for representation learning on real world data. In this work, we p"
301,,PINER: Prior-informed Implicit Neural Representation Learning for Test-time Adaptation in Sparse-view CT Reconstruction,Bowen Song; Liyue Shen; Lei Xing,2023,IEEE Workshop/Winter Conference on Applications of Computer Vision,,,,,32,0.000,0.000,10.1109/WACV56688.2023.00197,https://www.semanticscholar.org/paper/2820b30afcd82bd247ad8ce6a600c77a36e9fba0,,semantic_scholar,,"Recently, deep learning has been introduced to solve important medical image reconstruction problems such as sparse-view CT reconstruction. However, the developed deep reconstruction models are generally limited in generalization when applied to out-of-distribution samples in unseen domains. Further"
302,,An Ensemble Classification Model With Unsupervised Representation Learning for Driving Stress Recognition Using Physiological Signals,Ke Wang; Ping Guo,2020,IEEE transactions on intelligent transportation systems (Print),,,,,26,0.000,0.000,10.1109/TITS.2020.2980555,https://www.semanticscholar.org/paper/6ee0aa548ff1385d7248a871c9d4c285f62aa667,https://doi.org/10.1109/tits.2020.2980555,semantic_scholar,,"This paper presents an ensemble classification model with unsupervised feature learning for driving stress recognition under real-world driving conditions. The driving stress is detected using drivers’ different physiological signals, specifically the electromyogram, electrocardiogram, galvanic skin"
303,,Comprehensive Multiview Representation Learning via Deep Autoencoder-Like Nonnegative Matrix Factorization,Haonan Huang; Guoxu Zhou; Qianchuan Zhao; Lifang He; Shengli Xie,2023,IEEE Transactions on Neural Networks and Learning Systems,,,,,27,0.000,0.000,10.1109/TNNLS.2023.3304626,https://www.semanticscholar.org/paper/bb78039b15b7bad30139c5e740f193f7fd533205,,semantic_scholar,,Learning a comprehensive representation from multiview data is crucial in many real-world applications. Multiview representation learning (MRL) based on nonnegative matrix factorization (NMF) has been widely adopted by projecting high-dimensional space into a lower order dimensional space with great
304,,Dynamic Representation Learning via Recurrent Graph Neural Networks,Chun-Yang Zhang; Zhiliang Yao; Hong-Yu Yao; Feng Huang; C. L. P. Chen,2023,"IEEE Transactions on Systems, Man, and Cybernetics: Systems",,,,,27,0.000,0.000,10.1109/TSMC.2022.3196506,https://www.semanticscholar.org/paper/b2e17983164f1f7f9ad4a27c78c34fabb20353e5,,semantic_scholar,,"A large number of real-world systems generate graphs that are structured data aligned with nodes and edges. Graphs are usually dynamic in many scenarios, where nodes or edges keep evolving over time. Recently, graph representation learning (GRL) has received great success in network analysis, which "
305,,Action Image Representation: Learning Scalable Deep Grasping Policies with Zero Real World Data,Mohi Khansari; Daniel Kappler; Jianlan Luo; Jeffrey T. Bingham; Mrinal Kalakrishnan,2020,IEEE International Conference on Robotics and Automation,,,,,26,0.000,0.000,10.1109/ICRA40945.2020.9197415,https://www.semanticscholar.org/paper/db897344759e20ffbc468cfb92d6f8c930cbe15e,https://arxiv.org/pdf/2005.06594,semantic_scholar,,"This paper introduces Action Image, a new grasp proposal representation that allows learning an end-to-end deep-grasping policy. Our model achieves 84% grasp success on 172 real world objects while being trained only in simulation on 48 objects with just naive domain randomization. Similar to comput"
306,,Integrating 3D Model Representation for an Accurate Non-Invasive Assessment of Pressure Injuries with Deep Learning,Sofia Zahia; B. Garcia-Zapirain; Adel Said Elmaghraby,2020,Italian National Conference on Sensors,,,,,28,0.000,0.000,10.3390/s20102933,https://www.semanticscholar.org/paper/5b2a487f491434889f626aef5c34ec697cbae996,https://www.mdpi.com/1424-8220/20/10/2933/pdf?version=1591352202,semantic_scholar,,"Pressure injuries represent a major concern in many nations. These wounds result from prolonged pressure on the skin, which mainly occur among elderly and disabled patients. If retrieving quantitative information using invasive methods is the most used method, it causes significant pain and discomfo"
307,,Enhancing representation in radiography-reports foundation model: a granular alignment algorithm using masked contrastive learning,Weijian Huang; Cheng Li; Hong-Yu Zhou; Hao Yang; Jiarun Liu,2023,Nature Communications,,,,,51,0.000,0.000,10.1038/s41467-024-51749-0,https://www.semanticscholar.org/paper/9298cb4086ba3abaedcfc25f4a1ee1ff69de9a15,https://doi.org/10.1038/s41467-024-51749-0,semantic_scholar,,"Recently, multi-modal vision-language foundation models have gained significant attention in the medical field. While these models offer great opportunities, they still face crucial challenges, such as the requirement for fine-grained knowledge understanding in computer-aided diagnosis and the capab"
308,,Federated Representation Learning With Data Heterogeneity for Human Mobility Prediction,Xiao Zhang; Qilin Wang; Ziming Ye; Haochao Ying; Dongxiao Yu,2023,IEEE transactions on intelligent transportation systems (Print),,,,,22,0.000,0.000,10.1109/TITS.2023.3252029,https://www.semanticscholar.org/paper/22142c4df5cc6f6a734c7be84597d3f10cca62b0,,semantic_scholar,,"The advancement of smart wearable devices and location-based smart services has enabled a new paradigm for smart human mobility prediction (HMP), which has a broad range of applications in smart healthcare and smart cities. Due to the privacy concerns and rigorous data regulations, federated learnin"
309,,Memory-Enhanced Transformer for Representation Learning on Temporal Heterogeneous Graphs,Longhai Li; Lei Duan; Junchen Wang; Chengxin He; Zihao Chen,2023,Data Science and Engineering,,,,,20,0.000,0.000,10.1007/s41019-023-00207-w,https://www.semanticscholar.org/paper/8a4c12e0f5bddc04ad8e4977071a8227799bb6de,https://link.springer.com/content/pdf/10.1007/s41019-023-00207-w.pdf,semantic_scholar,,"Temporal heterogeneous graphs can model lots of complex systems in the real world, such as social networks and e-commerce applications, which are naturally time-varying and heterogeneous. As most existing graph representation learning methods cannot efficiently handle both of these characteristics, "
310,,Simultaneous Linear Multi-view Attributed Graph Representation Learning and Clustering,Chakib Fettal; Lazhar Labiod; M. Nadif,2023,Web Search and Data Mining,,,,,21,0.000,0.000,10.1145/3539597.3570367,https://www.semanticscholar.org/paper/a087f9632da1b24428bffaff70f8a43bb853ac1e,https://hal.science/hal-04467651/document,semantic_scholar,,"Over the last few years, various multi-view graph clustering methods have shown promising performances. However, we argue that these methods can have limitations. In particular, they are often unnecessarily complex, leading to scalability problems that make them prohibitive for most real-world graph"
311,,Efficiently Forgetting What You Have Learned in Graph Representation Learning via Projection,Weilin Cong; Mehrdad Mahdavi,2023,International Conference on Artificial Intelligence and Statistics,,,,,23,0.000,0.000,10.48550/arXiv.2302.08990,https://www.semanticscholar.org/paper/697d90ca7d7768526fc0bb8dd32e124a3070b22c,http://arxiv.org/pdf/2302.08990,semantic_scholar,,"As privacy protection receives much attention, unlearning the effect of a specific node from a pre-trained graph learning model has become equally important. However, due to the node dependency in the graph-structured data, representation unlearning in Graph Neural Networks (GNNs) is challenging and"
312,,Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal Bootstrapping,Luxuan Wang; Lei Bai; Ziyue Li; Rui Zhao; F. Tsung,2023,2023 IEEE 19th International Conference on Automation Science and Engineering (CASE),,,,,16,0.000,0.000,10.1109/CASE56687.2023.10260640,https://www.semanticscholar.org/paper/48b14f4b8a497d5d0db8ca7627047162b4fcd091,http://arxiv.org/pdf/2306.06994,semantic_scholar,,"Correlated time series analysis plays an important role in many real-world industries. Learning an efficient representation of this large-scale data for further downstream tasks is necessary but challenging. In this paper, we propose a time-step-level representation learning framework for individual"
313,,TransO: a knowledge-driven representation learning method with ontology information constraints,Zhao Li; Xin Liu; Xin Wang; Pengkai Liu; Yuxin Shen,2022,World wide web (Bussum),,,,,61,0.000,0.000,10.1007/s11280-022-01016-3,https://www.semanticscholar.org/paper/e5dc8448621556da24601cee1106f6e1e15bc11b,,semantic_scholar,,
314,,RoPAWS: Robust Semi-supervised Representation Learning from Uncurated Data,Sangwoo Mo; Jong-Chyi Su; Chih-Yao Ma; Mido Assran; Ishan Misra,2023,International Conference on Learning Representations,,,,,15,0.000,0.000,10.48550/arXiv.2302.14483,https://www.semanticscholar.org/paper/76f4416826b3393cf8f28ffd1a3191706d2b4286,http://arxiv.org/pdf/2302.14483,semantic_scholar,,"Semi-supervised learning aims to train a model using limited labels. State-of-the-art semi-supervised methods for image classification such as PAWS rely on self-supervised representations learned with large-scale unlabeled but curated data. However, PAWS is often less effective when using real-world"
315,,Video-3D LLM: Learning Position-Aware Video Representation for 3D Scene Understanding,Duo Zheng; Shijia Huang; Liwei Wang,2024,Computer Vision and Pattern Recognition,,,,,64,0.000,0.000,10.1109/CVPR52734.2025.00841,https://www.semanticscholar.org/paper/6444cc06ba4556a2c81aeb463f9974515aac9347,,semantic_scholar,,"The rapid advancement of Multimodal Large Language Models (MLLMs) has significantly impacted various multimodal tasks. However, these models face challenges in tasks that require spatial understanding within 3D environments. Efforts to enhance MLLMs, such as incorporating point cloud features, have "
316,,Simple Unsupervised Graph Representation Learning,Yujie Mo; Liang Peng; J. Xu; Xiaoshuang Shi; Xiaofeng Zhu,2022,AAAI Conference on Artificial Intelligence,,,,,156,0.000,0.000,10.1609/aaai.v36i7.20748,https://www.semanticscholar.org/paper/3cb7c04c80c1c75cd8df17c5b7ab6a5399563a65,https://ojs.aaai.org/index.php/AAAI/article/download/20748/20507,semantic_scholar,,"In this paper, we propose a simple unsupervised graph representation learning method to conduct effective and efficient contrastive learning. Specifically, the proposed multiplet loss explores the complementary information between the structural information and neighbor information to enlarge the in"
317,,FairSwiRL: fair semi-supervised classification with representation learning,Shuyi Yang; Mattia Cerrato; D. Ienco; R. Pensa; Roberto Esposito,2023,Machine-mediated learning,,,,,6,0.000,0.000,10.1007/s10994-023-06342-9,https://www.semanticscholar.org/paper/b4c574ecf2ea9ea5c1ba08cb0b4b2200c1148438,https://link.springer.com/content/pdf/10.1007/s10994-023-06342-9.pdf,semantic_scholar,,"Semi-supervised learning has shown its potential in many real-world applications where only few labeled examples are available. However, when some fairness constraints need to be satisfied, semi-supervised classification models often struggle as they are required to cope with the lack of sufficient "
318,,TREND: TempoRal Event and Node Dynamics for Graph Representation Learning,Zhihao Wen; Yuan Fang,2022,The Web Conference,,,,,101,0.000,0.000,10.1145/3485447.3512164,https://www.semanticscholar.org/paper/dc29560968af464ac80d51e6c57ce695ebe52500,https://ink.library.smu.edu.sg/context/sis_research/article/8485/viewcontent/TheWebConf22_TREND.pdf,semantic_scholar,,"Temporal graph representation learning has drawn significant attention for the prevalence of temporal graphs in the real world. However, most existing works resort to taking discrete snapshots of the temporal graph, or are not inductive to deal with new nodes, or do not model the exciting effects wh"
319,,DIRL: Domain-Invariant Representation Learning for Generalizable Semantic Segmentation,Qi Xu; Lili Yao; Zhengkai Jiang; Guannan Jiang; Wenqing Chu,2022,AAAI Conference on Artificial Intelligence,,,,,82,0.000,0.000,10.1609/aaai.v36i3.20193,https://www.semanticscholar.org/paper/fb76d171599e542d6b52102dd3bcaf14992233fc,https://ojs.aaai.org/index.php/AAAI/article/download/20193/19952,semantic_scholar,,"Model generalization to the unseen scenes is crucial to real-world applications, such as autonomous driving, which requires robust vision systems. To enhance the model generalization, domain generalization through learning the domain-invariant representation has been widely studied. However, most ex"
320,,Multi-type factors representation learning for deep learning-based knowledge tracing,Liangliang He; Jintao Tang; Xiao Li; Pancheng Wang; Feng Chen,2022,World wide web (Bussum),,,,,17,0.000,0.000,10.1007/s11280-022-01041-2,https://www.semanticscholar.org/paper/f86e298bac53987a3b1e0ce3190d3c55601b9706,,semantic_scholar,,
321,,Neighborhood-aware Scalable Temporal Network Representation Learning,Yu Luo; Pan Li,2022,LOG IN,,,,,63,0.000,0.000,10.48550/arXiv.2209.01084,https://www.semanticscholar.org/paper/931186d1ba3c6bcf34f69ad7e582cdcdac5503b2,https://arxiv.org/pdf/2209.01084,semantic_scholar,,"Temporal networks have been widely used to model real-world complex systems such as financial systems and e-commerce systems. In a temporal network, the joint neighborhood of a set of nodes often provides crucial structural information useful for predicting whether they may interact at a certain tim"
322,,VatLM: Visual-Audio-Text Pre-Training With Unified Masked Prediction for Speech Representation Learning,Qiu-shi Zhu; Long Zhou; Zi-Hua Zhang; Shujie Liu; Binxing Jiao,2022,IEEE transactions on multimedia,,,,,48,0.000,0.000,10.1109/TMM.2023.3275873,https://www.semanticscholar.org/paper/00b3421e147da7a0c1b60c15c878532cfc93ece6,http://arxiv.org/pdf/2211.11275,semantic_scholar,,"Although speech is a simple and effective way for humans to communicate with the outside world, a more realistic speech interaction contains multimodal information, e.g., vision, text. How to design a unified framework to integrate different modal information and leverage different resources (e.g., "
323,,RRL-GAT: Graph Attention Network-Driven Multilabel Image Robust Representation Learning,Bin Hu; Kehua Guo; Xiaokang Wang; Jian Zhang; Di Zhou,2022,IEEE Internet of Things Journal,,,,,53,0.000,0.000,10.1109/jiot.2021.3089180,https://www.semanticscholar.org/paper/ffb49012ed82f028bc404763564b3afbff3976c5,,semantic_scholar,,"Exploring the characterization laws of image data and improving the efficiency of image data characterization knowledge is essential to promote the development of the Internet of Things technology. Considering that images in the real world usually contain multiple objects, and the objects are closel"
324,,Causal Reasoning Meets Visual Representation Learning: A Prospective Study,Y. Liu; Yushen Wei; Hongyu Yan; Guanbin Li; Liang Lin,2022,Machine Intelligence Research,,,,,59,0.000,0.000,10.1007/s11633-022-1362-z,https://www.semanticscholar.org/paper/8274a4a71e1e5605bd5a9eb69c4f0df5f30bd1a8,https://link.springer.com/content/pdf/10.1007/s11633-022-1362-z.pdf,semantic_scholar,,"Visual representation learning is ubiquitous in various real-world applications, including visual comprehension, video understanding, multi-modal analysis, human-computer interaction, and urban computing. Due to the emergence of huge amounts of multimodal heterogeneous spatial/temporal/spatial-tempo"
325,,Efficient Graph Convolution for Joint Node Representation Learning and Clustering,Chakib Fettal; Lazhar Labiod; M. Nadif,2022,Web Search and Data Mining,,,,,44,0.000,0.000,10.1145/3488560.3498533,https://www.semanticscholar.org/paper/51483cb4ff1496a39d08b2ce46768efbcc1f3252,,semantic_scholar,,"Attributed graphs are used to model a wide variety of real-world networks. Recent graph convolutional network-based representation learning methods have set state-of-the-art results on the clustering of attributed graphs. However, these approaches deal with clustering as a downstream task while bett"
326,,Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks,Jintang Li; Zhouxin Yu; Zulun Zhu; Liang Chen; Qi Yu,2022,AAAI Conference on Artificial Intelligence,,,,,43,0.000,0.000,10.48550/arXiv.2208.10364,https://www.semanticscholar.org/paper/5f68069bd99ae7413e6db72d2a2906f732a66916,http://arxiv.org/pdf/2208.10364,semantic_scholar,,"Recent years have seen a surge in research on dynamic graph representation learning, which aims to model temporal graphs that are dynamic and evolving constantly over time. However, current work typically models graph dynamics with recurrent neural networks (RNNs), making them suffer seriously from "
327,,MultiBench: Multiscale Benchmarks for Multimodal Representation Learning,Paul Pu Liang; Yiwei Lyu; Xiang Fan; Zetian Wu; Yun Cheng,2021,NeurIPS Datasets and Benchmarks,,,,,222,0.000,0.000,,https://www.semanticscholar.org/paper/af86df6a0af3226a1b4b5eb27c17c9e45367f896,,semantic_scholar,,"Learning multimodal representations involves integrating information from multiple heterogeneous sources of data. It is a challenging yet crucial area with numerous real-world applications in multimedia, affective computing, robotics, finance, human-computer interaction, and healthcare. Unfortunatel"
328,,Cross-Linked Unified Embedding for cross-modality representation learning,Xinming Tu; Zhi-Jie Cao; Chen-Rui Xia; S. Mostafavi; Ge Gao,2022,Neural Information Processing Systems,,,,,30,0.000,0.000,,https://www.semanticscholar.org/paper/8b93bdab5a247e94b4f39ec7e2cf80f17326a4bc,,semantic_scholar,,
329,,Enhancing CTR Prediction with Context-Aware Feature Representation Learning,Fangye Wang; Yingxu Wang; Dongsheng Li; Hansu Gu; T. Lu,2022,Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,,,,,51,0.000,0.000,10.1145/3477495.3531970,https://www.semanticscholar.org/paper/6a4112746b8c276f380ca061c68ec0d281140831,https://arxiv.org/pdf/2204.08758,semantic_scholar,,"CTR prediction has been widely used in the real world. Many methods model feature interaction to improve their performance. However, most methods only learn a fixed representation for each feature without considering the varying importance of each feature under different contexts, resulting in infer"
330,,Self-Supervised Fair Representation Learning without Demographics,Junyi Chai; Xiaoqian Wang,2022,Neural Information Processing Systems,,,,,32,0.000,0.000,,https://www.semanticscholar.org/paper/eec9ef3f713a90898eaa60acea50b206f21b616c,,semantic_scholar,,
331,,Graph-Text Multi-Modal Pre-training for Medical Representation Learning,Sungjin Park; Seongsu Bae; Jiho Kim; Tackeun Kim; E. Choi,2022,"ACM Conference on Health, Inference, and Learning",,,,,23,0.000,0.000,10.48550/arXiv.2203.09994,https://www.semanticscholar.org/paper/903e43969669a84f1c2e775408d17f4e49d12e80,http://arxiv.org/pdf/2203.09994,semantic_scholar,,"As the volume of Electronic Health Records (EHR) sharply grows, there has been emerging interest in learning the representation of EHR for healthcare applications. Representation learning of EHR requires appropriate modeling of the two dominant modalities in EHR: structured data and unstructured tex"
332,,Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning,Yang Yue; Bingyi Kang; Zhongwen Xu; Gao Huang; Shuicheng Yan,2022,AAAI Conference on Artificial Intelligence,,,,,18,0.000,0.000,10.48550/arXiv.2206.12542,https://www.semanticscholar.org/paper/e3c598f5233d91f887edb626bb02ba86010a69c0,http://arxiv.org/pdf/2206.12542,semantic_scholar,,"Deep reinforcement learning (RL) algorithms suffer severe performance degradation when the interaction data is scarce, which limits their real-world application. Recently, visual representation learning has been shown to be effective and promising for boosting sample efficiency in RL. These methods "
333,,SpaceTimePilot: Generative Rendering of Dynamic Scenes Across Space and Time,Zhening Huang; Hyeonho Jeong; Xuelin Chen; Yulia Gryaditskaya; Tuanfeng Y. Wang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25075v1,https://arxiv.org/pdf/2512.25075v1,arxiv,,"We present SpaceTimePilot, a video diffusion model that disentangles space and time for controllable generative rendering. Given a monocular video, SpaceTimePilot can independently alter the camera viewpoint and the motion sequence within the generative process, re-rendering the scene for continuous"
334,,Randomization Times under Quantum Chaotic Hamiltonian Evolution,Souradeep Ghosh; Nicholas Hunter-Jones; Joaquin F. Rodriguez-Nieva,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25074v1,https://arxiv.org/pdf/2512.25074v1,arxiv,,"Randomness generation through quantum-chaotic evolution underpins foundational questions in statistical mechanics and applications across quantum information science, including benchmarking, tomography, metrology, and demonstrations of quantum computational advantage. While statistical mechanics suc"
335,,Edit3r: Instant 3D Scene Editing from Sparse Unposed Images,Jiageng Liu; Weijie Lyu; Xueting Li; Yejie Guo; Ming-Hsuan Yang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25071v1,https://arxiv.org/pdf/2512.25071v1,arxiv,,"We present Edit3r, a feed-forward framework that reconstructs and edits 3D scenes in a single pass from unposed, view-inconsistent, instruction-edited images. Unlike prior methods requiring per-scene optimization, Edit3r directly predicts instruction-aligned 3D edits, enabling fast and photorealisti"
336,,Coordinated Humanoid Manipulation with Choice Policies,Haozhi Qi; Yen-Jen Wang; Toru Lin; Brent Yi; Yi Ma,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25072v1,https://arxiv.org/pdf/2512.25072v1,arxiv,,"Humanoid robots hold great promise for operating in human-centric environments, yet achieving robust whole-body coordination across the head, hands, and legs remains a major challenge. We present a system that combines a modular teleoperation interface with a scalable learning framework to address t"
337,,Scaling Open-Ended Reasoning to Predict the Future,Nikhil Chandak; Shashwat Goel; Ameya Prabhu; Moritz Hardt; Jonas Geiping,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25070v1,https://arxiv.org/pdf/2512.25070v1,arxiv,,"High-stakes decision making involves reasoning under uncertainty about the future. In this work, we train language models to make predictions on open-ended forecasting questions. To scale up training data, we synthesize novel forecasting questions from global events reported in daily news, using a f"
338,,FineTec: Fine-Grained Action Recognition Under Temporal Corruption via Skeleton Decomposition and Sequence Completion,Dian Shao; Mingfei Shi; Like Liu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25067v1,https://arxiv.org/pdf/2512.25067v1,arxiv,,"Recognizing fine-grained actions from temporally corrupted skeleton sequences remains a significant challenge, particularly in real-world scenarios where online pose estimation often yields substantial missing data. Existing methods often struggle to accurately recover temporal dynamics and fine-gra"
339,,From Inpainting to Editing: A Self-Bootstrapping Framework for Context-Rich Visual Dubbing,Xu He; Haoxian Zhang; Hejia Chen; Changyuan Zheng; Liyang Chen,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25066v1,https://arxiv.org/pdf/2512.25066v1,arxiv,,"Audio-driven visual dubbing aims to synchronize a video's lip movements with new speech, but is fundamentally challenged by the lack of ideal training data: paired videos where only a subject's lip movements differ while all other visual conditions are identical. Existing methods circumvent this wit"
340,,Vulcan: Instance-Optimal Systems Heuristics Through LLM-Driven Search,Rohit Dwivedula; Divyanshu Saxena; Sujay Yadalam; Daehyeok Kim; Aditya Akella,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25065v1,https://arxiv.org/pdf/2512.25065v1,arxiv,,"Resource-management tasks in modern operating and distributed systems continue to rely primarily on hand-designed heuristics for tasks such as scheduling, caching, or active queue management. Designing performant heuristics is an expensive, time-consuming process that we are forced to continuously g"
341,,Feeling Blue: Constructing a Robust SALT3 UV Template and Constraining its Redshift Dependency,Qinan Wang; David O. Jones; Justin D. R. Pierel; Matthew R. Siebert; W. D'Arcy Kenworthy,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25064v1,https://arxiv.org/pdf/2512.25064v1,arxiv,,"Upcoming cosmological surveys will obtain numerous rest-frame ultraviolet (UV) observations of Type Ia supernovae (SNe Ia), yet there is concern about how standardizable SNe Ia are in the UV. In this work, we train a robust optical--UV SED model for SNe Ia (SALT3-UV) with the open-source model-train"
342,,Many Minds from One Model: Bayesian Transformers for Population Intelligence,Diji Yang; Yi Zhang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25063v1,https://arxiv.org/pdf/2512.25063v1,arxiv,,"Despite their scale and success, modern transformers are almost universally trained as single-minded systems: optimization produces one deterministic set of parameters, representing a single functional hypothesis about the data. Motivated by the idea that intelligence emerge from many minds, we prop"
343,,Melting curve of correlated iron at Earth's core conditions from machine-learned DFT+DMFT,Rishi Rao; Li Zhu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25061v1,https://arxiv.org/pdf/2512.25061v1,arxiv,,"Reliable constraints on iron's melting curve at Earth's inner-core boundary require accurate finite-temperature electronic correlations, yet DFT+DMFT calculations remain too costly for large-scale thermodynamic sampling. Here, we develop a machine-learning accelerator for charge self-consistent DFT+"
344,,On the geometry and topology of representations: the manifolds of modular addition,Gabriela Moisescu-Pareja; Gavin McCracken; Harley Wiltzer; Vincent Létourneau; Colin Daniels,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25060v1,https://arxiv.org/pdf/2512.25060v1,arxiv,,"The Clock and Pizza interpretations, associated with architectures differing in either uniform or learnable attention, were introduced to argue that different architectural designs can yield distinct circuits for modular addition. In this work, we show that this is not the case, and that both unifor"
345,,Reliable and Resilient Collective Communication Library for LLM Training and Serving,Wei Wang; Nengneng Yu; Sixian Xiong; Zaoxing Liu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25059v1,https://arxiv.org/pdf/2512.25059v1,arxiv,,"Modern ML training and inference now span tens to tens of thousands of GPUs, where network faults can waste 10--15\% of GPU hours due to slow recovery. Common network errors and link fluctuations trigger timeouts that often terminate entire jobs, forcing expensive checkpoint rollback during training"
346,,Sequential Bayesian parameter-state estimation in dynamical systems with noisy and incomplete observations via a variational framework,Liliang Wang; Alex Gorodetsky,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25056v1,https://arxiv.org/pdf/2512.25056v1,arxiv,,"Online joint estimation of unknown parameters and states in a dynamical system with uncertainty quantification is crucial in many applications. For example, digital twins dynamically update their knowledge of model parameters and states to support prediction and decision-making. Reliability and comp"
347,,Context-aware LLM-based AI Agents for Human-centered Energy Management Systems in Smart Buildings,Tianzhi He; Farrokh Jazizadeh,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25055v1,https://arxiv.org/pdf/2512.25055v1,arxiv,,This study presents a conceptual framework and a prototype assessment for Large Language Model (LLM)-based Building Energy Management System (BEMS) AI agents to facilitate context-aware energy management in smart buildings through natural language interaction. The proposed framework comprises three 
348,,Fluid dynamics as intersection problem,Nikita Nekrasov; Paul Wiegmann,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25053v1,https://arxiv.org/pdf/2512.25053v1,arxiv,,"We formulate the covariant hydrodynamics equations describing the fluid dynamics as the problem of intersection theory on the infinite dimensional symplectic manifold associated with spacetime. This point of view separates the structures related to the equation of state, the geometry of spacetime, a"
349,,The PDE-ODI principle and cylindrical mean curvature flows,Richard H. Bamler; Yi Lai,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25050v1,https://arxiv.org/pdf/2512.25050v1,arxiv,,"We introduce a new approach for analyzing ancient solutions and singularities of mean curvature flow that are locally modeled on a cylinder. Its key ingredient is a general mechanism, called the \emph{PDE--ODI principle}, which converts a broad class of parabolic differential equations into systems "
350,,Extreme nonlinear optics in optical fibers,Mario Ferraro; Bertrand Kibler; Pierre Béjot; Frédéric Gérome; Benoit Debord,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25046v1,https://arxiv.org/pdf/2512.25046v1,arxiv,,"This paper reviews the field of extreme nonlinear optics in optical fibers, highlighting key phenomena and advancements. It discusses multiple ionization effects caused by femtosecond laser pulses that generate plasma and induce permanent material modifications, as well as plasma luminescence and it"
351,,Bayesian Elastic Net Regression with Structured Prior Dependence,Christopher M. Hans; Ningyi Liu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25045v1,https://arxiv.org/pdf/2512.25045v1,arxiv,,"Many regularization priors for Bayesian regression assume the regression coefficients are a priori independent. In particular this is the case for standard Bayesian treatments of the lasso and the elastic net. While independence may be reasonable in some data-analytic settings, incorporating depende"
352,,Compound Estimation for Binomials,Yan Chen; Lihua Lei,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25042v1,https://arxiv.org/pdf/2512.25042v1,arxiv,,"Many applications involve estimating the mean of multiple binomial outcomes as a common problem -- assessing intergenerational mobility of census tracts, estimating prevalence of infectious diseases across countries, and measuring click-through rates for different demographic groups. The most standa"
353,,Towards precision cosmology with Voids x CMB correlations (I): Roman-Agora mock catalogs and pipeline validation,Mar Pérez Sar; Carlos Hernández Monteagudo; András Kovács; Alice Pisani,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25040v1,https://arxiv.org/pdf/2512.25040v1,arxiv,,"We construct and validate a set of multi-purpose mock galaxy catalogs designed to capture, to different degrees of accuracy, the main characteristics of the Nancy Grace Roman Space Telescope survey. These catalogs provide a foundation for void statistics and various CMB cross-correlation analyses. O"
354,,The Hochschild homology of a noncommutative symmetric quotient stack,Rina Anno; Vladimir Baranovsky; Timothy Logvinenko,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25039v1,https://arxiv.org/pdf/2512.25039v1,arxiv,,"We prove an orbifold type decomposition theorem for the Hochschild homology of the symmetric powers of a small DG category $\mathcal{A}$. In noncommutative geometry, these can be viewed as the noncommutative symmetric quotient stacks of $\mathcal{A}$. We use this decomposition to show that the total"
355,,Anomalous (3+1)d Fermionic Topological Quantum Field Theories via Symmetry Extension,Zheyan Wan; Juven Wang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25038v1,https://arxiv.org/pdf/2512.25038v1,arxiv,,"Discrete finite-group global symmetries may suffer from nonperturbative 't-Hooft anomalies. Such global anomalies can be canceled by anomalous symmetry-preserving topological quantum field theories (TQFTs), which contain no local point operators but only extended excitations such as line and surface"
356,,Large Neutrino-Dark Matter Interactions: From Effective Field Theory to Ultraviolet Completions,K. S. Babu; P. S. Bhupal Dev; Anil Thapa,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25035v1,https://arxiv.org/pdf/2512.25035v1,arxiv,,"We develop a general effective field theory (EFT) framework for neutrino-dark matter (DM) interactions, and apply it to systematically find all possible gauge-invariant ultraviolet (UV) completions at a given EFT operator dimension. Our goal here is to find simple UV-complete models that can realize"
357,,Generative Classifiers Avoid Shortcut Solutions,Alexander C. Li; Ananya Kumar; Deepak Pathak,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25034v1,https://arxiv.org/pdf/2512.25034v1,arxiv,,"Discriminative approaches to classification often learn shortcuts that hold in-distribution but fail even under minor distribution shift. This failure mode stems from an overreliance on features that are spuriously correlated with the label. We show that generative classifiers, which use class-condi"
358,,EF(X) Orientations: A Parameterized Complexity Perspective,Sotiris Kanellopoulos; Edouard Nemery; Christos Pergaminelis; Minas Marios Sotiriou; Manolis Vasilakis,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25033v1,https://arxiv.org/pdf/2512.25033v1,arxiv,,"The concept of fair orientations in graphs was introduced by Christodoulou, Fiat, Koutsoupias, and Sgouritsa in 2023, naturally modeling fair division scenarios in which resources are only contested by neighbors. In this model, vertices represent agents and undirected edges represent goods; edges ha"
359,,Testing Monotonicity in a Finite Population,Jiafeng Chen; Jonathan Roth; Jann Spiess,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25032v1,https://arxiv.org/pdf/2512.25032v1,arxiv,,"We consider the extent to which we can learn from a completely randomized experiment whether everyone has treatment effects that are weakly of the same sign, a condition we call monotonicity. From a classical sampling perspective, it is well-known that monotonicity is untestable. By contrast, we sho"
360,,Fractal conduction pathways governing ionic transport in a glass,J. L. Iguain; F. O. Sanchez-Varreti; M. A. Frechero,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25031v1,https://arxiv.org/pdf/2512.25031v1,arxiv,,"We present a systematic characterization of the fractal conduction pathways governing ionic transport in a non-crystalline solid below the glass-transition temperature. Using classical molecular dynamics simulations of lithium metasilicate, we combine mobility-resolved dynamical analysis with a real"
361,,Multivariate Generalized Counting Process via Gamma Subordination,Manisha Dhillon; Kuldeep Kumar Kataria; Shyan Ghosh,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25030v1,https://arxiv.org/pdf/2512.25030v1,arxiv,,"In this paper, we study a multivariate gamma subordinator whose components are independent gamma processes subject to a random time governed by an independent negative binomial process. We derive the explicit expressions for its joint Laplace-Stieltjes transform, its probability density function and"
362,,Mod $p$ Poincaré duality for $p$-adic period domains,Guillaume Pignon-Ywanne,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25029v1,https://arxiv.org/pdf/2512.25029v1,arxiv,,"In this article, we introduce a new class of smooth partially proper rigid analytic varieties over a $p$-adic field that satisfy Poincaré duality for étale cohomology with mod $p$-coefficients : the varieties satisfying ""primitive comparison with compact support"". We show that almost proper varietie"
363,,Universal Seesaw Pati-Salam Model with P for Strong CP,K. S. Babu; Sumit Biswas,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25028v1,https://arxiv.org/pdf/2512.25028v1,arxiv,,"We develop a universal seesaw version of the Pati-Salam model wherein quarks and leptons of each family are unified into common multiplets transforming as $ψ_L(2,1,4))+ ψ_R((1,2,4)$ under the $SU(2)_L \times SU(2)_R \times SU(4)_c$ gauge symmetry. Parity symmetry is spontaneously broken in the model"
364,,Computational Analysis of Disease Progression in Pediatric Pulmonary Arterial Hypertension,Omar Said; Christopher Tossas-Betancourt; Mary K. Olive; Jimmy C. Lu; Adam Dorfman,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25027v1,https://arxiv.org/pdf/2512.25027v1,arxiv,,"Pulmonary arterial hypertension (PAH) is a progressive cardiopulmonary disease that leads to increased pulmonary pressures, vascular remodeling, and eventual right ventricular (RV) failure. Pediatric PAH remains understudied due to limited data and the lack of targeted diagnostic and therapeutic str"
365,,Modeling Language as a Sequence of Thoughts,Nasim Borazjanizadeh; James McClelland,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25026v1,https://arxiv.org/pdf/2512.25026v1,arxiv,,"Transformer language models can generate strikingly natural text by modeling language as a sequence of tokens. Yet, by relying primarily on surface-level co-occurrence statistics, they fail to form globally consistent latent representations of entities and events, lack of which contributes to brittl"
366,,Modewise Additive Factor Model for Matrix Time Series,Elynn Chen; Yuefeng Han; Jiayu Li; Ke Xu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25025v1,https://arxiv.org/pdf/2512.25025v1,arxiv,,"We introduce a Modewise Additive Factor Model (MAFM) for matrix-valued time series that captures row-specific and column-specific latent effects through an additive structure, offering greater flexibility than multiplicative frameworks such as Tucker and CP factor models. In MAFM, each observation d"
367,,ResponseRank: Data-Efficient Reward Modeling through Preference Strength Learning,Timo Kaufmann; Yannick Metz; Daniel Keim; Eyke Hüllermeier,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25023v1,https://arxiv.org/pdf/2512.25023v1,arxiv,,"Binary choices, as often used for reinforcement learning from human feedback (RLHF), convey only the direction of a preference. A person may choose apples over oranges and bananas over grapes, but which preference is stronger? Strength is crucial for decision-making under uncertainty and generalizat"
368,,Real Riemann Surfaces: Smooth and Discrete,Johanna Düntsch; Felix Günther,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25022v1,https://arxiv.org/pdf/2512.25022v1,arxiv,,This paper develops a discrete theory of real Riemann surfaces based on quadrilateral cellular decompositions (quad-graphs) and a linear discretization of the Cauchy-Riemann equations. We construct a discrete analogue of an antiholomorphic involution and classify the topological types of discrete re
369,,"Detector Response Matrices, Effective Areas, and Flash-Effective Areas for Radiation Detectors",Gregory Bowers; Eve Chase; William Ford; Daniel Coupland; Brian Larsen,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25021v1,https://arxiv.org/pdf/2512.25021v1,arxiv,,"A Detector Response Matrix (DRM) is a discrete representation of an instrument's Detector Response Function (DRF), which quantifies how many discrete energy depositions occur in a detector volume for a given distribution of particles incident on the detector. For simple radiation detectors that can "
370,,Loop-Level Lepton Flavor Violation and Diphoton Signals in the Minimal Left-Right Symmetric Model,Shufang Qiang; Peiwen Wu; Yongchao Zhang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25019v1,https://arxiv.org/pdf/2512.25019v1,arxiv,,"The left-right symmetric model (LRSM) could not only restore parity of the weak interaction, but also provide natural explanations of the tiny active neutrino masses via the seesaw mechanisms. The $SU(2)_R$-breaking scalar $H_3$ can induce lepton flavor violating (LFV) effects in the minimal version"
371,,Strengthening Dual Bounds for Multicommodity Capacitated Network Design with Unsplittable Flow Constraints,Lacy M. Greening; Santanu S. Dey; Alan L. Erera,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25018v1,https://arxiv.org/pdf/2512.25018v1,arxiv,,"Multicommodity capacitated network design (MCND) models can be used to optimize the consolidation of shipments within e-commerce fulfillment networks. In practice, fulfillment networks require that shipments with the same origin and destination follow the same transfer path. This unsplittable flow r"
372,,Convergence of the generalization error for deep gradient flow methods for PDEs,Chenguang Liu; Antonis Papapantoleon; Jasper Rou,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25017v1,https://arxiv.org/pdf/2512.25017v1,arxiv,,The aim of this article is to provide a firm mathematical foundation for the application of deep gradient flow methods (DGFMs) for the solution of (high-dimensional) partial differential equations (PDEs). We decompose the generalization error of DGFMs into an approximation and a training error. We f
373,,"Approximations for the Weighted Reversal, Transposition, and Indel Distance Problem with Intergenic Region Information",Gabriel Siqueira; Alexsandro Oliveira Alexandrino; Zanoni Dias,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25016v1,https://arxiv.org/pdf/2512.25016v1,arxiv,,"Genome rearrangement distances are an established method in genome comparison. Works in this area may include various rearrangement operations representing large-scale mutations, gene orientation information, the number of nucleotides in intergenic regions, and weights reflecting the expected freque"
374,,MAMA-Memeia! Multi-Aspect Multi-Agent Collaboration for Depressive Symptoms Identification in Memes,Siddhant Agarwal; Adya Dhuler; Polly Ruhnke; Melvin Speisman; Md Shad Akhtar,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25015v1,https://arxiv.org/pdf/2512.25015v1,arxiv,,"Over the past years, memes have evolved from being exclusively a medium of humorous exchanges to one that allows users to express a range of emotions freely and easily. With the ever-growing utilization of memes in expressing depressive sentiments, we conduct a study on identifying depressive sympto"
375,,Diffusion Language Models are Provably Optimal Parallel Samplers,Haozhe Jiang; Nika Haghtalab; Lijie Chen,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25014v1,https://arxiv.org/pdf/2512.25014v1,arxiv,,Diffusion language models (DLMs) have emerged as a promising alternative to autoregressive models for faster inference via parallel token generation. We provide a rigorous foundation for this advantage by formalizing a model of parallel sampling and showing that DLMs augmented with polynomial-length
376,,Parity order as a fundamental driver of bosonic topology,Ashirbad Padhan; Harsh Nigam,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25011v1,https://arxiv.org/pdf/2512.25011v1,arxiv,,"Symmetry-protected topological (SPT) phases in interacting bosonic systems have been extensively studied, yet most realizations rely on fine-tuned interactions or enlarged symmetries. Here we show that a qualitatively different mechanism--parity order coupled to bond dimerization--acts as a fundamen"
377,,Bounding regularity of $\mathrm{VI}^m$-modules,Wee Liang Gan; Khoa Ta,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25010v1,https://arxiv.org/pdf/2512.25010v1,arxiv,,Fix a finite field $\mathbb{F}$. Let $\mathrm{VI}$ be a skeleton of the category of finite dimensional $\mathbb{F}$-vector spaces and injective $\mathbb{F}$-linear maps. We study $\mathrm{VI}^m$-modules over a noetherian commutative ring in the nondescribing characteristic case. We prove that if a f
378,,FoundationSLAM: Unleashing the Power of Depth Foundation Models for End-to-End Dense Visual SLAM,Yuchen Wu; Jiahe Li; Fabio Tosi; Matteo Poggi; Jin Zheng,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25008v1,https://arxiv.org/pdf/2512.25008v1,arxiv,,"We present FoundationSLAM, a learning-based monocular dense SLAM system that addresses the absence of geometric consistency in previous flow-based approaches for accurate and robust tracking and mapping. Our core idea is to bridge flow estimation with geometric reasoning by leveraging the guidance f"
379,,"Distributions of wide binary stars in theory and in Gaia data: III. Orbital momenta, masses, and manifestations of MOND",Valeri V. Makarov,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25002v1,https://arxiv.org/pdf/2512.25002v1,arxiv,,"Using the censored catalog of 103,169 resolved Gaia DR3 binary stars with accurate astrometric data for each component, a new observable, object-specific parameter is computed for each pair: the projected orbital momentum. This parameter is the product of four functions of physical characteristics: "
380,,The local limit of weighted spanning trees on balanced networks,Ágnes Kúsz,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25001v1,https://arxiv.org/pdf/2512.25001v1,arxiv,,"We prove that the local limit of the weighted spanning trees on any simple connected high degree almost regular sequence of electric networks is the Poisson(1) branching process conditioned to survive forever, by generalizing [NP22] and closing a gap in their proof. We also study the local statistic"
381,,Bi-C2R: Bidirectional Continual Compatible Representation for Re-indexing Free Lifelong Person Re-identification,Zhenyu Cui; Jiahuan Zhou; Yuxin Peng,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25000v1,https://arxiv.org/pdf/2512.25000v1,arxiv,,"Lifelong person Re-IDentification (L-ReID) exploits sequentially collected data to continuously train and update a ReID model, focusing on the overall performance of all data. Its main challenge is to avoid the catastrophic forgetting problem of old knowledge while training on new data. Existing L-R"
382,,Basic Inequalities for First-Order Optimization with Applications to Statistical Risk Analysis,Seunghoon Paik; Kangjie Zhou; Matus Telgarsky; Ryan J. Tibshirani,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24999v1,https://arxiv.org/pdf/2512.24999v1,arxiv,,"We introduce \textit{basic inequalities} for first-order iterative optimization algorithms, forming a simple and versatile framework that connects implicit and explicit regularization. While related inequalities appear in the literature, we isolate and highlight a specific form and develop it as a w"
383,,Link Prediction on Heterophilic Graphs via Disentangled Representation Learning,Shijie Zhou; Zhimeng Guo; C. Aggarwal; Xiang Zhang; Suhang Wang,2022,arXiv.org,,,,,19,0.000,0.000,10.48550/arXiv.2208.01820,https://www.semanticscholar.org/paper/586bc7191c90f0fb4132946dc22c61a5f6b8d81e,http://arxiv.org/pdf/2208.01820,semantic_scholar,,"Link prediction is an important task that has wide applications in various domains. However, the majority of existing link prediction approaches assume the given graph follows homophily assumption, and designs similarity-based heuristics or representation learning approaches to predict links. Howeve"
384,,Universal Audio Generation,Antoine Laurent; Sameer Khurana; Anthony Larcher; Dominik Klement; Mickaël Rouvier,2026,HAL (Le Centre pour la Communication Scientifique Directe),,,,,0,0.000,0.000,,https://openalex.org/W4414932055,https://hal.science/hal-05110014v1/document,openalex,,This report describe the research done during the third ESPERANTO/JSALT workshop from the 10th June 2024 to the 2nd of August 2024.
385,,Interpretable World Model Imaginations as Deep Reinforcement Learning Explanation,"Wenninghoff, Nils; Schwammberger, Maike",2026,KITopen,,,,,0,0.000,0.000,10.5445/ir/1000186426,https://openalex.org/W7104041609,https://doi.org/10.5445/ir/1000186426,openalex,,
386,,Real-Time Soccer Analytics on the Edge : A Lightweight Framework and Data Standardization,Certification and Safety Society for Standards; Misheel Galbadrakh; Youngim Cho,2025,Society for Standards Certification and Safety,,,,,0,0.000,0.000,10.34139/jscs.2025.15.4.82,https://openalex.org/W7117730078,,openalex,,"Soccer video analysis has advanced rapidly with recent progress in artificial intelligence and computer vision, enabling the automatic extraction of tactical and performance-related information from broadcast footage. Early systems, such as the classical pipeline of Liu et al. (2009), established th"
387,,"Symbolic Expression Processing over Factor-Dense Radix Lattices: Theory, Implementation, and Validation",Edwin Jean-Paul Vening,2025,Zenodo (CERN European Organization for Nuclear Research),,,,,0,0.000,0.000,10.5281/zenodo.18100879,https://openalex.org/W7117687003,https://doi.org/10.5281/zenodo.18100879,openalex,,"Conceptual Contribution and Architectural Insight This work does not propose an incremental optimization of conventional digital computation, nor does it introduce a new arithmetic unit or instruction set. Instead, it demonstrates a symbolic computation framework in which computation emerges from st"
388,,Demand Response Potential Evaluation Based on Multivariate Heterogeneous Features and Stacking Mechanism,Chong Gao; Zhiheng Xu; Ran Cheng; Junxiao Zhang; Xinghang Weng,2025,Energies,,,,,0,0.000,0.000,10.3390/en19010194,https://openalex.org/W7117707127,https://www.mdpi.com/1996-1073/19/1/194/pdf?version=1767091128,openalex,,"Accurate evaluation of demand response (DR) potential at the individual user level is critical for the effective implementation and optimization of demand response programs. However, existing data-driven methods often suffer from insufficient feature representation, limited characterization of load "
389,,"AI, Audio, and Agriulture: Cross-Border Podcasting as a Tool for Digital Pedagogy and Sustainability Communication",Madison A. Dyment; Jamie Loizzo,2025,Canadian Agri-food & Rural Advisory Extension and Education Journal,,,,,0,0.000,0.000,10.21083/caree.v1i1.8963,https://openalex.org/W7117711914,https://journal.lib.uoguelph.ca/index.php/caree/article/download/8963/7907,openalex,,"As digital platforms reshape agri-food systems, podcasts offer an accessible way to share sustainability solutions globally. In agricultural and natural resource education, podcasting aligns with project-based learning, allowing students to develop communication skills through content creation. Guid"
390,,DT-sampler: A SAT-based Decision Tree Ensemble,Xiaotian Xue; Chao Huang; Koji Tsuda; Diptesh Das,2025,ACM SIGKDD Explorations Newsletter,,,,,0,0.000,0.000,10.1145/3787470.3787484,https://openalex.org/W7117933562,,openalex,,"Interpretable (or explainable) machine learning models, such as decision trees, play a crucial role in the context of trustworthy AI. However, finding optimal decision trees (i.e., minimum size and maximum accuracy trees) is not a simple task and remains an active area of research. While a single de"
391,,Improving sign Language recognition system for assisting deaf and dumb people using pathfinder algorithm with representation learning model,Nadhem Nemri; Mohammed Yahya Alzahrani; Wided Bouchelligua; Amani A. Alneil,2025,Scientific Reports,,,,,0,0.000,0.000,10.1038/s41598-025-34283-x,https://openalex.org/W7117449066,https://www.nature.com/articles/s41598-025-34283-x_reference.pdf,openalex,,"For many individuals, communication through sign language (SL) is the primary means of interacting with the world, and the potential applications of effective SL Recognition (SLR) systems are vast and far-reaching. SLR is a research area dedicated to the automatic analysis of hand gestures and other"
392,,A hybrid quantum–classical convolutional neural network with a quantum attention mechanism for skin cancer,Pradyumn Pandey; Shrabanti Mandal,2025,Scientific Reports,,,,,0,0.000,0.000,10.1038/s41598-025-31122-x,https://openalex.org/W7117450241,https://doi.org/10.1038/s41598-025-31122-x,openalex,,"Skin cancer is the most common and fatal illness globally, and therefore, proper early detection is essential for successful treatment and enhanced patient outcomes. Classic deep learning models, especially Convolutional Neural Networks (CNNs), have greatly succeeded in medical image classification."
393,,PFNet: A Phase Fusion Network for Pedestrian Trajectory Prediction,Hui Zhang; Jun Wang; Tang Xiaohan,2025,Intelligenza Artificiale,,,,,0,0.000,0.000,10.1177/17248035251406439,https://openalex.org/W7117486544,,openalex,,"Pedestrian trajectory prediction plays a pivotal role in real-world applications such as autonomous driving, unmanned delivery, and intelligent surveillance. However, existing deep learning approaches still face critical challenges, including mode collapse and the generation of unrealistic trajector"
394,,Multimodal Conflict-Aware and Generative-Enhanced AI for Early Startup Survival and Risk Prediction,Jiaying Xi,2025,,,,,,0,0.000,0.000,10.21203/rs.3.rs-8365925/v1,https://openalex.org/W7117517690,https://www.researchsquare.com/article/rs-8365925/latest.pdf,openalex,,"<title>Abstract</title> Early-stage startups are central to innovation-driven economies, yet their failure rates remain persistently high, with more than half of new ventures not surviving their first three to five years. Accurately assessing the risk of young ventures is challenging because relevan"
395,,Measuring national sustainability: ESG scores from corporate data,Sergio Hoffmann; Rita L. D’Ecclesia,2025,Socio-Economic Planning Sciences,,,,,0,0.000,0.000,10.1016/j.seps.2025.102408,https://openalex.org/W7117569979,https://doi.org/10.1016/j.seps.2025.102408,openalex,,"Environmental, Social, and Governance (ESG) metrics have become central to sustainability assessment, yet the link between national conditions and composite ESG performance remains largely unexplored. We develop a bottom-up national ESG rating by aggregating the distribution of listed firms’ ESG sco"
396,,The Architecture of Fictionality: A Computational Analysis of Narrative Divides,Emrah Peksoy,2025,SÖYLEM Filoloji Dergisi,,,,,0,0.000,0.000,10.29110/soylemdergi.1776640,https://openalex.org/W7117419774,https://dergipark.org.tr/en/download/article-file/5207035,openalex,,"Fictionality, as both a literary construct and a marker of cultural imagination, defines the shifting boundaries between storytelling and documentary representation. This study examines how narrativity and genre features distinguish fiction from nonfiction in contemporary literature. Drawing on narr"
397,,Let Samples Speak: Mitigating Spurious Correlation by Exploiting the Clusterness of Samples,Weiwei Li; Junzhuo Liu; Yuanyuan Ren; Yuchen Zheng; Yahao Liu,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2512.22874,https://openalex.org/W7117766558,https://doi.org/10.48550/arxiv.2512.22874,openalex,,"Deep learning models are known to often learn features that spuriously correlate with the class label during training but are irrelevant to the prediction task. Existing methods typically address this issue by annotating potential spurious attributes, or filtering spurious features based on some emp"
398,,Savant Phenomena and Non-Representational Cognition An Operatoric Approach,Timothy Speed,2025,Zenodo (CERN European Organization for Nuclear Research),,,,,2,0.000,0.000,10.5281/zenodo.18069350,https://openalex.org/W7117435150,https://doi.org/10.5281/zenodo.18069350,openalex,,"Savant phenomena are still regarded in cognitive science and neurological research as anomalies: as isolated island abilities, as exceptions within otherwise deficit-oriented cognitive profiles, or as curiosities that cannot be adequately integrated into established models of intelligence, learning,"
399,,Attention-Driven-Multi-Agent-Optimizer,Shichen Zhang,2025,Zenodo (CERN European Organization for Nuclear Research),,,,,0,0.000,0.000,10.5281/zenodo.18065320,https://openalex.org/W7117411429,https://doi.org/10.5281/zenodo.18065320,openalex,,"# Article **Optimizing Decision-Making Processes Using Deep Reinforcement Learning with Attention Mechanisms and Multi-Agent Systems** ## Description The project titled ""Optimizing Decision-Making Processes Using Deep Reinforcement Learning with Attention Mechanisms and Multi-Agent Systems"" aims to "
400,,Personalized-Marketing-DRL,Xinnan Ji,2025,Zenodo (CERN European Organization for Nuclear Research),,,,,0,0.000,0.000,10.5281/zenodo.18065295,https://openalex.org/W7117412689,https://doi.org/10.5281/zenodo.18065295,openalex,,# Article **Personalized Marketing Strategies for E-Commerce Using Deep Reinforcement Learning with Multi-Modal Data Integration** ## Description The project focuses on developing personalized marketing strategies for e-commerce by leveraging deep reinforcement learning (DRL) integrated with multi-m
401,,Energy-Market-Optimization,Xijun Lin,2025,Zenodo (CERN European Organization for Nuclear Research),,,,,0,0.000,0.000,10.5281/zenodo.18065371,https://openalex.org/W7117419293,https://doi.org/10.5281/zenodo.18065371,openalex,,# Article **Multi-Agent Systems for Energy Market Optimization and Real-Time Power Trading** ## Description This project presents a novel framework utilizing multi-agent systems for optimizing energy markets and facilitating real-time power trading. The approach addresses the complexities of dynamic
402,,"Reinforcement learning for medical image analysis: a systematic review of algorithms, engineering challenges, and clinical deployment",Masuda Begum Sampa; Nor Hidayati Abdul Aziz; Md. Siddikur Rahman; Nor Azlina Ab. Aziz; R. Besar,2025,Computer Assisted Surgery,,,,,0,0.000,0.000,10.1080/24699322.2025.2597553,https://openalex.org/W7117310426,https://doi.org/10.1080/24699322.2025.2597553,openalex,,"Reinforcement learning (RL) has emerged as a powerful artificial intelligence paradigm in medical image analysis, excelling in complex decision-making tasks. This systematic review synthesizes the applications of RL across diverse imaging domains-including landmark detection, image segmentation, les"
403,,High-fidelity 3D mesh generation from a single sketch using shape constraints,Yingbin Wu; Fubo Wang; Peng Zhao; MingQuan ZHOU; Shengling Geng,2025,Scientific Reports,,,,,0,0.000,0.000,10.1038/s41598-025-30843-3,https://openalex.org/W7117316252,https://doi.org/10.1038/s41598-025-30843-3,openalex,,"Abstract The research on 3D model reconstruction from a single image using deep learning technology has achieved remarkable progress. However, compared with images, sketches lack sufficient visual information, which challenges the reconstruction algorithm’s ability to correctly interpret sketches. H"
404,,Impact of Pedestrian and Vehicle Connectivity on Intersection Performance: A High-Fidelity Simulation with Deep Reinforcement Learning Control,Wissam Sleiman; Pedram Beigi; Tibor Petrov; Ľuboš Buzna; Peter Počta,2025,Transportation Research Record Journal of the Transportation Research Board,,,,,0,0.000,0.000,10.1177/03611981251384965,https://openalex.org/W7117354843,,openalex,,"Recent studies on the adoption of deep reinforcement learning (DRL) for traffic signal control (TSC) have demonstrated promising outcomes. However, limited research has explored the partial connectivity of diverse agents for DRL-TSC applications for intersections with the presence of pedestrians. Mo"
405,,Multimodal Vision Language Models in Interactive and Physical Environments,Lucas Rafael Guimarães Pereira; Martina Kovács; Ahmed El-Masry; Feidlimid Shyama,2025,Preprints.org,,,,,0,0.000,0.000,10.20944/preprints202512.2407.v1,https://openalex.org/W7117463132,https://www.preprints.org/frontend/manuscript/0df25ffc752bd01cc34ec6287933dc08/download_pub,openalex,,"Multimodal Large Vision--Language Models (LVLMs) have emerged as a central paradigm in contemporary artificial intelligence, enabling machines to jointly perceive, reason, and communicate across visual and linguistic modalities at unprecedented scale. By integrating advances in large language models"
406,,Double Deep Q-Network-Based Solution for the Dynamic Electric Vehicle Routing Problem,Mehmet Bilge Han Taş; Kemal Özkan; İnci Sarıçiçek; Ahmet Yazıcı,2025,Applied Sciences,,,,,0,0.000,0.000,10.3390/app16010278,https://openalex.org/W7117480706,https://www.mdpi.com/2076-3417/16/1/278/pdf,openalex,,"The Dynamic Electric Vehicle Routing Problem (D-EVRP) presents a framework that requires electric vehicles to meet demand with limited energy capacity. When dynamic demand flows and charging requirements are considered together, traditional methods cannot provide sufficient adaptation for real-time "
407,,HWL-HIN: A Hypergraph-Level Hypergraph Isomorphism Network as Powerful as the Hypergraph Weisfeiler-Lehman Test with Application to Higher-Order Network Robustness,Chengyu Tian; Wenbin Pei,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2512.22014,https://openalex.org/W7117570597,https://doi.org/10.48550/arxiv.2512.22014,openalex,,"Robustness in complex systems is of significant engineering and economic importance. However, conventional attack-based a posteriori robustness assessments incur prohibitive computational overhead. Recently, deep learning methods, such as Convolutional Neural Networks (CNNs) and Graph Neural Network"
408,,DATA VISUALIZATION AS A FORM OF SCULPTURAL ART,Manivannan Karunakaran; Praney Madan; Sachin Pratap Singh; Peeyush Kumar Gupta; Mohd Faisal,2025,ShodhKosh Journal of Visual and Performing Arts,,,,,0,0.000,0.000,10.29121/shodhkosh.v6.i4s.2025.6863,https://openalex.org/W7117421613,https://doi.org/10.29121/shodhkosh.v6.i4s.2025.6863,openalex,,"Data visualization, the interconnection of information and sculptural art is a new paradigm where information moves out of the digital screens and finds space in the physical world to express itself. This paper examines the conceptual, aesthetic and technological systems that allow the data to be ph"
409,,An EEG Dataset for Multimodal Semantic Alignment and Neural Decoding during Reading and Listening,Sitong Chen; B. Li; Cuilin He; D. Li; Mingyan Wu,2025,Scientific Data,,,,,0,0.000,0.000,10.1038/s41597-025-06466-8,https://openalex.org/W7117148056,https://www.nature.com/articles/s41597-025-06466-8_reference.pdf,openalex,,"EEG-based neural decoding requires large-scale benchmark datasets. Paired brain-language data across speaking, listening, and reading modalities are essential for aligning neural activity with the semantic representation of large language models (LLMs). However, such datasets are rare, especially fo"
410,,RESEARCH ON A HYBRID LSTM-CNN-ATTENTION MODEL FOR TEXTBASED WEB CONTENT CLASSIFICATION,M. V. Kuz; I. M. Lazarovych; M. I. Kozlenko; M. V. Pikuliak; A. D. Kvasniuk,2025,Radio Electronics Computer Science Control,,,,,0,0.000,0.000,10.15588/1607-3274-2025-4-10,https://openalex.org/W7117160304,https://ric.zp.edu.ua/article/download/346199/333232,openalex,,"Context. Text-based web content classification plays a pivotal role in various natural language processing (NLP) tasks, including fake news detection, spam filtering, content categorization, and automated moderation. As the scale and complexity of textual data on the web continue to grow, traditiona"
411,,SAE-AM: Enhancing Network Intrusion Detection Using Sparse Autoencoders with Attention Modules,Akanksha Pamena; Manohar Naik Sugali,2025,Applied Cybersecurity & Internet Governance,,,,,0,0.000,0.000,10.60097/acig/213872,https://openalex.org/W7117239784,https://www.acigjournal.com/pdf-213872-133704?filename=SAE-AM--Enhancing-Network.pdf,openalex,,"In response to the relentless evolution of cyber threats that continue to outpace traditional defence mechanisms, this study addresses key limitations in existing Intrusion Detection Systems (IDS), particularly those related to high dimensionality and computational inefficiency. We propose a novel f"
412,,Embodied Outdoors Arts-Based Approaches to Mathematical Understanding,Susan Gerofsky,2025,Encounters in Theory and History of Education,,,,,0,0.000,0.000,10.24908/encounters.v26i0.20253,https://openalex.org/W7117250715,https://doi.org/10.24908/encounters.v26i0.20253,openalex,,"School mathematics instruction remains shaped by pedagogical traditions rooted in nineteenth-century industrial models that privilege static, indoor, and calculation-focused learning. Alternative possibilities for mathematical understanding emerge through embodied, arts-based, and outdoor pedagogies"
413,,Interpretable Feature Interaction via Statistical Self-supervised Learning on Tabular Data,Susan Zhang; Haoyi Xiong,2025,Machine Learning Science and Technology,,,,,0,0.000,0.000,10.1088/2632-2153/ae3104,https://openalex.org/W7117251088,https://doi.org/10.1088/2632-2153/ae3104,openalex,,"Abstract In high-stakes scientific contexts, explainable AI is crucial for deriving meaningful insights from complex tabular data. A formidable challenge is ensuring both rigorous statistical guarantees and clear interpretability in feature extraction. While traditional methods like PCA are limited "
414,,Semantic Refinement with LLMs for Graph Representations,Safal Thapaliya; Zehong Wang; Jiazheng Li; Ziming Li; Yanfang Ye,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2512.21106,https://openalex.org/W7117291900,https://doi.org/10.48550/arxiv.2512.21106,openalex,,"Graph-structured data exhibit substantial heterogeneity in where their predictive signals originate: in some domains, node-level semantics dominate, while in others, structural patterns play a central role. This structure-semantics heterogeneity implies that no graph learning model with a fixed indu"
415,,UniTacHand: Unified Spatio-Tactile Representation for Human to Robotic Hand Skill Transfer,Chi Zhang; Penglin Cai; Haoqi Yuan; Chaoyi Xu; Zongqing Lu,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2512.21233,https://openalex.org/W7117293674,https://doi.org/10.48550/arxiv.2512.21233,openalex,,"Tactile sensing is crucial for robotic hands to achieve human-level dexterous manipulation, especially in scenarios with visual occlusion. However, its application is often hindered by the difficulty of collecting large-scale real-world robotic tactile data. In this study, we propose to collect low-"
416,,Generalization of Diffusion Models Arises with a Balanced Representation Space,Zekai Zhang; Xiao Li; Xiang Li; Lianghe Shi; Meng  Shan Wu,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2512.20963,https://openalex.org/W7117305786,https://doi.org/10.48550/arxiv.2512.20963,openalex,,"Diffusion models excel at generating high-quality, diverse samples, yet they risk memorizing training data when overfit to the training objective. We analyze the distinctions between memorization and generalization in diffusion models through the lens of representation learning. By investigating a t"
417,,ДОСЛІДЖЕННЯ ГІБРИДНОЇ МОДЕЛІ LSTM-CNN-ATTENTION ДЛЯ КЛАСИФІКАЦІЇ ВЕБ-КОНТЕНТУ НА ОСНОВІ ТЕКСТУ,М. В. Кузь; І. М. Лазарович; М. І. Козленко; М. В. Пікуляк; А. Д. Кваснюк,2025,Scientific periodicals of Ukraine,,,,,0,0.000,0.000,,https://openalex.org/W7117340432,https://ric.zp.edu.ua/article/view/346199,openalex,,"Context. Text-based web content classification plays a pivotal role in various natural language processing (NLP) tasks, including fake news detection, spam filtering, content categorization, and automated moderation. As the scale and complexity of textual data on the web continue to grow, traditiona"
418,,Multimodal machine learning for 5-year mortality prediction after percutaneous coronary intervention,Byeolhee Kim; Jungyo Suh; Y.-H. Kim; Jung-Min Ahn; Tae Joon Jun,2025,Scientific Reports,,,,,0,0.000,0.000,10.1038/s41598-025-32734-z,https://openalex.org/W7116876845,https://doi.org/10.1038/s41598-025-32734-z,openalex,,"Percutaneous coronary intervention (PCI) is a cornerstone treatment for coronary artery disease, yet accurate prediction of long-term mortality remains a critical challenge due to the complex interplay of risk factors. Existing prognostic models rely predominantly on structured clinical data, overlo"
419,,A Hierarchical Predictive-Adaptive Control Framework for State-of-Charge Balancing in Mini-Grids Using Deep Reinforcement Learning,Iacovos Ioannou; Saher Javaid; Yasuo TAN; Vasos Vassiliou,2025,Electronics,,,,,0,0.000,0.000,10.3390/electronics15010061,https://openalex.org/W7117122392,https://doi.org/10.3390/electronics15010061,openalex,,"State-of-charge (SoC) balancing across multiple battery energy storage systems (BESS) is a central challenge in renewable-rich mini-grids. Heterogeneous battery capacities, differing states of health, stochastic renewable generation, and variable loads create a high-dimensional uncertain control pro"
420,,A Matrix-Statistics-Aware Attention Mechanism for Robust RUL Estimation in Aero-Engines,Ayşenur Hatipoğlu; Ersen Yılmaz,2025,Applied Sciences,,,,,0,0.000,0.000,10.3390/app16010169,https://openalex.org/W7117156470,https://doi.org/10.3390/app16010169,openalex,,"Prognostics and Health Management (PHM) is a vital approach which aims to predict the failure of engineering systems at an early stage and optimize maintenance strategies. It operates through continuous system monitoring, anomaly detection, fault detection, and Remaining Useful Life (RUL) estimation"
421,,LoLA: Long Horizon Latent Action Learning for General Robot Manipulation,Xiaofan Wang; Xingyu Gao; Jianlong Fu; LI Zuolei; Dean Fortier,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2512.20166,https://openalex.org/W7117241752,https://doi.org/10.48550/arxiv.2512.20166,openalex,,"The capability of performing long-horizon, language-guided robotic manipulation tasks critically relies on leveraging historical information and generating coherent action sequences. However, such capabilities are often overlooked by existing Vision-Language-Action (VLA) models. To solve this challe"
422,,Asynchronous Fast-Slow Vision-Language-Action Policies for Whole-Body Robotic Manipulation,Teqiang Zou; Hongliang Zeng; Yuxuan Nong; Yifan Li; Kehui Liu,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2512.20188,https://openalex.org/W7117242111,https://doi.org/10.48550/arxiv.2512.20188,openalex,,"Most Vision-Language-Action (VLA) systems integrate a Vision-Language Model (VLM) for semantic reasoning with an action expert generating continuous action signals, yet both typically run at a single unified frequency. As a result, policy performance is constrained by the low inference speed of larg"
423,,Numerical study of boson mixtures with multi-component continuous matrix product states,Wei Tang; Benoît Tuybens; Jutho Haegeman,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24998v1,https://arxiv.org/pdf/2512.24998v1,arxiv,,"The continuous matrix product state (cMPS) ansatz is a promising numerical tool for studying quantum many-body systems in continuous space. Although it provides a clean framework that allows one to directly simulate continuous systems, the optimization of cMPS is known to be a very challenging task,"
424,,Classifying long legal documents using short random chunks,Luis Adrián Cabrera-Diego,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24997v1,https://arxiv.org/pdf/2512.24997v1,arxiv,,"Classifying legal documents is a challenge, besides their specialized vocabulary, sometimes they can be very long. This means that feeding full documents to a Transformers-based models for classification might be impossible, expensive or slow. Thus, we present a legal document classifier based on De"
425,,Noise resilient real-time phase imaging via undetected light,Josué R. León-Torres; Patrick Hendra; Yugant Mukeshbhai Hadiyal; Christopher Spiess; Fabian Steinlechner,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24993v1,https://arxiv.org/pdf/2512.24993v1,arxiv,,Quantum imaging with undetected light has recently emerged as a technique in which quantum correlations and nonlinear interferometry are combined to decouple illumination and detection paths. This approach has been more recently extended and combined with digital phase-shifting holography and off-ax
426,,Efficiently Estimating Data Efficiency for Language Model Fine-tuning,Gyung Hyun Je; Colin Raffel,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24991v1,https://arxiv.org/pdf/2512.24991v1,arxiv,,"While large language models (LLMs) demonstrate reasonable zero-shot capability across many downstream tasks, fine-tuning is a common practice to improve their performance. However, a task's data efficiency--i.e., the number of fine-tuning examples needed to achieve a desired level of performance--is"
427,,Best Practices for Modelling Electrides,Lee A. Burton,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24989v1,https://arxiv.org/pdf/2512.24989v1,arxiv,,"Materials in which electrons occupy interstitial sites as anions are called electrides and exhibit unusual dimensionality-dependent electronic behavior. These properties make electrides attractive for catalysis, transparent conductors, and emergent quantum phenomena, yet their theoretical treatment "
428,,"Wall crossing, string networks and quantum toroidal algebras",Yegor Zenkevich,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24988v1,https://arxiv.org/pdf/2512.24988v1,arxiv,,"We investigate BPS states in 4d N=4 supersymmetric Yang-Mills theory and the corresponding (p, q) string networks in Type IIB string theory. We propose a new interpretation of the algebra of line operators in this theory as a tensor product of vector representations of a quantum toroidal algebra, wh"
429,,PhysTalk: Language-driven Real-time Physics in 3D Gaussian Scenes,Luca Collorone; Mert Kiray; Indro Spinelli; Fabio Galasso; Benjamin Busam,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24986v1,https://arxiv.org/pdf/2512.24986v1,arxiv,,"Realistic visual simulations are omnipresent, yet their creation requires computing time, rendering, and expert animation knowledge. Open-vocabulary visual effects generation from text inputs emerges as a promising solution that can unlock immense creative potential. However, current pipelines lack "
430,,DarkEQA: Benchmarking Vision-Language Models for Embodied Question Answering in Low-Light Indoor Environments,Yohan Park; Hyunwoo Ha; Wonjun Jo; Tae-Hyun Oh,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24985v1,https://arxiv.org/pdf/2512.24985v1,arxiv,,"Vision Language Models (VLMs) are increasingly adopted as central reasoning modules for embodied agents. Existing benchmarks evaluate their capabilities under ideal, well-lit conditions, yet robust 24/7 operation demands performance under a wide range of visual degradations, including low-light cond"
431,,Optical Spiking Neural Networks via Rogue-Wave Statistics,Bahadır Utku Kesgin; Gülsüm Yaren Durdu; Uğur Teğin,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24983v1,https://arxiv.org/pdf/2512.24983v1,arxiv,,"Optical computing could reduce the energy cost of artificial intelligence by leveraging the parallelism and propagation speed of light. However, implementing nonlinear activation, essential for machine learning, remains challenging in low-power optical systems dominated by linear wave physics. Here,"
432,,Lindbladian PT phase transitions,Yuma Nakanishi; Tomohiro Sasamoto,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24981v1,https://arxiv.org/pdf/2512.24981v1,arxiv,,"A parity-time (PT) transition is a spectral transition characteristic of non-Hermitian generators; it typically occurs at an exceptional point, where multiple eigenvectors coalesce. The concept of a PT transition has been extended to Markovian open quantum systems, which are described by the GKSL eq"
433,,A Modal Logic for Possibilistic Reasoning with Fuzzy Formal Contexts,Prosenjit Howlader; Churn-Jung Liau,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24980v1,https://arxiv.org/pdf/2512.24980v1,arxiv,,We introduce a two-sort weighted modal logic for possibilistic reasoning with fuzzy formal contexts. The syntax of the logic includes two types of weighted modal operators corresponding to classical necessity ($\Box$) and sufficiency ($\boxminus$) modalities and its formulas are interpreted in fuzzy
434,,SymSeqBench: a unified framework for the generation and analysis of rule-based symbolic sequences and datasets,Barna Zajzon; Younes Bouhadjar; Maxime Fabre; Felix Schmidt; Noah Ostendorf,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24977v1,https://arxiv.org/pdf/2512.24977v1,arxiv,,"Sequential structure is a key feature of multiple domains of natural cognition and behavior, such as language, movement and decision-making. Likewise, it is also a central property of tasks to which we would like to apply artificial intelligence. It is therefore of great importance to develop framew"
435,,Attribution-Guided Distillation of Matryoshka Sparse Autoencoders,Cristina P. Martin-Linares; Jonathan P. Ling,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24975v1,https://arxiv.org/pdf/2512.24975v1,arxiv,,"Sparse autoencoders (SAEs) aim to disentangle model activations into monosemantic, human-interpretable features. In practice, learned features are often redundant and vary across training runs and sparsity levels, which makes interpretations difficult to transfer and reuse. We introduce Distilled Ma"
436,,Hierarchical Deformation Planning and Neural Tracking for DLOs in Constrained Environments,Yunxi Tang; Tianqi Yang; Jing Huang; Xiangyu Chu; Kwok Wai Samuel Au,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24974v1,https://arxiv.org/pdf/2512.24974v1,arxiv,,"Deformable linear objects (DLOs) manipulation presents significant challenges due to DLOs' inherent high-dimensional state space and complex deformation dynamics. The wide-populated obstacles in realistic workspaces further complicate DLO manipulation, necessitating efficient deformation planning an"
437,,From Complex-Analytic Models to Sparse Domination: A Dyadic Approach of Hypersingular Operators via Bourgain's Interpolation Method,Bingyang Hu; Xiaojing Zhou,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24972v1,https://arxiv.org/pdf/2512.24972v1,arxiv,,"Motivated by the work of Cheng--Fang--Wang--Yu on the hypersingular Bergman projection, we develop a real-variable and dyadic framework for hypersingular operators in regimes where strong-type estimates fail at the critical line. The main new input is a hypersingular sparse domination principle comb"
438,,Evaluating the Impact of Compression Techniques on the Robustness of CNNs under Natural Corruptions,Itallo Patrick Castro Alves Da Silva; Emanuel Adler Medeiros Pereira; Erick de Andrade Barboza; Baldoino Fonseca dos Santos Neto; Marcio de Medeiros Ribeiro,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24971v1,https://arxiv.org/pdf/2512.24971v1,arxiv,,"Compressed deep learning models are crucial for deploying computer vision systems on resource-constrained devices. However, model compression may affect robustness, especially under natural corruption. Therefore, it is important to consider robustness evaluation while validating computer vision syst"
439,,Large language models and the entropy of English,Colin Scheibner; Lindsay M. Smith; William Bialek,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24969v1,https://arxiv.org/pdf/2512.24969v1,arxiv,,"We use large language models (LLMs) to uncover long-ranged structure in English texts from a variety of sources. The conditional entropy or code length in many cases continues to decrease with context length at least to $N\sim 10^4$ characters, implying that there are direct dependencies or interact"
440,,The Impact of LLMs on Online News Consumption and Production,Hangcheng Zhao; Ron Berman,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24968v1,https://arxiv.org/pdf/2512.24968v1,arxiv,,Large language models (LLMs) change how consumers acquire information online; their bots also crawl news publishers' websites for training data and to answer consumer queries; and they provide tools that can lower the cost of content creation. These changes lead to predictions of adverse impact on n
441,,Cosmic Himalayas in CROCODILE : Probing the Extreme Quasar Overdensities by Count-in-Cells analysis and Nearest Neighbor Distribution,Yuto Kuwayama; Yongming Liang; Kentaro Nagamine; Yuri Oku; Daisuke Nishihama,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24966v1,https://arxiv.org/pdf/2512.24966v1,arxiv,,"The recently reported Cosmic Himalayas (CH) -- an extreme quasar overdensity at z~2 -- poses an apparent challenge to the Lambda CDM framework, with a reported significance of 16.9-sigma under Gaussian assumptions. Such an event appears improbably rare, with a formal probability of P ~ 10^-68. In th"
442,,ShowUI-$π$: Flow-based Generative Models as GUI Dexterous Hands,Siyuan Hu; Kevin Qinghong Lin; Mike Zheng Shou,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24965v1,https://arxiv.org/pdf/2512.24965v1,arxiv,,"Building intelligent agents capable of dexterous manipulation is essential for achieving human-like automation in both robotics and digital environments. However, existing GUI agents rely on discrete click predictions (x,y), which prohibits free-form, closed-loop trajectories (e.g. dragging a progre"
443,,Fundamental Limits for Near-Field Sensing -- Part II: Wide-Band Systems,Tong Wei; Kumar Vijay Mishra; Bhavani Shankar M. R.; Björn Ottersten,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24962v1,https://arxiv.org/pdf/2512.24962v1,arxiv,,"Near-field sensing with extremely large-scale antenna arrays (ELAAs) in practical 6G systems is expected to operate over broad bandwidths, where delay, Doppler, and spatial effects become tightly coupled across frequency. The purpose of this and the companion paper (Part I) is to develop the unified"
444,,From Principles to Effective Models: A Constructive Framework for Effective Covariant Actions with a Unique Vacuum Solution,Kristina Giesel; Hongguang Liu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24960v1,https://arxiv.org/pdf/2512.24960v1,arxiv,,"The absence of Birkhoff's theorem in effective quantum gravity models leads to a fundamental ambiguity in the vacuum sector, where a priori no unique vacuum solution exists. As a result, phenomenological investigations of the physical implications of these models have been made more difficult. We ad"
445,,Semi-overlapping Multi-bandit Best Arm Identification for Sequential Support Network Learning,András Antos; András Millinghoffer; Péter Antal,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24959v1,https://arxiv.org/pdf/2512.24959v1,arxiv,,"Many modern AI and ML problems require evaluating partners' contributions through shared yet asymmetric, computationally intensive processes and the simultaneous selection of the most beneficial candidates. Sequential approaches to these problems can be unified under a new framework, Sequential Supp"
446,,Fundamental Limits for Near-Field Sensing -- Part I: Narrow-Band Systems,Tong Wei; Kumar Vijay Mishra; Bhavani Shankar M. R.; Björn Ottersten,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24958v1,https://arxiv.org/pdf/2512.24958v1,arxiv,,"Extremely large-scale antenna arrays (ELAAs) envisioned for 6G enable high-resolution sensing. However, the ELAAs worked in extremely high frequency will push operation into the near-field region, where spherical wavefronts invalidate classical far-field models and alter fundamental estimation limit"
447,,AMAP Agentic Planning Technical Report,Yulan Hu; Xiangwen Zhang; Sheng Ouyang; Hao Yi; Lu Xu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24957v1,https://arxiv.org/pdf/2512.24957v1,arxiv,,"We present STAgent, an agentic large language model tailored for spatio-temporal understanding, designed to solve complex tasks such as constrained point-of-interest discovery and itinerary planning. STAgent is a specialized model capable of interacting with ten distinct tools within spatio-temporal"
448,,MSACL: Multi-Step Actor-Critic Learning with Lyapunov Certificates for Exponentially Stabilizing Control,Yongwei Zhang; Yuanzhe Xing; Quan Quan; Zhikun She,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24955v1,https://arxiv.org/pdf/2512.24955v1,arxiv,,"Achieving provable stability in model-free reinforcement learning (RL) remains a challenge, particularly in balancing exploration with rigorous safety. This article introduces MSACL, a framework that integrates exponential stability theory with maximum entropy RL through multi-step Lyapunov certific"
449,,VIPER: Process-aware Evaluation for Generative Video Reasoning,Yifan Li; Yukai Gu; Yingqian Min; Zikang Liu; Yifan Du,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24952v1,https://arxiv.org/pdf/2512.24952v1,arxiv,,"Recent breakthroughs in video generation have demonstrated an emerging capability termed Chain-of-Frames (CoF) reasoning, where models resolve complex tasks through the generation of continuous frames. While these models show promise for Generative Video Reasoning (GVR), existing evaluation framewor"
450,,Laser intracavity absorption magnetometry for optical quantum sensing,J. M. Wollenberg; F. Perona; A. Palaci; H. Wenzel; H. Christopher,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24951v1,https://arxiv.org/pdf/2512.24951v1,arxiv,,"Intracavity absorption spectroscopy (ICAS) is a well-established technique for detecting weak absorption signals with ultrahigh sensitivity. Here, we extend this concept to magnetometry using nitrogen-vacancy (NV) centers in diamond. We introduce laser intracavity absorption magnetometry (LICAM), a "
451,,ProDM: Synthetic Reality-driven Property-aware Progressive Diffusion Model for Coronary Calcium Motion Correction in Non-gated Chest CT,Xinran Gong; Gorkem Durak; Halil Ertugrul Aktas; Vedat Cicek; Jinkui Hao,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24948v1,https://arxiv.org/pdf/2512.24948v1,arxiv,,"Coronary artery calcium (CAC) scoring from chest CT is a well-established tool to stratify and refine clinical cardiovascular disease risk estimation. CAC quantification relies on the accurate delineation of calcified lesions, but is oftentimes affected by artifacts introduced by cardiac and respira"
452,,CPJ: Explainable Agricultural Pest Diagnosis via Caption-Prompt-Judge with LLM-Judged Refinement,Wentao Zhang; Tao Fang; Lina Lu; Lifei Wang; Weihe Zhong,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24947v1,https://arxiv.org/pdf/2512.24947v1,arxiv,,"Accurate and interpretable crop disease diagnosis is essential for agricultural decision-making, yet existing methods often rely on costly supervised fine-tuning and perform poorly under domain shifts. We propose Caption--Prompt--Judge (CPJ), a training-free few-shot framework that enhances Agri-Pes"
453,,HaineiFRDM: Explore Diffusion to Restore Defects in Fast-Movement Films,Rongji Xun; Junjie Yuan; Zhongjie Wang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24946v1,https://arxiv.org/pdf/2512.24946v1,arxiv,,"Existing open-source film restoration methods show limited performance compared to commercial methods due to training with low-quality synthetic data and employing noisy optical flows. In addition, high-resolution films have not been explored by the open-source methods.We propose HaineiFRDM(Film Res"
454,,Dynamic response phenotypes and model discrimination in systems and synthetic biology,Eduardo D. Sontag,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24945v1,https://arxiv.org/pdf/2512.24945v1,arxiv,,"Biological systems encode function not primarily in steady states, but in the structure of transient responses elicited by time-varying stimuli. Overshoots, biphasic dynamics, adaptation kinetics, fold-change detection, entrainment, and cumulative exposure effects often determine phenotypic outcomes"
455,,Interaction of a Vortex Pair with a Polymeric Fluid Layer,Rabia Sonmez; Robert A. Handler; David B. Goldstein; Anton Burstev; Ryan Kelly,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24944v1,https://arxiv.org/pdf/2512.24944v1,arxiv,,"The interaction of vortical structures with boundaries has been extensively studied in Newtonian fluids, where conditions such as no slip walls, free surfaces, or contaminated surfaces dictate whether vortices rebound, dissipate, or generate secondary structures. In this work, we investigate a relat"
456,,RAIR: A Rule-Aware Benchmark Uniting Challenging Long-Tail and Visual Salience Subset for E-commerce Relevance Assessment,Chenji Lu; Zhuo Chen; Hui Zhao; Zhenyi Wang; Pengjie Wang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24943v1,https://arxiv.org/pdf/2512.24943v1,arxiv,,"Search relevance plays a central role in web e-commerce. While large language models (LLMs) have shown significant results on relevance task, existing benchmarks lack sufficient complexity for comprehensive model assessment, resulting in an absence of standardized relevance evaluation metrics across"
457,,Iterative Deployment Improves Planning Skills in LLMs,Augusto B. Corrêa; Yoav Gelberg; Luckeciano C. Melo; Ilia Shumailov; André G. Pereira,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24940v1,https://arxiv.org/pdf/2512.24940v1,arxiv,,"We show that iterative deployment of large language models (LLMs), each fine-tuned on data carefully curated by users from the previous models' deployment, can significantly change the properties of the resultant models. By testing this mechanism on various planning domains, we observe substantial i"
458,,"Vibe Coding, Interface Flattening",Hongrui Jin,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24939v1,https://arxiv.org/pdf/2512.24939v1,arxiv,,"Large language models are reshaping programming by enabling 'vibe coding': the development of softwares through natural-language interaction with model-driven toolchains. This article argues that vibe coding is best understood as interface flattening, a reconfiguration in which previously distinct m"
459,,Modelling the movements of organisms by stochastic theory in a comoving frame,Norberto Lucero Azuara; Rainer Klages,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24937v1,https://arxiv.org/pdf/2512.24937v1,arxiv,,Imagine you walk in a plane. You move by making a step of a certain length per time interval in a chosen direction. Repeating this process by randomly sampling step length and turning angle defines a two-dimensional random walk in what we call comoving frame coordinates. This is precisely how Ross a
460,,Searching for Periodicity in FRB 20240114A,J. I. Katz,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24936v1,https://arxiv.org/pdf/2512.24936v1,arxiv,,"FRB 20240114A is extraordinarily active, and therefore presents an opportunity to search for the periodicity predicted by magnetar models of Fast Radio Bursts (FRB). Zhang, et al. (2025) observed 11,553 bursts, including 3196 on MJD 60381 (March 12, 2024). We find no significant peak in the periodog"
461,,Fair Committee Selection under Ordinal Preferences and Limited Cardinal Information,Ameet Gadekar; Aristides Gionis; Suhas Thejaswi; Sijing Tu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24934v1,https://arxiv.org/pdf/2512.24934v1,arxiv,,"We study the problem of fair $k$-committee selection under an egalitarian objective. Given $n$ agents partitioned into $m$ groups (\eg, demographic quotas), the goal is to aggregate their preferences to form a committee of size $k$ that guarantees minimum representation from each group while minimiz"
462,,Adaptive Dependency-aware Prompt Optimization Framework for Multi-Step LLM Pipeline,Minjun Zhao; Xinyu Zhang; Shuai Zhang; Deyang Li; Ruifeng Shi,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24933v1,https://arxiv.org/pdf/2512.24933v1,arxiv,,"Multi-step LLM pipelines invoke large language models multiple times in a structured sequence and can effectively solve complex tasks, but their performance heavily depends on the prompts used at each step. Jointly optimizing these prompts is difficult due to missing step-level supervision and inter"
463,,Constraints on the perfect phylogeny mixture model and their effect on reducing degeneracy,John Marangola; Azadeh Sheikholeslami; José Bento,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24930v1,https://arxiv.org/pdf/2512.24930v1,arxiv,,"The perfect phylogeny mixture (PPM) model is useful due to its simplicity and applicability in scenarios where mutations can be assumed to accumulate monotonically over time. It is the underlying model in many tools that have been used, for example, to infer phylogenetic trees for tumor evolution an"
464,,Are First-Order Diffusion Samplers Really Slower? A Fast Forward-Value Approach,Yuchen Jiao; Na Li; Changxiao Cai; Gen Li,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24927v1,https://arxiv.org/pdf/2512.24927v1,arxiv,,"Higher-order ODE solvers have become a standard tool for accelerating diffusion probabilistic model (DPM) sampling, motivating the widespread view that first-order methods are inherently slower and that increasing discretization order is the primary path to faster generation. This paper challenges t"
465,,Towards Provably Secure Generative AI: Reliable Consensus Sampling,Yu Cui; Hang Fu; Sicheng Pan; Zhuoyu Sun; Yifei Liu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24925v1,https://arxiv.org/pdf/2512.24925v1,arxiv,,Existing research on generative AI security is primarily driven by mutually reinforcing attack and defense methodologies grounded in empirical experience. This dynamic frequently gives rise to previously unknown attacks that can circumvent current detection and prevention. This necessitates the cont
466,,Semi-Supervised Diversity-Aware Domain Adaptation for 3D Object detection,Bartłomiej Olber; Jakub Winter; Paweł Wawrzyński; Andrii Gamalii; Daniel Górniak,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24922v1,https://arxiv.org/pdf/2512.24922v1,arxiv,,"3D object detectors are fundamental components of perception systems in autonomous vehicles. While these detectors achieve remarkable performance on standard autonomous driving benchmarks, they often struggle to generalize across different domains - for instance, a model trained in the U.S. may perf"
467,,"No Vision, No Wearables: 5G-based 2D Human Pose Recognition with Integrated Sensing and Communications",Haojin Li; Dongzhe Li; Anbang Zhang; Wenqi Zhang; Chen Sun,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24923v1,https://arxiv.org/pdf/2512.24923v1,arxiv,,"With the increasing maturity of contactless human pose recognition (HPR) technology, indoor interactive applications have raised higher demands for natural, controller-free interaction methods. However, current mainstream HPR solutions relying on vision or radio-frequency (RF) (including WiFi, radar"
468,,Valence quark distribution of the pion inside a medium with finite baryon density: A Nambu--Jona-Lasinio model approach,Ashutosh Dwibedi; Satyajit Puhan; Sabyasachi Ghosh; Harleen Dahiya,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24921v1,https://arxiv.org/pdf/2512.24921v1,arxiv,,We calculate the in-medium valence quark distribution of the pion immersed in a finite baryon density using the light-cone quark model. The medium-modified pion properties are obtained by using the constituent quark mass-dependent light cone wave functions. To obtain the constituent quark masses at 
469,,Cosmological dynamics and observational constraints of an interacting early scalar field coupled to radiation,Dorian Araya; Felipe Herrera; Nelson Videla,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24918v1,https://arxiv.org/pdf/2512.24918v1,arxiv,,"We study the cosmic evolution of an interacting scalar field radiation model, in which a minimally coupled scalar field exchanges energy with the radiation sector through an exponential coupling. Extending previous formulations, a non-relativistic matter component is included explicitly, which allow"
470,,Frequent subgraph-based persistent homology for graph classification,Xinyang Chen; Amaël Broustet; Guoting Chen,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24917v1,https://arxiv.org/pdf/2512.24917v1,arxiv,,"Persistent homology (PH) has recently emerged as a powerful tool for extracting topological features. Integrating PH into machine learning and deep learning models enhances topology awareness and interpretability. However, most PH methods on graphs rely on a limited set of filtrations, such as degre"
471,,"Existence, uniqueness, and approximability of solutions to the classical Melan equation in suspension bridges",Jinxiang Wang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24915v1,https://arxiv.org/pdf/2512.24915v1,arxiv,,"The classical Melan equation modeling suspension bridges is considered. We first study the explicit expression and the uniform positivity of the analytical solution for the simplified ``less stiff'' model, based on which we develop a monotone iterative technique of lower and upper solutions to inves"
472,,AI-Driven Cloud Resource Optimization for Multi-Cluster Environments,Vinoth Punniyamoorthy; Akash Kumar Agarwal; Bikesh Kumar; Abhirup Mazumder; Kabilan Kannan,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24914v1,https://arxiv.org/pdf/2512.24914v1,arxiv,,"Modern cloud-native systems increasingly rely on multi-cluster deployments to support scalability, resilience, and geographic distribution. However, existing resource management approaches remain largely reactive and cluster-centric, limiting their ability to optimize system-wide behavior under dyna"
473,,SPIRAL: Self-supervised Perturbation-Invariant Representation Learning for Speech Pre-Training,WenYong Huang; Zhenhe Zhang; Y. Yeung; Xin Jiang; Qun Liu,2022,International Conference on Learning Representations,,,,,27,0.000,0.000,,https://www.semanticscholar.org/paper/0228d04512e04306ed5971117a4e07d11df458b8,,semantic_scholar,,"We introduce a new approach for speech pre-training named SPIRAL which works by learning denoising representation of perturbed data in a teacher-student framework. Specifically, given a speech utterance, we first feed the utterance to a teacher network to obtain corresponding representation. Then th"
474,,ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning,Chih-Yao Chen; Cheng-Te Li,2021,North American Chapter of the Association for Computational Linguistics,,,,,115,0.000,0.000,10.18653/V1/2021.NAACL-MAIN.272,https://www.semanticscholar.org/paper/93df9dc530b1cf0af6d5eef90d017741a2aab5d8,https://aclanthology.org/2021.naacl-main.272.pdf,semantic_scholar,,"While relation extraction is an essential task in knowledge acquisition and representation, and new-generated relations are common in the real world, less effort is made to predict unseen relations that cannot be observed at the training stage. In this paper, we formulate the zero-shot relation extr"
475,,A Survey on Protein Representation Learning: Retrospect and Prospect,Lirong Wu; Yu-Feng Huang; H. Lin; Stan Z. Li,2022,arXiv.org,,,,,14,0.000,0.000,10.48550/arXiv.2301.00813,https://www.semanticscholar.org/paper/72640b25e67413f5247efc4baa4200539b836b81,http://arxiv.org/pdf/2301.00813,semantic_scholar,,"Proteins are fundamental biological entities that play a key role in life activities. The amino acid sequences of proteins can be folded into stable 3D structures in the real physicochemical world, forming a special kind of sequence-structure data. With the development of Artificial Intelligence (AI"
476,,GTC: GNN-Transformer Co-contrastive Learning for Self-supervised Heterogeneous Graph Representation,Yundong Sun; Dongjie Zhu; Yansong Wang; Zhaoshuo Tian,2024,Neural Networks,,,,,37,0.000,0.000,10.48550/arXiv.2403.15520,https://www.semanticscholar.org/paper/9dabc93f744c4c42841ca2ca4e3b4270cebf8c92,,semantic_scholar,,"Graph Neural Networks (GNNs) have emerged as the most powerful weapon for various graph tasks due to the message-passing mechanism's great local information aggregation ability. However, over-smoothing has always hindered GNNs from going deeper and capturing multi-hop neighbors. Meanwhile, most meth"
477,,FedDAR: Federated Domain-Aware Representation Learning,Aoxiao Zhong; Hao He; Zhaolin Ren; N. Li; Quanzheng Li,2022,International Conference on Learning Representations,,,,,13,0.000,0.000,10.48550/arXiv.2209.04007,https://www.semanticscholar.org/paper/25f74e75e5dd41875eb2404b550b99bb91866ee7,http://arxiv.org/pdf/2209.04007,semantic_scholar,,"Cross-silo Federated learning (FL) has become a promising tool in machine learning applications for healthcare. It allows hospitals/institutions to train models with sufficient data while the data is kept private. To make sure the FL model is robust when facing heterogeneous data among FL clients, m"
478,,Adaptive Spatiotemporal Representation Learning for Skeleton-Based Human Action Recognition,Jiahui Yu; Hongwei Gao; Yongquan Chen; Dalin Zhou; Jinguo Liu,2022,IEEE Transactions on Cognitive and Developmental Systems,,,,,22,0.000,0.000,10.1109/TCDS.2021.3131253,https://www.semanticscholar.org/paper/4f7776c01379a56d43a5f75b72c9fb2a63320a69,,semantic_scholar,,"How do humans recognize an action or an interaction in the real world? Due to the diversity of viewing perspectives, it is a challenge for humans to identify a regular activity when they observe it from an uncommon perspective. We argue that discriminative spatiotemporal information remains an essen"
479,,Fair Graph Representation Learning with Imbalanced and Biased Data,Yu Wang,2022,Web Search and Data Mining,,,,,16,0.000,0.000,10.1145/3488560.3502218,https://www.semanticscholar.org/paper/66c2ebc655e9cb4e2be96a8ed871a3d061502331,,semantic_scholar,,
480,,Is Visual Context Really Helpful for Knowledge Graph? A Representation Learning Perspective,Meng Wang; Sen Wang; Han Yang; Zheng Zhang; Xi Chen,2021,ACM Multimedia,,,,,112,0.000,0.000,10.1145/3474085.3475470,https://www.semanticscholar.org/paper/1007b5a8d0af5b39d061eb0ac45a0700fe47bd1e,,semantic_scholar,,"Visual modality recently has aroused extensive attention in the fields of knowledge graph and multimedia because a lot of real-world knowledge is multi-modal in nature. However, it is currently unclear to what extent the visual modality can improve the performance of knowledge graph tasks over unimo"
481,,FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning,Indro Spinelli; Simone Scardapane; Amir Hussain; A. Uncini,2021,IEEE Transactions on Artificial Intelligence,,,,,106,0.000,0.000,10.1109/TAI.2021.3133818,https://www.semanticscholar.org/paper/15a4a8606caaae63a33bb617c5180cfadf598ebf,https://arxiv.org/pdf/2104.14210,semantic_scholar,,"Graph representation learning has become a ubiquitous component in many scenarios, ranging from social network analysis to energy forecasting in smart grids. In several applications, ensuring the fairness of the node (or graph) representations with respect to some protected attributes is crucial for"
482,,Retrieval-based Disentangled Representation Learning with Natural Language Supervision,Jiawei Zhou; Xiaoguang Li; Lifeng Shang; Xin Jiang; Qun Liu,2022,International Conference on Learning Representations,,,,,9,0.000,0.000,,https://www.semanticscholar.org/paper/7ead3dc147d7b6ff6abc3e92048b800701f6224e,,semantic_scholar,,"Disentangled representation learning remains challenging as the underlying factors of variation in the data do not naturally exist. The inherent complexity of real-world data makes it unfeasible to exhaustively enumerate and encapsulate all its variations within a finite set of factors. However, it "
483,,Dynamic and Static Representation Learning Network for Recommendation,Tongcun Liu; Siyuan Lou; Jianxin Liao; Hailin Feng,2022,IEEE Transactions on Neural Networks and Learning Systems,,,,,11,0.000,0.000,10.1109/TNNLS.2022.3177611,https://www.semanticscholar.org/paper/8d23ae8c2f1c42fb515195d1391330d235060e88,,semantic_scholar,,"Existing review-based recommendation methods learn a latent representation of user and item from user-generated reviews by a static strategy, which are unable to capture the dynamic evolution of users’ interests and the dynamic attraction of items. Here, we propose a dynamic and static representatio"
484,,Multi-View Information-Bottleneck Representation Learning,Zhibin Wan; Changqing Zhang; Peng Fei Zhu; Q. Hu,2021,AAAI Conference on Artificial Intelligence,,,,,104,0.000,0.000,10.1609/aaai.v35i11.17210,https://www.semanticscholar.org/paper/f7c86725504c7864dd9caef2ae1946b7e49a1e5b,https://ojs.aaai.org/index.php/AAAI/article/download/17210/17017,semantic_scholar,,"In real-world applications, clustering or classification can usually be improved by fusing information from different views. Therefore, unsupervised representation learning on multi-view data becomes a compelling topic in machine learning. In this paper, we propose a novel and flexible unsupervised "
485,,Embodied Object Representation Learning and Recognition,Toon Van de Maele; Tim Verbelen; Ozan Çatal; B. Dhoedt,2022,Frontiers in Neurorobotics,,,,,12,0.000,0.000,10.3389/fnbot.2022.840658,https://www.semanticscholar.org/paper/a8f929c88dbb8f387443af18b51baf642efa8b66,https://www.frontiersin.org/articles/10.3389/fnbot.2022.840658/pdf,semantic_scholar,,"Scene understanding and decomposition is a crucial challenge for intelligent systems, whether it is for object manipulation, navigation, or any other task. Although current machine and deep learning approaches for object detection and classification obtain high accuracy, they typically do not levera"
486,,Learnable Graph Guided Deep Multi-View Representation Learning via Information Bottleneck,Liang Zhao; Xiao Wang; Zhenjiao Liu; Ziyue Wang; Zhikui Chen,2025,IEEE transactions on circuits and systems for video technology (Print),,,,,16,0.000,0.000,10.1109/TCSVT.2024.3509892,https://www.semanticscholar.org/paper/60296bbabd52881cb6dbb52de2d6843d29541d01,,semantic_scholar,,"In real world applications, multi-view data has attracted intensive attention due to the complex and complementary relationship across views. Multi-view representation learning (MvRL) focuses on obtaining consistent feature representation from multi-view data, and becomes a popular topic in multi-vi"
487,,Multi-View Joint Graph Representation Learning for Urban Region Embedding,Xin Liang; Dawei Cheng; Fangzhou Yang; Yifeng Luo; Weining Qian,2020,International Joint Conference on Artificial Intelligence,,,,,221,0.000,0.000,10.24963/ijcai.2020/605,https://www.semanticscholar.org/paper/670941ccda31b4473943078e74011b5d54adaaf1,https://www.ijcai.org/proceedings/2020/0611.pdf,semantic_scholar,,"The increasing amount of urban data enable us to investigate urban dynamics, assist urban planning, and eventually, make our cities more livable and sustainable. In this paper, we focus on learning an embedding space from urban data for urban regions. For the first time, we propose a multi-view join"
488,,COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning,Simon Ging; M. Zolfaghari; H. Pirsiavash; T. Brox,2020,Neural Information Processing Systems,,,,,178,0.000,0.000,,https://www.semanticscholar.org/paper/80089ad641bae28b0e57771afef181b60011069e,,semantic_scholar,,"Many real-world video-text tasks involve different levels of granularity, such as frames and words, clip and sentences or videos and paragraphs, each with distinct semantics. In this paper, we propose a Cooperative hierarchical Transformer (COOT) to leverage this hierarchy information and model the "
489,,Robo-GS: A Physics Consistent Spatial-Temporal Model for Robotic Arm with Hybrid Representation,Haozhe Lou; Yurong Liu; Yike Pan; Yiran Geng; Jianteng Chen,2024,IEEE International Conference on Robotics and Automation,,,,,46,0.000,0.000,10.1109/ICRA55743.2025.11128786,https://www.semanticscholar.org/paper/e61b64d960acf3c609be16d2bba405d0a8ba5a11,,semantic_scholar,,The Real2Sim2Real (R2S2R) paradigm is critical for advancing robotic learning. Existing methods lack a comprehensive solution to accurately reconstruct real-world objects with both spatial representations and their associated physics attributes in the Real2Sim stage. We propose a Real2Sim pipeline t
490,,Learning Degradation-Invariant Representation for Robust Real-World Person Re-Identification,Yukun Huang; Xueyang Fu; Liang Li; Zhengjun Zha,2022,International Journal of Computer Vision,,,,,15,0.000,0.000,10.1007/s11263-022-01666-w,https://www.semanticscholar.org/paper/bd50839162afda4ce24adc4d2fe7681d14d6904c,,semantic_scholar,,
491,,HyperSAGE: Generalizing Inductive Representation Learning on Hypergraphs,Devanshu Arya; D. Gupta; Stevan Rudinac; M. Worring,2020,arXiv.org,,,,,93,0.000,0.000,,https://www.semanticscholar.org/paper/afcece271f5fa6032b58b515934f3a0f61910f48,,semantic_scholar,,"Graphs are the most ubiquitous form of structured data representation used in machine learning. They model, however, only pairwise relations between nodes and are not designed for encoding the higher-order relations found in many real-world datasets. To model such complex relations, hypergraphs have"
492,,Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data With Competing Risks,Chirag Nagpal; Xinyu Li; A. Dubrawski,2020,IEEE journal of biomedical and health informatics,,,,,146,0.000,0.000,10.1109/JBHI.2021.3052441,https://www.semanticscholar.org/paper/36cc5293722d7f420c5992d322c6d87fe7ef53c4,https://arxiv.org/pdf/2003.01176,semantic_scholar,,"We describe a new approach to estimating relative risks in time-to-event prediction problems with censored data in a fully parametric manner. Our approach does not require making strong assumptions of constant proportional hazards of the underlying survival distribution, as required by the Cox-propo"
493,,Vu1SPG: Vulnerability detection based on slice property graph representation learning,Weining Zheng; Yuan Jiang; Xiaohong Su,2021,IEEE International Symposium on Software Reliability Engineering,,,,,47,0.000,0.000,10.1109/ISSRE52982.2021.00054,https://www.semanticscholar.org/paper/79d77cf59739fbf09f48b421f84fd5ea65d743c6,,semantic_scholar,,"Vulnerability detection is an important issue in software security. Although various data-driven vulnerability detection methods have been proposed, the task remains challenging since the diversity and complexity of real-world vulnerable code in syntax and semantics make it difficult to extract vuln"
494,,Action-Sufficient State Representation Learning for Control with Structural Constraints,Biwei Huang; Chaochao Lu; Liu Leqi; José Miguel Hernández-Lobato; C. Glymour,2021,International Conference on Machine Learning,,,,,40,0.000,0.000,,https://www.semanticscholar.org/paper/62359c727af97139672759a64fd3ee22eb3badde,,semantic_scholar,,"Perceived signals in real-world scenarios are usually high-dimensional and noisy, and finding and using their representation that contains essential and sufficient information required by downstream decision-making tasks will help improve computational efficiency and generalization ability in the ta"
495,,LLM2CLIP: Powerful Language Model Unlocks Richer Visual Representation,Weiquan Huang; Aoqi Wu; Yifan Yang; Xufang Luo; Yuqing Yang,2024,arXiv.org,,,,,28,0.000,0.000,10.48550/arXiv.2411.04997,https://www.semanticscholar.org/paper/e675fbe742e3f26162e991444aadfbb6a632b4d2,,semantic_scholar,,CLIP is a foundational multimodal model that aligns image and text features into a shared representation space via contrastive learning on large-scale image-text pairs. Its effectiveness primarily stems from the use of natural language as rich supervision. Motivated by the remarkable advancements in
496,,Self-Supervised Point Cloud Representation Learning via Separating Mixed Shapes,Chao Sun; Zhedong Zheng; Xiaohan Wang; Mingliang Xu; Yi Yang,2021,IEEE transactions on multimedia,,,,,26,0.000,0.000,10.1109/TMM.2022.3206664,https://www.semanticscholar.org/paper/7865aaed7e9cf9c143232231890e0070f60ca8ee,https://arxiv.org/pdf/2109.00452,semantic_scholar,,"The manual annotation for large-scale point clouds costs a lot of time and is usually unavailable in harsh real-world scenarios. Inspired by the great success of the pre-training and fine-tuning paradigm in both vision and language tasks, we argue that pre-training is one potential solution for obta"
497,,Network Representation Learning: From Traditional Feature Learning to Deep Learning,Ke Sun; Lei Wang; Bo Xu; Wenhong Zhao; S. Teng,2021,IEEE Access,,,,,29,0.000,0.000,10.1109/ACCESS.2020.3037118,https://www.semanticscholar.org/paper/a3b0b89e91eeedbe36db7dff5d019cff7538b389,https://ieeexplore.ieee.org/ielx7/6287639/8948470/09253633.pdf,semantic_scholar,,"Network representation learning (NRL) is an effective graph analytics technique and promotes users to deeply understand the hidden characteristics of graph data. It has been successfully applied in many real-world tasks related to network science, such as social network data processing, biological i"
498,,Improving Noise Robustness of Contrastive Speech Representation Learning with Speech Reconstruction,Heming Wang; Yao Qian; Xiaofei Wang; Yiming Wang; Chengyi Wang,2021,"IEEE International Conference on Acoustics, Speech, and Signal Processing",,,,,33,0.000,0.000,10.1109/icassp43922.2022.9746220,https://www.semanticscholar.org/paper/a9e394f6c189627ae5ce7b560b3ece27c7f3e863,https://arxiv.org/pdf/2110.15430,semantic_scholar,,"Noise robustness is essential for deploying automatic speech recognition (ASR) systems in real-world environments. One way to reduce the effect of noise interference is to employ a preprocessing module that conducts speech enhancement, and then feed the enhanced speech to an ASR backend. In this wor"
499,,Automated Graph Representation Learning for Node Classification,Junwei Sun; Bai Wang; Bin Wu,2021,IEEE International Joint Conference on Neural Network,,,,,17,0.000,0.000,10.1109/IJCNN52387.2021.9533811,https://www.semanticscholar.org/paper/63163733436903277cc76058a970c73b23654248,,semantic_scholar,,"Graphs are ubiquitous and play an essential role in the real world. A vital prerequisite for analyzing graphs is to learn their effective representations. Most existing graph representation learning models are hand-crafted, which lack the scalability to different kinds of graphs. In this paper, we p"
500,,Inductive Representation Learning via CNN for Partially-Unseen Attributed Networks,Zhongying Zhao; Hui Zhou; Liang Qi; Liang Chang; Mengchu Zhou,2021,IEEE Transactions on Network Science and Engineering,,,,,31,0.000,0.000,10.1109/TNSE.2020.3048902,https://www.semanticscholar.org/paper/6c0949b813ad093a8d165f2ef915ebe05d0f7a56,,semantic_scholar,,Network embedding aims to map a complex network into a low-dimensional vector space while maximally preserving the properties of the original network. An attributed network is a typical real-world network that models the relationships and attributes of real-world entities. Its analysis is of great s
501,,Heterogeneous Combat Network Link Prediction Based on Representation Learning,Wenhao Chen; Jichao Li; Jiang Jiang,2021,IEEE Systems Journal,,,,,21,0.000,0.000,10.1109/jsyst.2020.3028168,https://www.semanticscholar.org/paper/6de787e4ad9bc790879e98fde9b3818565eec537,,semantic_scholar,,
502,,Multimodal Representation Learning for Place Recognition Using Deep Hebbian Predictive Coding,M. Pearson; Shirin Dora; Oliver Struckmeier; Thomas C. Knowles; B. Mitchinson,2021,Frontiers in Robotics and AI,,,,,17,0.000,0.000,10.3389/frobt.2021.732023,https://www.semanticscholar.org/paper/0985f8d6d672ec0bb51ddc5b75b0d94cda46e1f2,https://ir.cwi.nl/pub/31299/31299.pdf,semantic_scholar,,"Recognising familiar places is a competence required in many engineering applications that interact with the real world such as robot navigation. Combining information from different sensory sources promotes robustness and accuracy of place recognition. However, mismatch in data registration, dimens"
503,,Joint Representation Learning with Relation-Enhanced Topic Models for Intelligent Job Interview Assessment,Dazhong Shen; Chuan Qin; Hengshu Zhu; Tong Xu; Enhong Chen,2021,ACM Trans. Inf. Syst.,,,,,22,0.000,0.000,10.1145/3469654,https://www.semanticscholar.org/paper/35b5af28da749c9319931046dcb5dc91c89fd7bb,,semantic_scholar,,"The job interview is considered as one of the most essential tasks in talent recruitment, which forms a bridge between candidates and employers in fitting the right person for the right job. While substantial efforts have been made on improving the job interview process, it is inevitable to have bia"
504,,Disentangle-based Continual Graph Representation Learning,Xiaoyu Kou; Yankai Lin; Shaobo Liu; Peng Li; Jie Zhou,2020,Conference on Empirical Methods in Natural Language Processing,,,,,43,0.000,0.000,10.18653/v1/2020.emnlp-main.237,https://www.semanticscholar.org/paper/5c8239d961f06c8f7a9d1baf6bfd14fe0c1f4beb,https://www.aclweb.org/anthology/2020.emnlp-main.237.pdf,semantic_scholar,,"Graph embedding (GE) methods embed nodes (and/or edges) in graph into a low-dimensional semantic space, and have shown its effectiveness in modeling multi-relational data. However, existing GE models are not practical in real-world applications since it overlooked the streaming nature of incoming da"
505,,Geographical address representation learning for address matching,Shuangli Shan; Zhixu Li; Qiang Yang; An Liu; Lei Zhao,2020,World wide web (Bussum),,,,,22,0.000,0.000,10.1007/s11280-020-00782-2,https://www.semanticscholar.org/paper/81fb53ea19c58c8279897ad5d2bd6ae34134a832,,semantic_scholar,,
506,,RoboUniView: Visual-Language Model with Unified View Representation for Robotic Manipulaiton,Fanfan Liu; Feng Yan; Liming Zheng; Chengjian Feng; Yiyang Huang,2024,arXiv.org,,,,,21,0.000,0.000,10.48550/arXiv.2406.18977,https://www.semanticscholar.org/paper/02b5acd94cbbbf7b3c4b6c4d56d590104f4a4fc6,,semantic_scholar,,"Utilizing Vision-Language Models (VLMs) for robotic manipulation represents a novel paradigm, aiming to enhance the model's ability to generalize to new objects and instructions. However, due to variations in camera specifications and mounting positions, existing methods exhibit significant performa"
507,,Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning,L. Oneto; Michele Donini; Giulia Luise; C. Ciliberto; Andreas Maurer,2020,Neural Information Processing Systems,,,,,52,0.000,0.000,,https://www.semanticscholar.org/paper/cd3d5a6c7e77d29ce3abdeb00e643f992ed17d58,,semantic_scholar,,
508,,Learning Causality-inspired Representation Consistency for Video Anomaly Detection,Y. Liu; Zhaoyang Xia; Mengyang Zhao; Donglai Wei; Yuzheng Wang,2023,ACM Multimedia,,,,,39,0.000,0.000,10.1145/3581783.3612393,https://www.semanticscholar.org/paper/a0f9cdabab08829f67e6d2c44eea1ec568ec5239,https://arxiv.org/pdf/2308.01537,semantic_scholar,,"Video anomaly detection is an essential yet challenging task in the multimedia community, with promising applications in smart cities and secure communities. Existing methods attempt to learn abstract representations of regular events with statistical dependence to model the endogenous normality, wh"
509,,Temporally Coherent Embeddings for Self-Supervised Video Representation Learning,Joshua Knights; Anthony Vanderkop; Daniel Ward; Olivia Mackenzie-Ross; Peyman Moghadam,2020,International Conference on Pattern Recognition,,,,,39,0.000,0.000,10.1109/ICPR48806.2021.9412071,https://www.semanticscholar.org/paper/195a51f9e4be3537f930d87f5200e63a51b9a226,https://arxiv.org/pdf/2004.02753,semantic_scholar,,"This paper presents TCE: Temporally Coherent Embeddings for self-supervised video representation learning. The proposed method exploits inherent structure of unlabeled video data to explicitly enforce temporal coherency in the embedding space, rather than indirectly learning it through ranking or pr"
510,,On Representation Learning for Road Networks,Meng-xiang Wang; Wang-Chien Lee; Tao-Yang Fu; Ge Yu,2020,ACM Transactions on Intelligent Systems and Technology,,,,,35,0.000,0.000,10.1145/3424346,https://www.semanticscholar.org/paper/767f1e47fd694ee6c99b1c33b57a26b7ac9ff1d1,,semantic_scholar,,"Informative representation of road networks is essential to a wide variety of applications on intelligent transportation systems. In this article, we design a new learning framework, called Representation Learning for Road Networks (RLRN), which explores various intrinsic properties of road networks"
511,,Temporal representation learning for time series classification,Yupeng Hu; Peng Zhan; Yang Xu; Jia Zhao; Yujun Li,2020,Neural computing & applications (Print),,,,,32,0.000,0.000,10.1007/s00521-020-05179-w,https://www.semanticscholar.org/paper/b3043129b718e44f75e212fb12052dff09299523,,semantic_scholar,,
512,,CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning,Yanqiao Zhu; Yichen Xu; Feng Yu; Shu Wu; Liang Wang,2020,arXiv.org,,,,,29,0.000,0.000,,https://www.semanticscholar.org/paper/093104209c8992362e43faede49fb416ca9c5976,,semantic_scholar,,"Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving graph topological structures and node attributive features. Previous graph neural networks (GNN) require a large number of labeled nodes, which may not be accessible in real-"
513,,HID: Hierarchical Multiscale Representation Learning for Information Diffusion,Pengyang Wang; Yanjie Fu; Yuanchun Zhou; Kunpeng Liu; Xiaolin Li,2020,International Joint Conference on Artificial Intelligence,,,,,25,0.000,0.000,10.24963/ijcai.2020/468,https://www.semanticscholar.org/paper/c63515ef2583a60cbb2078bbeaa6b8aeaae7d1cf,https://www.ijcai.org/proceedings/2020/0468.pdf,semantic_scholar,,"Multiscale modeling has yielded immense success on various machine learning tasks. However, it has not been properly explored for the prominent task of information diffusion, which aims to understand how information propagates along users in online social networks. For a specific user, whether and w"
514,,STAIR: Learning Sparse Text and Image Representation in Grounded Tokens,Chen Chen; Bowen Zhang; Liangliang Cao; Jiguang Shen; Tom Gunter,2023,Conference on Empirical Methods in Natural Language Processing,,,,,25,0.000,0.000,10.48550/arXiv.2301.13081,https://www.semanticscholar.org/paper/2d9fac35dd9fc10747d076faefc24c75c2622172,http://arxiv.org/pdf/2301.13081,semantic_scholar,,"Image and text retrieval is one of the foundational tasks in the vision and language domain with multiple real-world applications. State-of-the-art approaches, e.g. CLIP, ALIGN, represent images and texts as dense embeddings and calculate the similarity in the dense embedding space as the matching s"
515,,Learning an SAR Image Despeckling Model Via Weighted Sparse Representation,Junchao Zhang; Jianlai Chen; Hanwen Yu; Degui Yang; Xiaoqing Xu,2021,IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,,,,,18,0.000,0.000,10.1109/JSTARS.2021.3097119,https://www.semanticscholar.org/paper/5b3de6bb58519572be7fd0af314e33721842f607,https://ieeexplore.ieee.org/ielx7/4609443/9314330/09484779.pdf,semantic_scholar,,"Synthetic aperture radar (SAR) images are inherently degraded by the speckle noise due to the coherent imaging, which may affect the performance of subsequent image analysis task. To address this problem, a weighted sparse representation-based method is proposed in this article for SAR image despeck"
516,,TemporalGAT: Attention-Based Dynamic Graph Representation Learning,A. Fathy; Kan Li,2020,Pacific-Asia Conference on Knowledge Discovery and Data Mining,,,,,20,0.000,0.000,10.1007/978-3-030-47426-3_32,https://www.semanticscholar.org/paper/c06fc165523554b79ce59db9a8ce113b074359a0,https://link.springer.com/content/pdf/10.1007%2F978-3-030-47426-3_32.pdf,semantic_scholar,,"Learning representations for dynamic graphs is fundamental as it supports numerous graph analytic tasks such as dynamic link prediction, node classification, and visualization. Real-world dynamic graphs are continuously evolved where new nodes and edges are introduced or removed during graph evoluti"
517,,Subtractive Mixture Models via Squaring: Representation and Learning,Lorenzo Loconte; Aleksanteri M. Sladek; Stefan Mengel; Martin Trapp; Arno Solin,2023,International Conference on Learning Representations,,,,,33,0.000,0.000,10.48550/arXiv.2310.00724,https://www.semanticscholar.org/paper/2db9aa8f05caf33cdca127024bdaa2cd0657c9b2,https://arxiv.org/pdf/2310.00724,semantic_scholar,,"Mixture models are traditionally represented and learned by adding several distributions as components. Allowing mixtures to subtract probability mass or density can drastically reduce the number of components needed to model complex distributions. However, learning such subtractive mixtures while e"
518,,Integrating Image-Based and Knowledge-Based Representation Learning,Ruobing Xie; Stefan Heinrich; Zhiyuan Liu; C. Weber; Yuan Yao,2020,IEEE Transactions on Cognitive and Developmental Systems,,,,,16,0.000,0.000,10.1109/TCDS.2019.2906685,https://www.semanticscholar.org/paper/3f3bba81ab55d7ca7d3064241d7595592bc9dc86,https://ieeexplore.ieee.org/ielx7/7274989/9113770/08689107.pdf,semantic_scholar,,"A variety of brain areas is involved in language understanding and generation, accounting for the scope of language that can refer to many real-world matters. In this paper, we investigate how regularities among real-world entities impact emergent language representations. Specifically, we consider "
519,,Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Class-Imbalanced Data,Utkarsh Ojha; Krishna Kumar Singh; Cho-Jui Hsieh; Yong Jae Lee,2020,Neural Information Processing Systems,,,,,23,0.000,0.000,,https://www.semanticscholar.org/paper/68bfd32360e6399144d9559b3010b701a3588458,,semantic_scholar,,
520,,FunctionalGrasp: Learning Functional Grasp for Robots via Semantic Hand-Object Representation,Yibiao Zhang; Jinglue Hang; Tianqiang Zhu; Xiangbo Lin; Rina Wu,2023,IEEE Robotics and Automation Letters,,,,,22,0.000,0.000,10.1109/LRA.2023.3264760,https://www.semanticscholar.org/paper/eeda376e21f5ca9566607140d829991170f06d5c,,semantic_scholar,,"Successful grasp is an important and long-standing issue for robots to interact with the real world. Most recent studies have devoted more attention to stable grasp rather than functional grasp, which cannot guarantee task-oriented postgrasp manipulation. To achieve human-like functional grasp, a se"
521,,A Liouville-Weierstrass correspondence for Spacelike and Timelike Minimal Surfaces in $\mathbb{L}^3$,Adriana A. Cintra; Iury Domingos; Irene I. Onnis,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24908v1,https://arxiv.org/pdf/2512.24908v1,arxiv,,"We investigate a correspondence between solutions $λ(x,y)$ of the Liouville equation \[ Δλ= -\varepsilon e^{-4λ}, \] and the Weierstrass representations of spacelike ($\varepsilon = 1$) and timelike ($\varepsilon = -1$) minimal surfaces with diagonalizable Weingarten map in the three-dimensional Lor"
522,,Stochastic factors can matter: improving robust growth under ergodicity,Balint Binkert; David Itkin; Paul Mangers Bastian; Josef Teichmann,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24906v1,https://arxiv.org/pdf/2512.24906v1,arxiv,,"Drifts of asset returns are notoriously difficult to model accurately and, yet, trading strategies obtained from portfolio optimization are very sensitive to them. To mitigate this well-known phenomenon we study robust growth-optimization in a high-dimensional incomplete market under drift uncertain"
523,,One-Shot Camera-Based Extrusion Optimization for High Speed Fused Filament Fabrication,Yufan Lin; Xavier Guidetti; Yannick Nagel; Efe C. Balta; John Lygeros,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24905v1,https://arxiv.org/pdf/2512.24905v1,arxiv,,"Off-the-shelf fused filament fabrication 3D printers are widely accessible and convenient, yet they exhibit quality loss at high speeds due to dynamic mis-synchronization between printhead motion and material extrusion systems, notably corner over-extrusion. Existing methods require specialized hard"
524,,"FinMMDocR: Benchmarking Financial Multimodal Reasoning with Scenario Awareness, Document Understanding, and Multi-Step Computation",Zichen Tang; Haihong E; Rongjin Li; Jiacheng Liu; Linwei Jia,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24903v1,https://arxiv.org/pdf/2512.24903v1,arxiv,,"We introduce FinMMDocR, a novel bilingual multimodal benchmark for evaluating multimodal large language models (MLLMs) on real-world financial numerical reasoning. Compared to existing benchmarks, our work delivers three major advancements. (1) Scenario Awareness: 57.9% of 1,200 expert-annotated pro"
525,,Spectral Graph Neural Networks for Cognitive Task Classification in fMRI Connectomes,Debasis Maji; Arghya Banerjee; Debaditya Barman,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24901v1,https://arxiv.org/pdf/2512.24901v1,arxiv,,"Cognitive task classification using machine learning plays a central role in decoding brain states from neuroimaging data. By integrating machine learning with brain network analysis, complex connectivity patterns can be extracted from functional magnetic resonance imaging connectomes. This process "
526,,MTSP-LDP: A Framework for Multi-Task Streaming Data Publication under Local Differential Privacy,Chang Liu; Junzhou Zhao,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24899v1,https://arxiv.org/pdf/2512.24899v1,arxiv,,"The proliferation of streaming data analytics in data-driven applications raises critical privacy concerns, as directly collecting user data may compromise personal privacy. Although existing $w$-event local differential privacy (LDP) mechanisms provide formal guarantees without relying on trusted t"
527,,PRISM: A hierarchical multiscale approach for time series forecasting,Zihao Chen; Alexandre Andre; Wenrui Ma; Ian Knight; Sergey Shuvaev,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24898v1,https://arxiv.org/pdf/2512.24898v1,arxiv,,"Forecasting is critical in areas such as finance, biology, and healthcare. Despite the progress in the field, making accurate forecasts remains challenging because real-world time series contain both global trends, local fine-grained structure, and features on multiple scales in between. Here, we pr"
528,,Self-Supervised Amortized Neural Operators for Optimal Control: Scaling Laws and Applications,Wuzhe Xu; Jiequn Han; Rongjie Lai,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24897v1,https://arxiv.org/pdf/2512.24897v1,arxiv,,"Optimal control provides a principled framework for transforming dynamical system models into intelligent decision-making, yet classical computational approaches are often too expensive for real-time deployment in dynamic or uncertain environments. In this work, we propose a method based on self-sup"
529,,Semi-Automated Data Annotation in Multisensor Datasets for Autonomous Vehicle Testing,Andrii Gamalii; Daniel Górniak; Robert Nowak; Bartłomiej Olber; Krystian Radlak,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24896v1,https://arxiv.org/pdf/2512.24896v1,arxiv,,"This report presents the design and implementation of a semi-automated data annotation pipeline developed within the DARTS project, whose goal is to create a large-scale, multimodal dataset of driving scenarios recorded in Polish conditions. Manual annotation of such heterogeneous data is both costl"
530,,Resolving the Origins and Pathways of Ionizing Radiation Escape with UV Integral Field Spectroscopy,Cody Carr; Renyue Cen; Brian Fleming; Sophia Flury; Stephan McCandliss,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24895v1,https://arxiv.org/pdf/2512.24895v1,arxiv,,"The Epoch of Reionization marks the last major phase transition in the early Universe, during which the majority of neutral hydrogen once filling the intergalactic medium was ionized by the first galaxies. The James Webb Space Telescope (JWST) is now identifying promising galaxy candidates capable o"
531,,Bubbling wormholes and matrix models,Panos Betzios; Ji Hoon Lee; Olga Papadoulaki; Yanjun Zhou,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24891v1,https://arxiv.org/pdf/2512.24891v1,arxiv,,The thermofield double state entangles two copies of a CFT via a sum over energy eigenstates and is dual to the two-sided eternal black hole. We explore an analogous construction using sums over gauge group representations of half-BPS Wilson loops in multiple copies of $U(N)$ $\mathcal{N}=4$ super Y
532,,Heterogeneous Multi-Agent Multi-Target Tracking using Cellular Sheaves,Tyler Hanks; Cristian F. Nino; Joana Bou Barcelo; Austin Copeland; Warren Dixon,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24886v1,https://arxiv.org/pdf/2512.24886v1,arxiv,,"Multi-agent target tracking in the presence of nonlinear dynamics and agent heterogeneity, where state-space dimensions may differ, is a challenging problem that traditional graph Laplacian methods cannot easily address. This work leverages the framework of cellular sheaves, a mathematical generaliz"
533,,BEDA: Belief Estimation as Probabilistic Constraints for Performing Strategic Dialogue Acts,Hengli Li; Zhaoxin Yu; Qi Shen; Chenxi Li; Mengmeng Wang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24885v1,https://arxiv.org/pdf/2512.24885v1,arxiv,,"Strategic dialogue requires agents to execute distinct dialogue acts, for which belief estimation is essential. While prior work often estimates beliefs accurately, it lacks a principled mechanism to use those beliefs during generation. We bridge this gap by first formalizing two core acts Adversari"
534,,Quantumness of hybrid systems under quantum noise,M. Abdellaoui; N. -E. Abouelkhir; A. Slaoui; R. Ahl Laamara; S. Haddadi,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24884v1,https://arxiv.org/pdf/2512.24884v1,arxiv,,"We investigate the quantum correlations in an axially symmetric hybrid qubit-qutrit system subjected to different noisy environments. We first introduce a physical model and analyze its Hamiltonian structure, emphasizing the role of hybrid dimensionality and axial symmetry. The effects of decoherenc"
535,,Probing quantum-coherent dynamics with free electrons,H. B. Crispin; N. Talebi,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24883v1,https://arxiv.org/pdf/2512.24883v1,arxiv,,"Recent advances in time-resolved cathodoluminescence have enabled ultrafast studies of single emitters in quantum materials with femtosecond temporal resolution. Here, we develop a quantum theory modeling the dynamics of free electrons interacting with quantum emitters in arbitrary initial states. O"
536,,mHC: Manifold-Constrained Hyper-Connections,Zhenda Xie; Yixuan Wei; Huanqi Cao; Chenggang Zhao; Chengqi Deng,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24880v1,https://arxiv.org/pdf/2512.24880v1,arxiv,,"Recently, studies exemplified by Hyper-Connections (HC) have extended the ubiquitous residual connection paradigm established over the past decade by expanding the residual stream width and diversifying connectivity patterns. While yielding substantial performance gains, this diversification fundame"
537,,Exact Identity Linking Entropy Production and Mutual Information,Doohyeong Cho; Hawoong Jeong,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24877v1,https://arxiv.org/pdf/2512.24877v1,arxiv,,Linking entropy production (EP) to information is a key step toward data-driven nonequilibrium thermodynamics. We derive an exact identity for overdamped Langevin dynamics that equates the total EP rate to the mutual-information rate between an infinitesimal displacement and its time-symmetric midpo
538,,Insights on the homogeneous $3$-local representations of the twin groups,Mohamad N. Nasser,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24874v1,https://arxiv.org/pdf/2512.24874v1,arxiv,,"We provide a complete classification of the homogeneous $3$-local representations of the twin group $T_n$, the virtual twin group $VT_n$, and the welded twin group $WT_n$, for all $n\geq 4$. Beyond this classification, we examine the main characteristics of these representations, particularly their "
539,,"Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem",Weixun Wang; XiaoXiao Xu; Wanhe An; Fangwen Dai; Wei Gao,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24873v1,https://arxiv.org/pdf/2512.24873v1,arxiv,,"Agentic crafting requires LLMs to operate in real-world environments over multiple turns by taking actions, observing outcomes, and iteratively refining artifacts. Despite its importance, the open-source community lacks a principled, end-to-end ecosystem to streamline agent development. We introduce"
540,,Variational phase-field modeling of fracture and fatigue in shape memory alloys,Alma Brambilla; Laura De Lorenzis; Lorenza Petrini,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24871v1,https://arxiv.org/pdf/2512.24871v1,arxiv,,"We propose a novel variational phase-field model for fracture and fatigue in pseudoelastic shape memory alloys (SMAs). The model, developed in a one-dimensional setting, builds upon the Auricchio-Petrini constitutive formulation for SMAs and couples damage evolution with phase transformation. We stu"
541,,Configuration Spaces of Finite Representation Type Algebras,Nima Arkani-Hamed; Hadleigh Frost; Pierre-Guy Plamondon; Giulio Salvatori; Hugh Thomas,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24870v1,https://arxiv.org/pdf/2512.24870v1,arxiv,,"To every finite-dimensional $\mathbb C$-algebra $Λ$ of finite representation type we associate an affine variety. These varieties are a large generalization of the varieties defined by ""$u$ variables"" satisfying ""$u$-equations"", first introduced in the context of open string theory and moduli space "
542,,Correlating Resonant Di-Higgs and Tri-Higgs Production to $H\to VV$ in the 2HDM,Guglielmo Coloretti; Andreas Crivellin; Howard Haber,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24868v1,https://arxiv.org/pdf/2512.24868v1,arxiv,,"The observation of resonant di-Higgs production, which would strongly suggest the existence of a new heavy neutral scalar $H$, has been searched for extensively at the LHC. In the two-Higgs-doublet model (2HDM) with $m_H\gg m_h$, where $h$ is the Higgs boson of mass 125 GeV observed at the LHC, we s"
543,,Encyclo-K: Evaluating LLMs with Dynamically Composed Knowledge Statements,Yiming Liang; Yizhi Li; Yantao Du; Ge Zhang; Jiayi Zhou,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24867v1,https://arxiv.org/pdf/2512.24867v1,arxiv,,"Benchmarks play a crucial role in tracking the rapid advancement of large language models (LLMs) and identifying their capability boundaries. However, existing benchmarks predominantly curate questions at the question level, suffering from three fundamental limitations: vulnerability to data contami"
544,,Characterization of Transfer Using Multi-task Learning Curves,András Millinghoffer; Bence Bolgár; Péter Antal,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24866v1,https://arxiv.org/pdf/2512.24866v1,arxiv,,"Transfer effects manifest themselves both during training using a fixed data set and in inductive inference using accumulating data. We hypothesize that perturbing the data set by including more samples, instead of perturbing the model by gradient updates, provides a complementary and more fundament"
545,,Latent Twins: A Framework for Scene Recognition and Fast Radiative Transfer Inversion in FORUM All-Sky Observations,Cristina Sgattoni; Luca Sgheri; Matthias Chung; Michele Martinazzo,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24865v1,https://arxiv.org/pdf/2512.24865v1,arxiv,,"The FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) mission will provide, for the first time, systematic far-infrared spectral measurements of Earth's outgoing radiation, enabling improved understanding of atmospheric processes and the radiation budget. Retrieving atmospheric st"
546,,Big AI is accelerating the metacrisis: What can we do?,Steven Bird,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24863v1,https://arxiv.org/pdf/2512.24863v1,arxiv,,"The world is in the grip of ecological, meaning, and language crises which are converging into a metacrisis. Big AI is accelerating them all. Language engineers are playing a central role, persisting with a scalability story that is failing humanity, supplying critical talent to plutocrats and klept"
547,,OFL-SAM2: Prompt SAM2 with Online Few-shot Learner for Efficient Medical Image Segmentation,Meng Lan; Lefei Zhang; Xiaomeng Li,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24861v1,https://arxiv.org/pdf/2512.24861v1,arxiv,,"The Segment Anything Model 2 (SAM2) has demonstrated remarkable promptable visual segmentation capabilities in video data, showing potential for extension to medical image segmentation (MIS) tasks involving 3D volumes and temporally correlated 2D image sequences. However, adapting SAM2 to MIS presen"
548,,Approximate Computation via Le Cam Simulability,Deniz Akdemir,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24860v1,https://arxiv.org/pdf/2512.24860v1,arxiv,,"We propose a decision-theoretic framework for computational complexity, complementary to classical theory: moving from syntactic exactness (Turing / Shannon) to semantic simulability (Le Cam). While classical theory classifies problems by the cost of exact solution, modern computation often seeks on"
549,,Feature Slice Matching for Precise Bug Detection,Ke Ma; Jianjun Huang; Wei You; Bin Liang; Jingzheng Wu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24858v1,https://arxiv.org/pdf/2512.24858v1,arxiv,,"Measuring the function similarity to detect bugs is effective, but the statements unrelated to the bugs can impede the performance due to the noise interference. Suppressing the noise interference in existing works does not manage the tough job, i.e., eliminating the noise in the targets. In this pa"
550,,Measuring Mixed-State Topological Invariant in Open Photonic Quantum Walk,Qin-Qin Wang; Xiao-Ye Xu; Yong-Jian Han; Chuan-Feng Li; Guang-Can Guo,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24857v1,https://arxiv.org/pdf/2512.24857v1,arxiv,,"Pure-state manifestations of geometric phase are well established and have found applications across essentially all branches of physics, yet their generalization to mixed-state regimes remains largely unexplored experimentally. The Uhlmann geometric phase offers a natural extension of pure-state pa"
551,,Advances in Agentic AI: Back to the Future,Sergio Alvarez-Telena; Marta Diez-Fernandez,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24856v1,https://arxiv.org/pdf/2512.24856v1,arxiv,,"In light of the recent convergence between Agentic AI and our field of Algorithmization, this paper seeks to restore conceptual clarity and provide a structured analytical framework for an increasingly fragmented discourse. First, (a) it examines the contemporary landscape and proposes precise defin"
552,,QCD Wehrl and entanglement entropies in a gluon spectator model at small-$x$,Gabriel Rabelo-Soares; Reinaldo Francener; Gabriel S. Ramos; Giorgio Torrieri,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24855v1,https://arxiv.org/pdf/2512.24855v1,arxiv,,"Recent studies have shown that hadronic multiplicity in deep inelastic scattering is associated with an entanglement entropy. However, such definitions are intrinsically longitudinal and do not capture the full phase--space structure of the proton. In this work, we investigate the Wehrl entropy of t"
553,,VLN-MME: Diagnosing MLLMs as Language-guided Visual Navigation agents,Xunyi Zhao; Gengze Zhou; Qi Wu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24851v1,https://arxiv.org/pdf/2512.24851v1,arxiv,,"Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities across a wide range of vision-language tasks. However, their performance as embodied agents, which requires multi-round dialogue spatial reasoning and sequential action prediction, needs further exploration. Our work "
554,,SSCHA-based evolutionary crystal structure prediction at finite temperatures with account for quantum nuclear motion,Daniil Poletaev; Artem Oganov,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24849v1,https://arxiv.org/pdf/2512.24849v1,arxiv,,"Accurate crystal structure prediction (CSP) at finite temperatures with quantum anharmonic effects remains challenging but very prominent in systems with lightweight atoms such as superconducting hydrides. In this work, we integrate machine-learned interatomic potentials (MLIPs) with the stochastic "
555,,AODDiff: Probabilistic Reconstruction of Aerosol Optical Depth via Diffusion-based Bayesian Inference,Linhao Fan; Hongqiang Fang; Jingyang Dai; Yong Jiang; Qixing Zhang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24847v1,https://arxiv.org/pdf/2512.24847v1,arxiv,,"High-quality reconstruction of Aerosol Optical Depth (AOD) fields is critical for Atmosphere monitoring, yet current models remain constrained by the scarcity of complete training data and a lack of uncertainty quantification.To address these limitations, we propose AODDiff, a probabilistic reconstr"
556,,ArtiSG: Functional 3D Scene Graph Construction via Human-demonstrated Articulated Objects Manipulation,Qiuyi Gu; Yuze Sheng; Jincheng Yu; Jiahao Tang; Xiaolong Shan,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24845v1,https://arxiv.org/pdf/2512.24845v1,arxiv,,"3D scene graphs have empowered robots with semantic understanding for navigation and planning, yet they often lack the functional information required for physical manipulation, particularly regarding articulated objects. Existing approaches for inferring articulation mechanisms from static observat"
557,,Influence of Centre Body on the Dynamics of Isothermal Flow Swirl Combustor,Ratnesh Pathak; Nitesh Kumar Sahu; Pradeep Kumar Sonkar,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24844v1,https://arxiv.org/pdf/2512.24844v1,arxiv,,"This study examines the effect of centre-body geometry on the dynamics of an isothermal, non-reacting swirl combustor through computational fluid dynamics (CFD) simulations. Two different central body shapes were considered in a lab-scale combustor configuration, modelled as transient, incompressibl"
558,,friends.test: rank-based method for feature selection in interaction matrices,Alexandra Suvorikova; Alexey Kroshnin; Dmirijs Lvovs; Vera Mukhina; Andrey Mironov,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24843v1,https://arxiv.org/pdf/2512.24843v1,arxiv,,"The analysis of the interaction matrix between two distinct sets is essential across diverse fields, from pharmacovigilance to transcriptomics. Not all interactions are equally informative: a marker gene associated with a few specific biological processes is more informative than a highly expressed "
559,,Triangulation as an Acceptance Rule for Multilingual Mechanistic Interpretability,Yanan Long,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24842v1,https://arxiv.org/pdf/2512.24842v1,arxiv,,"Multilingual language models achieve strong aggregate performance yet often behave unpredictably across languages, scripts, and cultures. We argue that mechanistic explanations for such models should satisfy a \emph{causal} standard: claims must survive causal interventions and must \emph{cross-refe"
560,,When Does the Silhouette Score Work? A Comprehensive Study in Network Clustering,Zongyue Teng; Jun Yan; Dandan Liu; Panpan Zhang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24841v1,https://arxiv.org/pdf/2512.24841v1,arxiv,,"Selecting the number of communities is a fundamental challenge in network clustering. The silhouette score offers an intuitive, model-free criterion that balances within-cluster cohesion and between-cluster separation. Albeit its widespread use in clustering analysis, its performance in network-base"
561,,Scalable Stellar Parameter Inference Using Python-based LASP: From CPU Optimization to GPU Acceleration,Jun-Chao Liang; Yin-Bi Li; A-Li Luo; Fang Zuo; Bing Du,2025,arXiv,,,,,0,0.000,0.000,10.3847/1538-4357/ae1446,http://arxiv.org/abs/2512.24840v1,https://arxiv.org/pdf/2512.24840v1,arxiv,,"To enhance the efficiency, scalability, and cross-survey applicability of stellar parameter inference in large spectroscopic datasets, we present a modular, parallelized Python framework with automated error estimation, built on the LAMOST Atmospheric Parameter Pipeline (LASP) originally implemented"
562,,Role reversal in quantum Mpemba effect,Arunabha Das; Paranjoy Chaki; Priya Ghosh; Ujjwal Sen,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24839v1,https://arxiv.org/pdf/2512.24839v1,arxiv,,"We investigate the quantum Mpemba effect in a dissipative Dicke model, which consists of a spin-1/2 ensemble coupled to a bosonic mode, which in turn is coupled to a bosonic bath. We derive a sufficient criterion for occurrence of the quantum Mpemba effect, characterized by quantum coherence, in thi"
563,,A Low Background Beta Detection System using a Time Projection Chamber,Ruiyang Zhang; Zhiyong Zhang; Zengxuan Huang; Yong Zhou; Jianbei Liu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24837v1,https://arxiv.org/pdf/2512.24837v1,arxiv,,"In this paper, we present a Time Projection Chamber (TPC) system for low-background beta radiation measurements. The system consists of a TPC with two-dimensional-strip readout Micromegas and an anti-coincidence detector with readout pads for cosmic ray veto. The detector system utilize an AGET-base"
564,,Symmetric mass generation as a multicritical point with enhanced symmetry,Sandip Maiti; Debasish Banerjee; Shailesh Chandrasekharan; Marina K. Marinkovic,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24836v1,https://arxiv.org/pdf/2512.24836v1,arxiv,,We explore the phase diagram of a lattice fermion model that exhibits three distinct phases: a massless fermion (MF) phase; a massive fermion phase with spontaneous symmetry breaking (SSB) induced by a fermion bilinear condensate; and a massive fermion phase with symmetric mass generation (SMG). Usi
565,,GenZ: Foundational models as latent variable generators within traditional statistical models,Marko Jojic; Nebojsa Jojic,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24834v1,https://arxiv.org/pdf/2512.24834v1,arxiv,,"We present GenZ, a hybrid model that bridges foundational models and statistical modeling through interpretable semantic features. While large language models possess broad domain knowledge, they often fail to capture dataset-specific patterns critical for prediction tasks. Our approach addresses th"
566,,Classical integrability in 2D and asymptotic symmetries,Marcela Cárdenas,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24833v1,https://arxiv.org/pdf/2512.24833v1,arxiv,,"These lecture notes are a contribution to the proceedings of the school ""Geometric, Algebraic and Topological Methods for Quantum Field Theory"", held in Villa de Leyva, Colombia, from 31st of July to 9th of August 2023. Its intention is to put together several basic tools of classical integrability "
567,,Experimental Study on Fracture Structure of Sumi-Wari,Michiko Shimokawa; Lucas Goehring; Akie Kinoshita; Ludovic Pauchard; Hidetsugu Sakaguchid,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24830v1,https://arxiv.org/pdf/2512.24830v1,arxiv,,"Local variations in surface tension can induce complex fracture dynamics in thin interfacial films. Here, we investigate the fracture patterns that emerge when a localized surface-tension perturbation is applied to a sumi film supported on a water-glycerol subphase. Sumi is a traditional Japanese ca"
568,,Explaining Why Things Go Where They Go: Interpretable Constructs of Human Organizational Preferences,Emmanuel Fashae; Michael Burke; Leimin Tian; Lingheng Meng; Pamela Carreno-Medrano,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24829v1,https://arxiv.org/pdf/2512.24829v1,arxiv,,"Robotic systems for household object rearrangement often rely on latent preference models inferred from human demonstrations. While effective at prediction, these models offer limited insight into the interpretable factors that guide human decisions. We introduce an explicit formulation of object ar"
569,,Discovering Coordinated Joint Options via Inter-Agent Relative Dynamics,Raul D. Steleac; Mohan Sridharan; David Abel,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24827v1,https://arxiv.org/pdf/2512.24827v1,arxiv,,"Temporally extended actions improve the ability to explore and plan in single-agent settings. In multi-agent settings, the exponential growth of the joint state space with the number of agents makes coordinated behaviours even more valuable. Yet, this same exponential growth renders the design of mu"
570,,Video and Language Alignment in 2D Systems for 3D Multi-object Scenes with Multi-Information Derivative-Free Control,Jason Armitage; Rico Sennnrich,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24826v1,https://arxiv.org/pdf/2512.24826v1,arxiv,,Cross-modal systems trained on 2D visual inputs are presented with a dimensional shift when processing 3D scenes. An in-scene camera bridges the dimensionality gap but requires learning a control module. We introduce a new method that improves multivariate mutual information estimates by regret mini
571,,Practising responsibility: Ethics in NLP as a hands-on course,Malvina Nissim; Viviana Patti; Beatrice Savoldi,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24825v1,https://arxiv.org/pdf/2512.24825v1,arxiv,,"As Natural Language Processing (NLP) systems become more pervasive, integrating ethical considerations into NLP education has become essential. However, this presents inherent challenges in curriculum development: the field's rapid evolution from both academia and industry, and the need to foster cr"
572,,LMG Index: A Robust Learned Index for Multi-Dimensional Performance Balance,Yuzhen Chen; Bin Yao,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24824v1,https://arxiv.org/pdf/2512.24824v1,arxiv,,"Index structures are fundamental for efficient query processing on large-scale datasets. Learned indexes model the indexing process as a prediction problem to overcome the inherent trade-offs of traditional indexes. However, most existing learned indexes optimize only for limited objectives like que"
573,,Unsupervised Topological Phase Discovery in Periodically Driven Systems via Floquet-Bloch State,Chen-Yang Wang; Jing-Ping Xu; Ce Wang; Ya-Ping Yang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24822v1,https://arxiv.org/pdf/2512.24822v1,arxiv,,"Floquet engineering offers an unparalleled platform for realizing novel non-equilibrium topological phases. However, the unique structure of Floquet systems, which includes multiple quasienergy gaps, poses a significant challenge to classification using conventional analytical methods. We propose a "
574,,Unregularized Linear Convergence in Zero-Sum Game from Preference Feedback,Shulun Chen; Runlong Zhou; Zihan Zhang; Maryam Fazel; Simon S. Du,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24818v1,https://arxiv.org/pdf/2512.24818v1,arxiv,,"Aligning large language models (LLMs) with human preferences has proven effective for enhancing model capabilities, yet standard preference modeling using the Bradley-Terry model assumes transitivity, overlooking the inherent complexity of human population preferences. Nash learning from human feedb"
575,,Number of $K$-rational points with given $j$-invariant on modular curves,Ivan Novak,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24817v1,https://arxiv.org/pdf/2512.24817v1,arxiv,,"In this article, we study how to compute the number of $K$-rational points with a given $j$-invariant on an arbitrary modular curve. As an application, for each positive integer $n$, we determine the list of possible numbers of cyclic $n$-isogenies an elliptic curve over some number field can admit."
576,,"Upscaling from ab initio atomistic simulations to electrode scale: The case of manganese hexacyanoferrate, a cathode material for Na-ion batteries",Yuan-Chi Yang; Eric Woillez; Quentin Jacquet; Ambroise van Roekeghem,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24816v1,https://arxiv.org/pdf/2512.24816v1,arxiv,,"We present a generalizable scale-bridging computational framework that enables predictive modeling of insertion-type electrode materials from atomistic to device scales. Applied to sodium manganese hexacyanoferrate, a promising cathode material for grid-scale sodium-ion batteries, our methodology em"
577,,Probing gluons-enriched dark jets from Higgs boson exotic decays at the LHC,Wanyun Chen; Chih-Ting Lu; Hanxin Shen,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24814v1,https://arxiv.org/pdf/2512.24814v1,arxiv,,"The dark sector may possess a rich structure yet to be uncovered, and a QCD-like dark sector with GeV-scale dark hadrons can yield novel signatures at the Large Hadron Collider (LHC). In this work, we focus on a light singlet pseudoscalar mediator that connects the QCD-like dark sector to the Standa"
578,,Learning Temporally Consistent Turbulence Between Sparse Snapshots via Diffusion Models,Mohammed Sardar; Małgorzata J. Zimoń; Samuel Draycott; Alistair Revell; Alex Skillen,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24813v1,https://arxiv.org/pdf/2512.24813v1,arxiv,,"We investigate the statistical accuracy of temporally interpolated spatiotemporal flow sequences between sparse, decorrelated snapshots of turbulent flow fields using conditional Denoising Diffusion Probabilistic Models (DDPMs). The developed method is presented as a proof-of-concept generative surr"
579,,DTI-GP: Bayesian operations for drug-target interactions using deep kernel Gaussian processes,Bence Bolgár; András Millinghoffer; Péter Antal,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24810v1,https://arxiv.org/pdf/2512.24810v1,arxiv,,"Precise probabilistic information about drug-target interaction (DTI) predictions is vital for understanding limitations and boosting predictive performance. Gaussian processes (GP) offer a scalable framework to integrate state-of-the-art DTI representations and Bayesian inference, enabling novel op"
580,,Operator Entanglement from Non-Commutative Symmetries,Michele Arzano; Goffredo Chirco,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24806v1,https://arxiv.org/pdf/2512.24806v1,arxiv,,"We argue that Hopf-algebra deformations of symmetries -- as encountered in non-commutative models of quantum spacetime -- carry an intrinsic content of $operator$ $entanglement$ that is enforced by the coproduct-defined notion of composite generators. As a minimal and exactly solvable example, we an"
581,,Active phase separation: role of attractive interactions from stalled particles,Kingshuk Panja; Rajesh Singh,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24805v1,https://arxiv.org/pdf/2512.24805v1,arxiv,,"Dry active matter systems are well-known to exhibit Motility-Induced Phase Separation (MIPS). However, in wet active systems, attractive hydrodynamic interactions mediated by active particles stalled at a boundary can introduce complementary mechanisms for aggregation. In the work of Caciagli et al."
582,,Minimal Modular Flavor Symmetry and Lepton Textures Near Fixed Points,Zurab Tavartkiladze,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24804v1,https://arxiv.org/pdf/2512.24804v1,arxiv,,"An extension of the Standard Model with $Γ_2\simeq S_3$ modular flavor symmetry is presented. We consider the construction of the lepton sector, augmented by two right-handed neutrino states, in the vicinity of the fixed points $τ= i\infty $ and $τ= i$. Due to the residual symmetries at these points"
583,,Limits of quantum generative models with classical sampling hardness,Sabrina Herbst; Ivona Brandić; Adrián Pérez-Salinas,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24801v1,https://arxiv.org/pdf/2512.24801v1,arxiv,,"Sampling tasks have been successful in establishing quantum advantages both in theory and experiments. This has fueled the use of quantum computers for generative modeling to create samples following the probability distribution underlying a given dataset. In particular, the potential to build gener"
584,,LeanCat: A Benchmark Suite for Formal Category Theory in Lean (Part I: 1-Categories),Rongge Xu; Hui Dai; Yiming Fu; Jiedong Jiang; Tianjiao Nie,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24796v1,https://arxiv.org/pdf/2512.24796v1,arxiv,,"Large language models (LLMs) have made rapid progress in formal theorem proving, yet current benchmarks under-measure the kind of abstraction and library-mediated reasoning that organizes modern mathematics. In parallel with FATE's emphasis on frontier algebra, we introduce LeanCat, a Lean benchmark"
585,,Nonlinear Noise2Noise for Efficient Monte Carlo Denoiser Training,Andrew Tinits; Stephen Mann,2025,arXiv,,,,,0,0.000,0.000,10.1145/3757377.3763931,http://arxiv.org/abs/2512.24794v1,https://arxiv.org/pdf/2512.24794v1,arxiv,,"The Noise2Noise method allows for training machine learning-based denoisers with pairs of input and target images where both the input and target can be noisy. This removes the need for training with clean target images, which can be difficult to obtain. However, Noise2Noise training has a major lim"
586,,Self-Supervised Neural Architecture Search for Multimodal Deep Neural Networks,Shota Suzuki; Satoshi Ono,2025,arXiv,,,,,0,0.000,0.000,10.1587/transinf.2024EDL8018,http://arxiv.org/abs/2512.24793v1,https://arxiv.org/pdf/2512.24793v1,arxiv,,"Neural architecture search (NAS), which automates the architectural design process of deep neural networks (DNN), has attracted increasing attention. Multimodal DNNs that necessitate feature fusion from multiple modalities benefit from NAS due to their structural complexity; however, constructing an"
587,,Projection-based Adversarial Attack using Physics-in-the-Loop Optimization for Monocular Depth Estimation,Takeru Kusakabe; Yudai Hirose; Mashiho Mukaida; Satoshi Ono,2025,arXiv,,,,,0,0.000,0.000,10.1587/transinf.2025MUL0002,http://arxiv.org/abs/2512.24792v1,https://arxiv.org/pdf/2512.24792v1,arxiv,,"Deep neural networks (DNNs) remain vulnerable to adversarial attacks that cause misclassification when specific perturbations are added to input images. This vulnerability also threatens the reliability of DNN-based monocular depth estimation (MDE) models, making robustness enhancement a critical ne"
588,,Rational orbits in some prehomogeneous vector spaces associated to $Sp_{6}$ revisited,Sayan Pal,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24789v1,https://arxiv.org/pdf/2512.24789v1,arxiv,,"Let $k$ be a field with $\text{char}(k)\neq 2$. We prove that all maximal flags of composition algebras over $k$, appear as the $k$-rational $Sp_{6}$-orbits in a Zariski-dense $Sp_{6}$-invariant subset $V^{ss}\subset V=\wedge^{3}V_{6}$, where $V_{6}$ is the standard $6$-dimensional irreducible repre"
589,,HiGR: Efficient Generative Slate Recommendation via Hierarchical Planning and Multi-Objective Preference Alignment,Yunsheng Pang; Zijian Liu; Yudong Li; Shaojie Zhu; Zijian Luo,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24787v1,https://arxiv.org/pdf/2512.24787v1,arxiv,,"Slate recommendation, where users are presented with a ranked list of items simultaneously, is widely adopted in online platforms. Recent advances in generative models have shown promise in slate recommendation by modeling sequences of discrete semantic IDs autoregressively. However, existing autore"
590,,A Dual-Tuned Concentric Multimodal RF Coil for 7T 1H/31P MRSI: Concurrently Enhancing B1 Efficiency Over Single-Tuned References,Yunkun Zhao; Xiaoliang Zhang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24786v1,https://arxiv.org/pdf/2512.24786v1,arxiv,,"This study presents the design, simulation, and experimental validation of a dual-tuned concentric multimodal surface coil for 7T 1H/31P magnetic resonance spectroscopic imaging (MRSI), developed to significantly enhance 31P B1 efficiency while improving 1H performance. The coil architecture utilize"
591,,On rational orbits in some prehomogeneous vector spaces,Sayan Pal,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24783v1,https://arxiv.org/pdf/2512.24783v1,arxiv,,"Let $k$ be a field with characteristic different from $2$. In this paper, we describe the $k$-rational orbit spaces in some irreducible prehomogeneous vector spaces $(G,V)$ over $k$, where $G$ is a connected reductive algebraic group defined over $k$ and $V$ is an irreducible rational representation"
592,,Gradient Descent as Implicit EM in Distance-Based Neural Models,Alan Oursland,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24780v1,https://arxiv.org/pdf/2512.24780v1,arxiv,,"Neural networks trained with standard objectives exhibit behaviors characteristic of probabilistic inference: soft clustering, prototype specialization, and Bayesian uncertainty tracking. These phenomena appear across architectures -- in attention mechanisms, classification heads, and energy-based m"
593,,"The tournament ratchet's clicktime process, and metastability in a Moran model",Jan Lukas Igelbrink; Charline Smadi; Anton Wakolbinger,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24779v1,https://arxiv.org/pdf/2512.24779v1,arxiv,,"Muller's ratchet, in its prototype version, models a haploid, asexual population whose size~$N$ is constant over the generations. Slightly deleterious mutations are acquired along the lineages at a constant rate, and individuals carrying less mutations have a selective advantage. In the classical va"
594,,Quasiparticle Dynamics in the 4d-4f Ising-like Double Perovskite Ba2DyRuO6 Probed by Neutron Scattering and Machine-Learning Framework,Gourab Roy; Ekta Kushwaha; Mohit Kumar; Sayan Ghosh; Fabio Orlandi,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24778v1,https://arxiv.org/pdf/2512.24778v1,arxiv,,"Double perovskites containing 4d--4f interactions provide a platform to study complex magnetic phenomena in correlated systems. Here, we investigate the magnetic ground state and quasiparticle excitations of the fascinating double perovskite system, Ba$_2$DyRuO$_6$, through Time of flight (TOF) neut"
595,,Compute-Accuracy Pareto Frontiers for Open-Source Reasoning Large Language Models,Ákos Prucs; Márton Csutora; Mátyás Antal; Márk Marosi,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24776v1,https://arxiv.org/pdf/2512.24776v1,arxiv,,"Large Language Models (LLMs) are demonstrating rapid improvements on complex reasoning benchmarks, particularly when allowed to utilize intermediate reasoning steps before converging on a final solution. However, current literature often overlooks the significant computational burden associated with"
596,,Novel exact solutions of the Duffing equation: stability analysis and application to real non-linear deformation tests,A. D. Berezner; V. A. Fedorov; N. S. Perov; G. V. Grigoriev,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24774v1,https://arxiv.org/pdf/2512.24774v1,arxiv,,"In this study, novel exact solutions of the Duffing equation with their phase portraits have been proposed and reasoned. It is shown that phase trajectories are initially elliptical and become distorted in the unstable area within the growth of the variable parameter. Instability criteria of identif"
597,,Throughput Optimization in UAV-Mounted RIS under Jittering and Imperfect CSI via DRL,Anas K. Saeed; Mahmoud M. Salim; Ali Arshad Nasir; Ali H. Muqaibel,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24773v1,https://arxiv.org/pdf/2512.24773v1,arxiv,,"Reconfigurable intelligent surfaces (RISs) mounted on unmanned aerial vehicles (UAVs) can reshape wireless propagation on-demand. However, their performance is sensitive to UAV jitter and cascaded channel uncertainty. This paper investigates a downlink multiple-input single-output UAV-mounted RIS sy"
598,,Uncertainty-aware Semi-supervised Ensemble Teacher Framework for Multilingual Depression Detection,Mohammad Zia Ur Rehman; Velpuru Navya; Sanskar; Shuja Uddin Qureshi; Nagendra Kumar,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24772v1,https://arxiv.org/pdf/2512.24772v1,arxiv,,"Detecting depression from social media text is still a challenging task. This is due to different language styles, informal expression, and the lack of annotated data in many languages. To tackle these issues, we propose, Semi-SMDNet, a strong Semi-Supervised Multilingual Depression detection Networ"
599,,On Prats' problem with anomalous diffusion,A. Barletta,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24769v1,https://arxiv.org/pdf/2512.24769v1,arxiv,,"The classical Prats' problem of flow instability in a horizontal porous channel saturated by a fluid subject to a buoyancy force is reconsidered. In the original formulation, the driving buoyancy force results from thermal diffusion. This study, however, substitutes thermal diffusion with mass diffu"
600,,Sparse Offline Reinforcement Learning with Corruption Robustness,Nam Phuong Tran; Andi Nika; Goran Radanovic; Long Tran-Thanh; Debmalya Mandal,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24768v1,https://arxiv.org/pdf/2512.24768v1,arxiv,,"We investigate robustness to strong data corruption in offline sparse reinforcement learning (RL). In our setting, an adversary may arbitrarily perturb a fraction of the collected trajectories from a high-dimensional but sparse Markov decision process, and our goal is to estimate a near optimal poli"
601,,From Trial to Deployment: A SEM Analysis of Traveler Adoptions to Fully Operational Autonomous Taxis,Yutong Cai; Hua Wang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24767v1,https://arxiv.org/pdf/2512.24767v1,arxiv,,"Autonomous taxi services represent a transformative advancement in urban mobility, offering safety, efficiency, and round-the-clock operations. While existing literature has explored user acceptance of autonomous taxis through stated preference experiments and hypothetical scenarios, few studies hav"
602,,Dream2Flow: Bridging Video Generation and Open-World Manipulation with 3D Object Flow,Karthik Dharmarajan; Wenlong Huang; Jiajun Wu; Li Fei-Fei; Ruohan Zhang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24766v1,https://arxiv.org/pdf/2512.24766v1,arxiv,,"Generative video modeling has emerged as a compelling tool to zero-shot reason about plausible physical interactions for open-world manipulation. Yet, it remains a challenge to translate such human-led motions into the low-level actions demanded by robotic systems. We observe that given an initial i"
603,,Predicting the Oscillatory Regimes of Global Synchrony Induced by Secondary Clusters,Gug Young Kim; Mi Jin Lee; Seung-Woo Son,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24765v1,https://arxiv.org/pdf/2512.24765v1,arxiv,,"Synchronization systems with effective inertia, such as power grid networks and coupled electromechanical oscillators, are commonly modeled by the second-order Kuramoto model. In the forward process, numerical simulations exhibit a staircase-like growth of global synchrony, reflecting temporal oscil"
604,,LUNCH: A Lightweight Unified Deep-Learning Framework for General Transients Classification in High-Energy Time-Domain Astronomy,Peng Zhang; Chen-Wei Wang; Zheng-Hang Yu; Ren-Zhou Gui; Shao-Lin Xiong,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24764v1,https://arxiv.org/pdf/2512.24764v1,arxiv,,"The increasing data volume of high-energy space monitors necessitates real-time, automated transient classification for multi-messenger follow-up. Conventional methods rely on empirical features like hardness ratios and reliable localization, which are not always precisely available during early det"
605,,UniC-Lift: Unified 3D Instance Segmentation via Contrastive Learning,Ankit Dhiman; Srinath R; Jaswanth Reddy; Lokesh R Boregowda; Venkatesh Babu Radhakrishnan,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24763v1,https://arxiv.org/pdf/2512.24763v1,arxiv,,"3D Gaussian Splatting (3DGS) and Neural Radiance Fields (NeRF) have advanced novel-view synthesis. Recent methods extend multi-view 2D segmentation to 3D, enabling instance/semantic segmentation for better scene understanding. A key challenge is the inconsistency of 2D instance labels across views, "
606,,OpenOneRec Technical Report,Guorui Zhou; Honghui Bao; Jiaming Huang; Jiaxin Deng; Jinghao Zhang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24762v1,https://arxiv.org/pdf/2512.24762v1,arxiv,,"While the OneRec series has successfully unified the fragmented recommendation pipeline into an end-to-end generative framework, a significant gap remains between recommendation systems and general intelligence. Constrained by isolated data, they operate as domain specialists-proficient in pattern m"
607,,Mobility-induced phase separation in a binary mixture of active Brownian particles,D. Jiménez-Flores; A. Rodríguez-Rivas; J. M. Romero-Enrique,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24761v1,https://arxiv.org/pdf/2512.24761v1,arxiv,,"In this paper, we report a Brownian dynamics simulation of the mobility-induced phase separation which occurs in a two-dimensional binary mixture of active soft Brownian particles, whose interactions are modeled by non-additive Weeks-Chandler-Andersen potentials inspired in Lennard-Jones potentials "
608,,Generalization Capability of Deep Learning for Predicting Drag Reduction in Pulsating Turbulent Pipe Flow with Arbitrary Acceleration and Deceleration,Sota Kumazawa; Yasuhiro Yoshida; Tomohiro Nimura; Akira Murata; Kaoru Iwamoto,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24757v1,https://arxiv.org/pdf/2512.24757v1,arxiv,,"The spatiotemporal evolution of pulsating turbulent pipe flow was predicted by deep learning. A convolutional neural network (CNN) and long short-term memory (LSTM) were employed for long-term prediction by recursively predicting the local temporal evolution. To enhance prediction, physical componen"
609,,Trustworthy Equipment Monitoring via Cascaded Anomaly Detection and Thermal Localization,Sungwoo Kang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24755v1,https://arxiv.org/pdf/2512.24755v1,arxiv,,"Predictive maintenance demands accurate anomaly detection and trustable explanations. Although multimodal fusion of sensor time-series and thermal imagery shows promise, we demonstrate that naive fusion strategies can paradoxically degrade performance. This paper introduces a Cascaded Anomaly Detect"
610,,Probing a NED inspired Magnetically Charged Black Hole in the Hernquist Dark Matter Halo,Sohan Kumar Jha,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24753v1,https://arxiv.org/pdf/2512.24753v1,arxiv,,"With an intent to examine the combined effect of non-linear electrodynamics (NED) and dark matter (DM), we obtain a static and spherically symmetric solution with the black hole (BH) magnetically charged and immersed in the Hernquist DM halo (MHDM). The position of the event horizon $r_h$ and the cr"
611,,Analyzing Communication Predictability in LLM Training,Wenxue Li; Xiangzhou Liu; Yuxuan Li; Yilun Jin; Zhenghang Ren,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24750v1,https://arxiv.org/pdf/2512.24750v1,arxiv,,"Effective communication is essential in distributed training, with predictability being one of its most significant characteristics. However, existing studies primarily focus on exploiting predictability through online profiling for runtime optimization, without a systematic understanding of it. In "
612,,Quasi-Maximum Likelihood Estimation for a Genuinely Unbalanced Dynamic Network Panel Data Model,Zhijian Wang; Xingbai Xu; Tuo Liu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24748v1,https://arxiv.org/pdf/2512.24748v1,arxiv,,"This paper develops a quasi-maximum likelihood estimator for genuinely unbalanced dynamic network panel data models with individual fixed effects. We propose a model that accommodates contemporaneous and lagged network spillovers, temporal dependence, and a listing effect that activates upon a unit'"
613,,Fairness-Aware Insurance Pricing: A Multi-Objective Optimization Approach,Tim J. Boonen; Xinyue Fan; Zixiao Quan,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24747v1,https://arxiv.org/pdf/2512.24747v1,arxiv,,"Machine learning improves predictive accuracy in insurance pricing but exacerbates trade-offs between competing fairness criteria across different discrimination measures, challenging regulators and insurers to reconcile profitability with equitable outcomes. While existing fairness-aware models off"
614,,S-Duality for Non-Abelian Monopoles,Shan Hu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24743v1,https://arxiv.org/pdf/2512.24743v1,arxiv,,"In $\mathcal{N}=4$ super-Yang-Mills theory with gauge group $G$ spontaneously broken to a subgroup $H$, S-duality requires that the BPS monopole spectrum organizes into the same representation as W-bosons in the dual theory, where $G^{\vee}$ is broken to $H^{\vee}$. The expectation has been extensiv"
615,,Splatwizard: A Benchmark Toolkit for 3D Gaussian Splatting Compression,Xiang Liu; Yimin Zhou; Jinxiang Wang; Yujun Huang; Shuzhao Xie,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24742v1,https://arxiv.org/pdf/2512.24742v1,arxiv,,"The recent advent of 3D Gaussian Splatting (3DGS) has marked a significant breakthrough in real-time novel view synthesis. However, the rapid proliferation of 3DGS-based algorithms has created a pressing need for standardized and comprehensive evaluation tools, especially for compression task. Exist"
616,,Control of Microrobots with Reinforcement Learning under On-Device Compute Constraints,Yichen Liu; Kesava Viswanadha; Zhongyu Li; Nelson Lojo; Kristofer S. J. Pister,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24740v1,https://arxiv.org/pdf/2512.24740v1,arxiv,,"An important function of autonomous microrobots is the ability to perform robust movement over terrain. This paper explores an edge ML approach to microrobot locomotion, allowing for on-device, lower latency control under compute, memory, and power constraints. This paper explores the locomotion of "
617,,SLM-TTA: A Framework for Test-Time Adaptation of Generative Spoken Language Models,Yuan-Kuei Wu; Yang Liu; Yiteng Huang; Zhaojun Yang; Haibin Wu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24739v1,https://arxiv.org/pdf/2512.24739v1,arxiv,,"Spoken Language Models (SLMs) are increasingly central to modern speech-driven applications, but performance degrades under acoustic shift - real-world noise, reverberation, and microphone variation. Prior solutions rely on offline domain adaptation, which is post-hoc, data-intensive, and slow. We i"
618,,The disordered Su-Schrieffer-Heeger model,Michael Hilke,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24738v1,https://arxiv.org/pdf/2512.24738v1,arxiv,,"Quantum topology categorizes physical systems in integer invariants, which are robust to some deformations and certain types of disorder. A prime example is the Su-Schrieffer-Heeger (SSH) model, which has two distinct topological phases, the trivial phase with no edge states and the non-trivial phas"
619,,Structure of twisted Jacquet modules of principal series representations of $GL_{2n}(F)$,C. Harshitha; C. G. Venketasubramanian,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24737v1,https://arxiv.org/pdf/2512.24737v1,arxiv,,Let $F$ be a non-archimedean local field or a finite field. Let $π$ be a principal series representation of $GL_{2n}(F)$ induced from any of its maximal standard parabolic subgroups. Let $N$ be the unipotent radical of the maximal parabolic subgroup $P$ of $GL_{2n}(F)$ corresponding to the partition
620,,Some Studies on Stochastic Optimization based Quantitative Risk Management,Zhaolin Hu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24736v1,https://arxiv.org/pdf/2512.24736v1,arxiv,,"Risk management often plays an important role in decision making under uncertainty. In quantitative risk management, assessing and optimizing risk metrics requires efficient computing techniques and reliable theoretical guarantees. In this paper, we introduce several topics on quantitative risk mana"
621,,Exact compensation of communication delays for discrete-time heterogeneous multi-agent linear systems with applications to SIR epidemic model,Qin Fang; Mamadou Diagne; Yang Zhu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24735v1,https://arxiv.org/pdf/2512.24735v1,arxiv,,"This paper investigates the output synchronization problem for discrete-time heterogeneous multi-agent systems (MASs) subject to distinct communication delays. The presence of such delays prevents the instantaneous delivery of information from neighboring nodes, thereby severely degrading the perfor"
622,,From boundary random walks to Feller's Brownian Motions,Liping Li; Zhangjie Wang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24734v1,https://arxiv.org/pdf/2512.24734v1,arxiv,,"We establish an invariance principle connecting boundary random walks on $\mathbb N$ with Feller's Brownian motions on $[0,\infty)$. A Feller's Brownian motion is a Feller process on $[0,\infty)$ whose excursions away from the boundary $0$ coincide with those of a killed Brownian motion, while its b"
623,,BIOME-Bench: A Benchmark for Biomolecular Interaction Inference and Multi-Omics Pathway Mechanism Elucidation from Scientific Literature,Sibo Wei; Peng Chen; Lifeng Dong; Yin Luo; Lei Wang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24733v1,https://arxiv.org/pdf/2512.24733v1,arxiv,,"Multi-omics studies often rely on pathway enrichment to interpret heterogeneous molecular changes, but pathway enrichment (PE)-based workflows inherit structural limitations of pathway resources, including curation lag, functional redundancy, and limited sensitivity to molecular states and intervent"
624,,EchoFoley: Event-Centric Hierarchical Control for Video Grounded Creative Sound Generation,Bingxuan Li; Yiming Cui; Yicheng He; Yiwei Wang; Shu Zhang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24731v1,https://arxiv.org/pdf/2512.24731v1,arxiv,,"Sound effects build an essential layer of multimodal storytelling, shaping the emotional atmosphere and the narrative semantics of videos. Despite recent advancement in video-text-to-audio (VT2A), the current formulation faces three key limitations: First, an imbalance between visual and textual con"
625,,Model-independent search of gravitational wave echoes in LVK data,Di Wu; Xi-Li Zhang; Qing-Guo Huang; Jing Ren,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24730v1,https://arxiv.org/pdf/2512.24730v1,arxiv,,"Gravitational wave echoes offer a unique probe of the near-horizon structure of astrophysical black holes, beyond the standard ''black hole spectroscopy''. Theoretical waveform predictions, however, remain uncertain, motivating robust searches that avoid specific echo modeling. We present a model-in"
626,,"T-duality for toric manifolds in $\mathcal{N}=(2, 2)$ superspace",Dmitri Bykov; Savva Kutsubin; Andrew Kuzovchikov,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24726v1,https://arxiv.org/pdf/2512.24726v1,arxiv,,"We study the situation when the T-dual of a toric Kähler geometry is a generalized Kähler geometry involving semi-chiral fields. We explain that this situation is generic for polycylinders, tori and related geometries. Gauging multiple isometries in this case requires the introduction of semi-chiral"
627,,FlowBlending: Stage-Aware Multi-Model Sampling for Fast and High-Fidelity Video Generation,Jibin Song; Mingi Kwon; Jaeseok Jeong; Youngjung Uh,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24724v1,https://arxiv.org/pdf/2512.24724v1,arxiv,,"In this work, we show that the impact of model capacity varies across timesteps: it is crucial for the early and late stages but largely negligible during the intermediate stage. Accordingly, we propose FlowBlending, a stage-aware multi-model sampling strategy that employs a large model and a small "
628,,Equivalence of Personalized PageRank and Successor Representations,Beren Millidge,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24722v1,https://arxiv.org/pdf/2512.24722v1,arxiv,,"The hippocampus appears to implement two core but highly distinct functions in the brain: long term memory retrieval and planning and spatial navigation. Naively, these functions appear very different algorithmically. In this short note, we demonstrate that two powerful algorithms that have each ind"
629,,Products of random Hermitian matrices and brickwork Hurwitz numbers. Products of normal matrices,Ch. Li; A. Yu. Orlov,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24720v1,https://arxiv.org/pdf/2512.24720v1,arxiv,,"We consider products of $n$ random Hermitian matrices which generalize the one-matrix model and show its relation to Hurwitz numbers which count ramified coverings of certain type. Namely, these Hurwitz numbers count $2k$-fold ramified coverings of the Riemann sphere with arbitrary ramification type"
630,,A proximal subgradient algorithm for constrained multiobjective DC-type optimization,Nguyen Van Tuyen; Minh N. Dao; Tran Van Nghi,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24717v1,https://arxiv.org/pdf/2512.24717v1,arxiv,,"In this paper, we consider a class of constrained multiobjective optimization problems, where each objective function can be expressed by adding a possibly nonsmooth nonconvex function and a differentiable function with Lipschitz continuous gradient, then subtracting a weakly convex function. This e"
631,,MDiffFR: Modality-Guided Diffusion Generation for Cold-start Items in Federated Recommendation,Kang Fu; Honglei Zhang; Xuechao Zou; Yidong Li,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24715v1,https://arxiv.org/pdf/2512.24715v1,arxiv,,"Federated recommendations (FRs) provide personalized services while preserving user privacy by keeping user data on local clients, which has attracted significant attention in recent years. However, due to the strict privacy constraints inherent in FRs, access to user-item interaction data and user "
632,,FPGA Co-Design for Efficient N:M Sparse and Quantized Model Inference,Fen-Yu Hsieh; Yun-Chang Teng; Ding-Yong Hong; Jan-Jan Wu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24713v1,https://arxiv.org/pdf/2512.24713v1,arxiv,,"Large language models (LLMs) have demonstrated remarkable performance across a wide range of language processing tasks. However, this success comes at the cost of substantial computation and memory requirements, which significantly impedes their deployment in resource-constrained environments. To ad"
633,,LSRE: Latent Semantic Rule Encoding for Real-Time Semantic Risk Detection in Autonomous Driving,Qian Cheng; Weitao Zhou; Cheng Jing; Nanshan Deng; Junze Wen,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24712v1,https://arxiv.org/pdf/2512.24712v1,arxiv,,"Real-world autonomous driving must adhere to complex human social rules that extend beyond legally codified traffic regulations. Many of these semantic constraints, such as yielding to emergency vehicles, complying with traffic officers' gestures, or stopping for school buses, are intuitive for huma"
634,,MEIC-DT: Memory-Efficient Incremental Clustering for Long-Text Coreference Resolution with Dual-Threshold Constraints,Kangyang Luo; Shuzheng Si; Yuzhuo Bai; Cheng Gao; Zhitong Wang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24711v1,https://arxiv.org/pdf/2512.24711v1,arxiv,,"In the era of large language models (LLMs), supervised neural methods remain the state-of-the-art (SOTA) for Coreference Resolution. Yet, their full potential is underexplored, particularly in incremental clustering, which faces the critical challenge of balancing efficiency with performance for lon"
635,,BandiK: Efficient Multi-Task Decomposition Using a Multi-Bandit Framework,András Millinghoffer; András Formanek; András Antos; Péter Antal,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24708v1,https://arxiv.org/pdf/2512.24708v1,arxiv,,"The challenge of effectively transferring knowledge across multiple tasks is of critical importance and is also present in downstream tasks with foundation models. However, the nature of transfer, its transitive-intransitive nature, is still an open problem, and negative transfer remains a significa"
636,,Baryons and baryoniums in the perspective of QCD sum rules,Sheng-Qi Zhang; Cong-Feng Qiao,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24706v1,https://arxiv.org/pdf/2512.24706v1,arxiv,,"Following the experimental confirmation of tetraquark and pentaquark states, the search for hexaquark states has emerged as a new frontier in hadron physics. Recent experimental progress, particularly by the BESIII collaboration, has provided compelling evidence for the existence of near-threshold $"
637,,"Interfacing Atomic Spins with Photons for Quantum Metrology, Simulation and Computation",Monika Schleier-Smith,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24705v1,https://arxiv.org/pdf/2512.24705v1,arxiv,,"These lecture notes discuss applications of atom-light interactions in cavities to quantum metrology, simulation, and computation. A focus is on nonlocally interacting spin systems realized by coupling many atoms to a delocalized mode of light. We will build up from the fundamentals: understanding h"
638,,"Evolving, Not Training: Zero-Shot Reasoning Segmentation via Evolutionary Prompting",Kai Ye; Xiaotong You; Jianghang Lin; Jiayi Ji; Pingyang Dai,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24702v1,https://arxiv.org/pdf/2512.24702v1,arxiv,,"Reasoning Segmentation requires models to interpret complex, context-dependent linguistic queries to achieve pixel-level localization. Current dominant approaches rely heavily on Supervised Fine-Tuning (SFT) or Reinforcement Learning (RL). However, SFT suffers from catastrophic forgetting and domain"
639,,Reformulating Confidence as Extended Likelihood,Youngjo Lee,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24701v1,https://arxiv.org/pdf/2512.24701v1,arxiv,,"Fisher's fiducial probability has recently received renewed attention under the name confidence. In this paper, we reformulate it within an extended-likelihood framework, a representation that helps to resolve many long-standing controversies. The proposed formulation accommodates multi-dimensional "
640,,Dynamic Policy Learning for Legged Robot with Simplified Model Pretraining and Model Homotopy Transfer,Dongyun Kang; Min-Gyu Kim; Tae-Gyu Song; Hajun Kim; Sehoon Ha,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24698v1,https://arxiv.org/pdf/2512.24698v1,arxiv,,"Generating dynamic motions for legged robots remains a challenging problem. While reinforcement learning has achieved notable success in various legged locomotion tasks, producing highly dynamic behaviors often requires extensive reward tuning or high-quality demonstrations. Leveraging reduced-order"
641,,Causal Discovery with Mixed Latent Confounding via Precision Decomposition,Amir Asiaee; Samhita Pal; James O'quinn; James P. Long,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24696v1,https://arxiv.org/pdf/2512.24696v1,arxiv,,"We study causal discovery from observational data in linear Gaussian systems affected by \emph{mixed latent confounding}, where some unobserved factors act broadly across many variables while others influence only small subsets. This setting is common in practice and poses a challenge for existing m"
642,,Nested Learning: The Illusion of Deep Learning Architectures,Ali Behrouz; Meisam Razaviyayn; Peilin Zhong; Vahab Mirrokni,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24695v1,https://arxiv.org/pdf/2512.24695v1,arxiv,,"Despite the recent progresses, particularly in developing Language Models, there are fundamental challenges and unanswered questions about how such models can continually learn/memorize, self-improve, and find effective solutions. In this paper, we present a new learning paradigm, called Nested Lear"
643,,Mobility-Assisted Decentralized Federated Learning: Convergence Analysis and A Data-Driven Approach,Reza Jahani; Md Farhamdur Reza; Richeng Jin; Huaiyu Dai,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24694v1,https://arxiv.org/pdf/2512.24694v1,arxiv,,"Decentralized Federated Learning (DFL) has emerged as a privacy-preserving machine learning paradigm that enables collaborative training among users without relying on a central server. However, its performance often degrades significantly due to limited connectivity and data heterogeneity. As we mo"
644,,MUSIC: MUlti-Step Instruction Contrast for Multi-Turn Reward Models,Wenzhe Li; Shujian Zhang; Wenxuan Zhou; John Lambert; Chi Jin,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24693v1,https://arxiv.org/pdf/2512.24693v1,arxiv,,"Evaluating the quality of multi-turn conversations is crucial for developing capable Large Language Models (LLMs), yet remains a significant challenge, often requiring costly human evaluation. Multi-turn reward models (RMs) offer a scalable alternative and can provide valuable signals for guiding LL"
645,,${\cal N}=8$ supersymmetric mechanics with spin variables from indecomposable multiplets,Evgeny Ivanov; Stepan Sidorov,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24692v1,https://arxiv.org/pdf/2512.24692v1,arxiv,,"We define two new indecomposable (not fully reducible) ${\cal N}=8$, $d=1$ off-shell multiplets and consider the corresponding models of ${\cal N}=8$ supersymmetric mechanics with spin variables. Each multiplet is described off shell by a scalar superfield which is a nonlinear deformation of the sta"
646,,Non-perturbative Thermodynamics of Quark Gluon Plasma and Gravitational Waves,Narasimha Reddy Gosala; Arundhati Dasgupta,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24691v1,https://arxiv.org/pdf/2512.24691v1,arxiv,,"Quark-Gluon Plasma (QGP), a strongly interacting state of the early universe, exhibits remarkably fluid-like behavior despite its underlying non-Abelian dynamics. Motivated by these features, we explore time-dependent SU(2) Yang-Mills condensates as non-linear classical background fields to model QG"
647,,CREPES-X: Hierarchical Bearing-Distance-Inertial Direct Cooperative Relative Pose Estimation System,Zhehan Li; Zheng Wang; Jiadong Lu; Qi Liu; Zhiren Xun,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24688v1,https://arxiv.org/pdf/2512.24688v1,arxiv,,Relative localization is critical for cooperation in autonomous multi-robot systems. Existing approaches either rely on shared environmental features or inertial assumptions or suffer from non-line-of-sight degradation and outliers in complex environments. Robust and efficient fusion of inter-robot 
648,,Quantum Visual Word Sense Disambiguation: Unraveling Ambiguities Through Quantum Inference Model,Wenbo Qiao; Peng Zhang; Qinghua Hu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24687v1,https://arxiv.org/pdf/2512.24687v1,arxiv,,"Visual word sense disambiguation focuses on polysemous words, where candidate images can be easily confused. Traditional methods use classical probability to calculate the likelihood of an image matching each gloss of the target word, summing these to form a posterior probability. However, due to th"
649,,BatteryAgent: Synergizing Physics-Informed Interpretation with LLM Reasoning for Intelligent Battery Fault Diagnosis,Songqi Zhou; Ruixue Liu; Boman Su; Jiazhou Wang; Yixing Wang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24686v1,https://arxiv.org/pdf/2512.24686v1,arxiv,,"Fault diagnosis of lithium-ion batteries is critical for system safety. While existing deep learning methods exhibit superior detection accuracy, their ""black-box"" nature hinders interpretability. Furthermore, restricted by binary classification paradigms, they struggle to provide root cause analysi"
650,,Waste-to-Energy-Coupled AI Data Centers: Cooling Efficiency and Grid Resilience,Qi He; Chunyu Qu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24683v1,https://arxiv.org/pdf/2512.24683v1,arxiv,,AI data-center expansion is increasingly constrained by the coupled availability of deliverable electricity and heat-rejection (cooling) capacity. We propose and evaluate an integrated Waste-to-Energy-AI Data Center configuration that treats cooling as a first-class energy service rather than an una
651,,CellSecInspector: Safeguarding Cellular Networks via Automated Security Analysis on Specifications,Ke Xie; Xingyi Zhao; Yiwen Hu; Munshi Saifuzzaman; Wen Li,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24682v1,https://arxiv.org/pdf/2512.24682v1,arxiv,,"The complexity, interdependence, and rapid evolution of 3GPP specifications present fundamental challenges for ensuring the security of modern cellular networks. Manual reviews and existing automated approaches, which often depend on rule-based parsing or small sets of manually crafted security requ"
652,,ReSPIRe: Informative and Reusable Belief Tree Search for Robot Probabilistic Search and Tracking in Unknown Environments,Kangjie Zhou; Zhaoyang Li; Han Gao; Yao Su; Hangxin Liu,2025,arXiv,,,,,0,0.000,0.000,10.1109/TSMC.2025.3636589,http://arxiv.org/abs/2512.24680v1,https://arxiv.org/pdf/2512.24680v1,arxiv,,"Target search and tracking (SAT) is a fundamental problem for various robotic applications such as search and rescue and environmental exploration. This paper proposes an informative trajectory planning approach, namely ReSPIRe, for SAT in unknown cluttered environments under considerably inaccurate"
653,,Multi-modal cross-domain mixed fusion model with dual disentanglement for fault diagnosis under unseen working conditions,Pengcheng Xia; Yixiang Huang; Chengjin Qin; Chengliang Liu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24679v1,https://arxiv.org/pdf/2512.24679v1,arxiv,,"Intelligent fault diagnosis has become an indispensable technique for ensuring machinery reliability. However, existing methods suffer significant performance decline in real-world scenarios where models are tested under unseen working conditions, while domain adaptation approaches are limited to th"
654,,A New Decomposition Paradigm for Graph-structured Nonlinear Programs via Message Passing,Kuangyu Ding; Marie Maros; Gesualdo Scutari,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24676v1,https://arxiv.org/pdf/2512.24676v1,arxiv,,"We study finite-sum nonlinear programs whose decision variables interact locally according to a graph or hypergraph. We propose MP-Jacobi (Message Passing-Jacobi), a graph-compliant decentralized framework that couples min-sum message passing with Jacobi block updates. The (hyper)graph is partitione"
655,,"An Adaptive, Disentangled Representation for Multidimensional MRI Reconstruction",Ruiyang Zhao; Fan Lam,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24674v1,https://arxiv.org/pdf/2512.24674v1,arxiv,,"We present a new approach for representing and reconstructing multidimensional magnetic resonance imaging (MRI) data. Our method builds on a novel, learned feature-based image representation that disentangles different types of features, such as geometry and contrast, into distinct low-dimensional l"
656,,VLA-RAIL: A Real-Time Asynchronous Inference Linker for VLA Models and Robots,Yongsheng Zhao; Lei Zhao; Baoping Cheng; Gongxin Yao; Xuanzhang Wen,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24673v1,https://arxiv.org/pdf/2512.24673v1,arxiv,,"Vision-Language-Action (VLA) models have achieved remarkable breakthroughs in robotics, with the action chunk playing a dominant role in these advances. Given the real-time and continuous nature of robotic motion control, the strategies for fusing a queue of successive action chunks have a profound "
657,,An Effective Theory for Biased Tracers via the Boltzmann-Equation Approach,Tomohiro Fujita; Tomo Takahashi; Sora Yamashita,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24672v1,https://arxiv.org/pdf/2512.24672v1,arxiv,,"We develop an effective theory for biased tracers formulated at the level of the Boltzmann equation, providing a unified description of density and velocity bias. We introduce a general effective collision term in the tracer Boltzmann equation to encode tracer dynamics that are intrinsically differe"
658,,Nonparametric Bandits with Single-Index Rewards: Optimality and Adaptivity,Wanteng Ma; T. Tony Cai,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24669v1,https://arxiv.org/pdf/2512.24669v1,arxiv,,"Contextual bandits are a central framework for sequential decision-making, with applications ranging from recommendation systems to clinical trials. While nonparametric methods can flexibly model complex reward structures, they suffer from the curse of dimensionality. We address this challenge using"
659,,Disentangle Intertwined Interactions in Correlated Charge Density Wave with Magnetic Impurities,J. W. Park; H. Kim; H. W. Yeom,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24668v1,https://arxiv.org/pdf/2512.24668v1,arxiv,,"Magnetic impurities in strongly correlated electronic systems serve as sensitive probes to a wide range of many-body quantum phenomena. Broken symmetries in such a system can lead to inequivalent lattice sites, and magnetic impurities may interact selectively with particular orbitals or sublattices."
660,,Distributed Bilevel Optimization with Dual Pruning for Resource-limited Clients,Mingyi Li; Xiao Zhang; Ruisheng Zheng; Hongjian Shi; Yuan Yuan,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24667v1,https://arxiv.org/pdf/2512.24667v1,arxiv,,"With the development of large-scale models, traditional distributed bilevel optimization algorithms cannot be applied directly in low-resource clients. The key reason lies in the excessive computation involved in optimizing both the lower- and upper-level functions. Thus, we present the first resour"
661,,HeteroHBA: A Generative Structure-Manipulating Backdoor Attack on Heterogeneous Graphs,Honglin Gao; Lan Zhao; Junhao Ren; Xiang Li; Gaoxi Xiao,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24665v1,https://arxiv.org/pdf/2512.24665v1,arxiv,,"Heterogeneous graph neural networks (HGNNs) have achieved strong performance in many real-world applications, yet targeted backdoor poisoning on heterogeneous graphs remains less studied. We consider backdoor attacks for heterogeneous node classification, where an adversary injects a small set of tr"
662,,Renormalization Group Guided Tensor Network Structure Search,Maolin Wang; Bowen Yu; Sheng Zhang; Linjie Mi; Wanyu Wang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24663v1,https://arxiv.org/pdf/2512.24663v1,arxiv,,"Tensor network structure search (TN-SS) aims to automatically discover optimal network topologies and rank configurations for efficient tensor decomposition in high-dimensional data representation. Despite recent advances, existing TN-SS methods face significant limitations in computational tractabi"
663,,Aspects of Sommerfeld Enhancement in the light of Halo gamma-ray excess,Yongsoo Jho; Jeonghwan Park; Min Gi Park; Seong Chan Park,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24662v1,https://arxiv.org/pdf/2512.24662v1,arxiv,,We examine Sommerfeld enhancement in dark matter annihilation as a potential origin of the halo-like gamma-ray excess near $E_γ\simeq 20$ GeV reported by Totani. A minimal model with a light CP-even scalar mediator naturally produces a velocity-dependent annihilation cross section consistent with th
664,,Do Large Language Models Know What They Are Capable Of?,Casey O. Barkan; Sid Black; Oliver Sourbut,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24661v1,https://arxiv.org/pdf/2512.24661v1,arxiv,,We investigate whether large language models (LLMs) can predict whether they will succeed on a given task and whether their predictions improve as they progress through multi-step tasks. We also investigate whether LLMs can learn from in-context experiences to make better decisions about whether to 
665,,Hierarchical Online Optimization Approach for IRS-enabled Low-altitude MEC in Vehicular Networks,Yixian Wang; Geng Sun; Zemin Sun; Jiacheng Wang; Changyuan Zhao,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24659v1,https://arxiv.org/pdf/2512.24659v1,arxiv,,"In this paper, we propose an intelligent reflecting surface (IRS)-enabled low-altitude multi-access edge computing (MEC) architecture, where an aerial MEC server cooperates with a terrestrial MEC server to provide computing services, while hybrid IRSs (i.e., building-installed and UAV-carried IRSs) "
666,,Taking Advantage of Rational Canonical Form for Faster Ring-LWE based Encrypted Controller with Recursive Multiplication,Donghyeon Song; Yeongjun Jang; Joowon Lee; Junsoo Kim,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24658v1,https://arxiv.org/pdf/2512.24658v1,arxiv,,"This paper aims to provide an efficient implementation of encrypted linear dynamic controllers that perform recursive multiplications on a Ring-Learning With Errors (Ring-LWE) based cryptosystem. By adopting a system-theoretical approach, we significantly reduce both time and space complexities, par"
667,,Characterizing Bugs and Quality Attributes in Quantum Software: A Large-Scale Empirical Study,Mir Mohammad Yousuf; Shabir Ahmad Sofi,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24656v1,https://arxiv.org/pdf/2512.24656v1,arxiv,,"Quantum Software Engineering (QSE) is essential for ensuring the reliability and maintainability of hybrid quantum-classical systems, yet empirical evidence on how bugs emerge and affect quality in real-world quantum projects remains limited. This study presents the first ecosystem-scale longitudina"
668,,Thermodynamics Reconstructed from Information Theory:An Axiomatic Framework via Information-Volume Constraints and Path-Space KL Divergence,Tatsuaki Tsuruyama,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24655v1,https://arxiv.org/pdf/2512.24655v1,arxiv,,"We develop an axiomatic reconstruction of thermodynamics based entirely on two primitive components: a description of what aspects of a system are observed and a reference measure that encodes the underlying descriptive convention. These ingredients define an ""information volume"" for each observatio"
669,,Muscle Synergy Patterns During Running: Coordinative Mechanisms From a Neuromechanical Perspective,Ye Ma; Shixin Lin; Shengxing Fu; Yuwei Liu; Chenyi Guo,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24654v1,https://arxiv.org/pdf/2512.24654v1,arxiv,,"Running is a fundamental form of human locomotion and a key task for evaluating neuromuscular control and lower-limb coordination. In recent years, muscle synergy analysis based on surface electromyography (sEMG) has become an important approach in this area. This review focuses on muscle synergies "
670,,"RoboMIND 2.0: A Multimodal, Bimanual Mobile Manipulation Dataset for Generalizable Embodied Intelligence",Chengkai Hou; Kun Wu; Jiaming Liu; Zhengping Che; Di Wu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24653v1,https://arxiv.org/pdf/2512.24653v1,arxiv,,"While data-driven imitation learning has revolutionized robotic manipulation, current approaches remain constrained by the scarcity of large-scale, diverse real-world demonstrations. Consequently, the ability of existing models to generalize across long-horizon bimanual tasks and mobile manipulation"
671,,Hybrid Motion Planning with Deep Reinforcement Learning for Mobile Robot Navigation,Yury Kolomeytsev; Dmitry Golembiovsky,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24651v1,https://arxiv.org/pdf/2512.24651v1,arxiv,,"Autonomous mobile robots operating in complex, dynamic environments face the dual challenge of navigating large-scale, structurally diverse spaces with static obstacles while safely interacting with various moving agents. Traditional graph-based planners excel at long-range pathfinding but lack reac"
672,,A unified spatiotemporal formulation with physics-preserving structure for time-dependent convection-diffusion problems,James H. Adler; Xiaozhe Hu; Seulip Lee,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24650v1,https://arxiv.org/pdf/2512.24650v1,arxiv,,"We propose a unified four-dimensional (4D) spatiotemporal formulation for time-dependent convection-diffusion problems that preserves underlying physical structures. By treating time as an additional space-like coordinate, the evolution problem is reformulated as a stationary convection-diffusion eq"
673,,Solving the inverse Source Problems for wave equation with final time measurements by a data driven approach,Qiling Gu; Wenlong Zhang; Zhidong Zhang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24647v1,https://arxiv.org/pdf/2512.24647v1,arxiv,,"This paper develops a discrete data-driven approach for solving the inverse source problem of the wave equation with final time measurements. Focusing on the $L^2$-Tikhonov regularization method, we analyze its convergence under two different noise models, using noisy discrete spatial observations. "
674,,Dilepton emission as a novel probe of QCD critical point,Gaoqing Cao; Xiaofeng Luo; Weijie Fu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24646v1,https://arxiv.org/pdf/2512.24646v1,arxiv,,"In this work, we propose dilepton emission rate (DER) as a sensitive probe of QCD critical point based on the extended Polyakov-quark-meson model. The model could successfully capture two main mechanisms for dilepton production, $π^\pm$ and quark-antiquark annihilations on one hand, and self-consist"
675,,AudioFab: Building A General and Intelligent Audio Factory through Tool Learning,Cheng Zhu; Jing Han; Qianshuai Xue; Kehan Wang; Huan Zhao,2025,arXiv,,,,,0,0.000,0.000,10.1145/3746027.3756869,http://arxiv.org/abs/2512.24645v1,https://arxiv.org/pdf/2512.24645v1,arxiv,,"Currently, artificial intelligence is profoundly transforming the audio domain; however, numerous advanced algorithms and tools remain fragmented, lacking a unified and efficient framework to unlock their full potential. Existing audio agent frameworks often suffer from complex environment configura"
676,,Vapor-solid-solid growth of single-walled carbon nanotubes,Daniel Hedman,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24644v1,https://arxiv.org/pdf/2512.24644v1,arxiv,,"Single-walled carbon nanotubes are one-dimensional $sp^2$ carbon materials whose electronic and optical properties are governed by their chirality. Catalytic chemical vapor deposition often uses transition-metal nanoparticles that liquefy at elevated temperature, and vapor-liquid-solid growth is com"
677,,A Scalable Framework for logP Prediction: From Terabyte-Scale Data Integration to Interpretable Ensemble Modeling,Malikussaid; Septian Caesar Floresko; Ade Romadhony; Isman Kurniawan; Warih Maharani,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24643v1,https://arxiv.org/pdf/2512.24643v1,arxiv,,"This study presents a large-scale predictive modeling framework for logP prediction using 426850 bioactive compounds rigorously curated from the intersection of three authoritative chemical databases: PubChem, ChEMBL, and eMolecules. We developed a novel computational infrastructure to address the d"
678,,$\ell_0$-Regularized Item Response Theory Model for Robust Ideal Point Estimation,Kwangok Seo; Johan Lim; Seokho Lee; Jong Hee Park,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24642v1,https://arxiv.org/pdf/2512.24642v1,arxiv,,"Ideal point estimation methods face a significant challenge when legislators engage in protest voting -- strategically voting against their party to express dissatisfaction. Such votes introduce attenuation bias, making ideologically extreme legislators appear artificially moderate. We propose a nov"
679,,From Sequential to Spatial: Reordering Autoregression for Efficient Visual Generation,Siyang Wang; Hanting Li; Wei Li; Jie Hu; Xinghao Chen,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24639v1,https://arxiv.org/pdf/2512.24639v1,arxiv,,"Inspired by the remarkable success of autoregressive models in language modeling, this paradigm has been widely adopted in visual generation. However, the sequential token-by-token decoding mechanism inherent in traditional autoregressive models leads to low inference efficiency.In this paper, we pr"
680,,Resolving State Ambiguity in Robot Manipulation via Adaptive Working Memory Recoding,Qingda Hu; Ziheng Qiu; Zijun Xu; Kaizhao Zhang; Xizhou Bu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24638v1,https://arxiv.org/pdf/2512.24638v1,arxiv,,"State ambiguity is common in robotic manipulation. Identical observations may correspond to multiple valid behavior trajectories. The visuomotor policy must correctly extract the appropriate types and levels of information from the history to identify the current task phase. However, naively extendi"
681,,MSched: GPU Multitasking via Proactive Memory Scheduling,Weihang Shen; Yinqiu Chen; Rong Chen; Haibo Chen,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24637v1,https://arxiv.org/pdf/2512.24637v1,arxiv,,"The limited HBM capacity has become the primary bottleneck for hosting an increasing number of larger-scale GPU tasks. While demand paging extends capacity via host DRAM, it incurs up to 78x slowdown due to the massive working sets and poor locality of GPU workloads. We observe, however, that GPU me"
682,,How Do Agentic AI Systems Deal With Software Energy Concerns? A Pull Request-Based Study,Tanjum Motin Mitul; Md. Masud Mazumder; Md Nahidul Islam Opu; Shaiful Chowdhury,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24636v1,https://arxiv.org/pdf/2512.24636v1,arxiv,,"As Software Engineering enters its new era (SE 3.0), AI coding agents increasingly automate software development workflows. However, it remains unclear how exactly these agents recognize and address software energy concerns-an issue growing in importance due to large-scale data centers, energy-hungr"
683,,DynaFix: Iterative Automated Program Repair Driven by Execution-Level Dynamic Information,Zhili Huang; Ling Xu; Chao Liu; Weifeng Sun; Xu Zhang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24635v1,https://arxiv.org/pdf/2512.24635v1,arxiv,,"Automated Program Repair (APR) aims to automatically generate correct patches for buggy programs. Recent approaches leveraging large language models (LLMs) have shown promise but face limitations. Most rely solely on static analysis, ignoring runtime behaviors. Some attempt to incorporate dynamic si"
684,,Soliton profiles: Classical Numerical Schemes vs. Neural Network - Based Solvers,Chandler Haight; Svetlana Roudenko; Zhongming Wang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24634v1,https://arxiv.org/pdf/2512.24634v1,arxiv,,"We present a comparative study of classical numerical solvers, such as Petviashvili's method or finite difference with Newton iterations, and neural network-based methods for computing ground states or profiles of solitary-wave solutions to the one-dimensional dispersive PDEs that include the nonlin"
685,,Branched polymers with loops coupled to the critical Ising model,Jan Ambjørn; Yukimura Izawa; Yuki Sato,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24633v1,https://arxiv.org/pdf/2512.24633v1,arxiv,,"We study the continuum limit of branched polymers (BPs) with loops coupled to Ising spins at the zero-temperature critical point. It is known that the continuum partition function can be represented by a Hermitian two-matrix model, and we propose a string field theory whose Dyson-Schwinger equation "
686,,How Do Agentic AI Systems Address Performance Optimizations? A BERTopic-Based Analysis of Pull Requests,Md Nahidul Islam Opu; Shahidul Islam; Muhammad Asaduzzaman; Shaiful Chowdhury,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24630v1,https://arxiv.org/pdf/2512.24630v1,arxiv,,"LLM-based software engineering is influencing modern software development. In addition to correctness, prior studies have also examined the performance of software artifacts generated by AI agents. However, it is unclear how exactly the agentic AI systems address performance concerns in practice. In"
687,,Probing the inner structures of the observed $Ξ_b$ and $Ξ_b'$ resonances,Yu-Bin Zhang; Yi-Heng Wang; Hui-Hua Zhong; Li-Ye Xiao,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24629v1,https://arxiv.org/pdf/2512.24629v1,arxiv,,"To shed light on the inner structure of the observed single-bottom strange baryons, in this work we systematically study the Okubo-Zweig-Iizuka allowed strong decay properties of $1P$- and $2S$-wave $Ξ_b$ and $Ξ_b'$ baryons within the $j-j$ coupling scheme in the framework of the quark pair creation"
688,,AI-Driven Acoustic Voice Biomarker-Based Hierarchical Classification of Benign Laryngeal Voice Disorders from Sustained Vowels,Mohsen Annabestani; Samira Aghadoost; Anais Rameau; Olivier Elemento; Gloria Chia-Yi Chiang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24628v1,https://arxiv.org/pdf/2512.24628v1,arxiv,,"Benign laryngeal voice disorders affect nearly one in five individuals and often manifest as dysphonia, while also serving as non-invasive indicators of broader physiological dysfunction. We introduce a clinically inspired hierarchical machine learning framework for automated classification of eight"
689,,AutoFed: Manual-Free Federated Traffic Prediction via Personalized Prompt,Zijian Zhao; Yitong Shang; Sen Li,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24625v1,https://arxiv.org/pdf/2512.24625v1,arxiv,,"Accurate traffic prediction is essential for Intelligent Transportation Systems, including ride-hailing, urban road planning, and vehicle fleet management. However, due to significant privacy concerns surrounding traffic data, most existing methods rely on local training, resulting in data silos and"
690,,FireRescue: A UAV-Based Dataset and Enhanced YOLO Model for Object Detection in Fire Rescue Scenes,Qingyu Xu; Runtong Zhang; Zihuan Qiu; Fanman Meng,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24622v1,https://arxiv.org/pdf/2512.24622v1,arxiv,,"Object detection in fire rescue scenarios is importance for command and decision-making in firefighting operations. However, existing research still suffers from two main limitations. First, current work predominantly focuses on environments such as mountainous or forest areas, while paying insuffic"
691,,Forward-Oriented Causal Observables for Non-Stationary Financial Markets,Lucas A. Souza,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24621v1,https://arxiv.org/pdf/2512.24621v1,arxiv,,"We study short-horizon forecasting in financial time series under strict causal constraints, treating the market as a non-stationary stochastic system in which any predictive observable must be computable online from information available up to the decision time. Rather than proposing a machine-lear"
692,,LLHA-Net: A Hierarchical Attention Network for Two-View Correspondence Learning,Shuyuan Lin; Yu Guo; Xiao Chen; Yanjie Liang; Guobao Xiao,2025,arXiv,,,,,0,0.000,0.000,10.1016/j.patcog.2025.112896,http://arxiv.org/abs/2512.24620v1,https://arxiv.org/pdf/2512.24620v1,arxiv,,"Establishing the correct correspondence of feature points is a fundamental task in computer vision. However, the presence of numerous outliers among the feature points can significantly affect the matching results, reducing the accuracy and robustness of the process. Furthermore, a challenge arises "
693,,Youtu-LLM: Unlocking the Native Agentic Potential for Lightweight Large Language Models,Junru Lu; Jiarui Qin; Lingfeng Qiao; Yinghui Li; Xinyi Dai,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24618v1,https://arxiv.org/pdf/2512.24618v1,arxiv,,"We introduce Youtu-LLM, a lightweight yet powerful language model that harmonizes high computational efficiency with native agentic intelligence. Unlike typical small models that rely on distillation, Youtu-LLM (1.96B) is pre-trained from scratch to systematically cultivate reasoning and planning ca"
694,,Dynamic Large Concept Models: Latent Reasoning in an Adaptive Semantic Space,Xingwei Qu; Shaowen Wang; Zihao Huang; Kai Hua; Fan Yin,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24617v1,https://arxiv.org/pdf/2512.24617v1,arxiv,,"Large Language Models (LLMs) apply uniform computation to all tokens, despite language exhibiting highly non-uniform information density. This token-uniform regime wastes capacity on locally predictable spans while under-allocating computation to semantically critical transitions. We propose $\textb"
695,,Youtu-Agent: Scaling Agent Productivity with Automated Generation and Hybrid Policy Optimization,Yuchen Shi; Yuzheng Cai; Siqi Cai; Zihan Xu; Lichao Chen,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24615v1,https://arxiv.org/pdf/2512.24615v1,arxiv,,"Existing Large Language Model (LLM) agent frameworks face two significant challenges: high configuration costs and static capabilities. Building a high-quality agent often requires extensive manual effort in tool integration and prompt engineering, while deployed agents struggle to adapt to dynamic "
696,,Chat-Driven Optimal Management for Virtual Network Services,Yuya Miyaoka; Masaki Inoue; Kengo Urata; Shigeaki Harada,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24614v1,https://arxiv.org/pdf/2512.24614v1,arxiv,,"This paper proposes a chat-driven network management framework that integrates natural language processing (NLP) with optimization-based virtual network allocation, enabling intuitive and reliable reconfiguration of virtual network services. Conventional intent-based networking (IBN) methods depend "
697,,Group Deliberation Oriented Multi-Agent Conversational Model for Complex Reasoning,Zheyu Shi; Dong Qiu; Shanlong Yu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24613v1,https://arxiv.org/pdf/2512.24613v1,arxiv,,"This paper proposes a group deliberation oriented multi-agent conversational model to address the limitations of single large language models in complex reasoning tasks. The model adopts a three-level role division architecture consisting of generation, verification, and integration. An opinion gene"
698,,Reinforcement Learning-Augmented LLM Agents for Collaborative Decision Making and Performance Optimization,Dong Qiu; Duo Xu; Limengxi Yue,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24609v1,https://arxiv.org/pdf/2512.24609v1,arxiv,,Large Language Models (LLMs) perform well in language tasks but often lack collaborative awareness and struggle to optimize global performance in multi-agent settings. We present a reinforcement learning-augmented LLM agent framework that formulates cooperation as a decentralized partially observabl
699,,MoniRefer: A Real-world Large-scale Multi-modal Dataset based on Roadside Infrastructure for 3D Visual Grounding,Panquan Yang; Junfei Huang; Zongzhangbao Yin; Yingsong Hu; Anni Xu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24605v1,https://arxiv.org/pdf/2512.24605v1,arxiv,,3D visual grounding aims to localize the object in 3D point cloud scenes that semantically corresponds to given natural language sentences. It is very critical for roadside infrastructure system to interpret natural languages and localize relevant target objects in complex traffic environments. Howe
700,,Generalized Poisson Matrix Factorization for Overdispersed Count Data,Ryo Ohashi; Hiroyasu Abe; Fumitake Sakaori,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24604v1,https://arxiv.org/pdf/2512.24604v1,arxiv,,"Non-negative matrix factorization (NMF) is widely used as a feature extraction technique for matrices with non-negative entries, such as image data, purchase histories, and other types of count data. In NMF, a non-negative matrix is decomposed into the product of two non-negative matrices, and the a"
701,,Collaborative Low-Rank Adaptation for Pre-Trained Vision Transformers,Zheng Liu; Jinchao Zhu; Gao Huang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24603v1,https://arxiv.org/pdf/2512.24603v1,arxiv,,"Low-rank adaptation (LoRA) has achieved remarkable success in fine-tuning pre-trained vision transformers for various downstream tasks. Existing studies mainly focus on exploring more parameter-efficient strategies or more effective representation learning schemes. However, these methods either sacr"
702,,"Secure Digital Semantic Communications: Fundamentals, Challenges, and Opportunities",Weixuan Chen; Qianqian Yang; Yuanyuan Jia; Junyu Pan; Shuo Shao,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24602v1,https://arxiv.org/pdf/2512.24602v1,arxiv,,"Semantic communication (SemCom) has emerged as a promising paradigm for future wireless networks by prioritizing task-relevant meaning over raw data delivery, thereby reducing communication overhead and improving efficiency. However, shifting from bit-accurate transmission to task-oriented delivery "
703,,Recursive Language Models,Alex L. Zhang; Tim Kraska; Omar Khattab,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24601v1,https://arxiv.org/pdf/2512.24601v1,arxiv,,"We study allowing large language models (LLMs) to process arbitrarily long prompts through the lens of inference-time scaling. We propose Recursive Language Models (RLMs), a general inference strategy that treats long prompts as part of an external environment and allows the LLM to programmatically "
704,,Dynamic Phase Transitions in Periodically Driving 1D Ising Model,Yuanyuan Cheng; Yuxia Zhang; Tianhui Qiu; Peipei Xin; Bao-Ming Xu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24600v1,https://arxiv.org/pdf/2512.24600v1,arxiv,,"This work investigates dynamical quantum phase transitions (DQPTs) in a one-dimensional Ising model subjected to a periodically modulated transverse field. In contrast to sudden quenches, we demonstrate that DQPTs can be induced in two distinct ways. First, when the system remains within a given pha"
705,,A Tale of 1001 LoC: Potential Runtime Error-Guided Specification Synthesis for Verifying Large-Scale Programs,Zhongyi Wang; Tengjie Lin; Mingshuai Chen; Haokun Li; Mingqi Yang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24594v1,https://arxiv.org/pdf/2512.24594v1,arxiv,,"Fully automated verification of large-scale software and hardware systems is arguably the holy grail of formal methods. Large language models (LLMs) have recently demonstrated their potential for enhancing the degree of automation in formal verification by, e.g., generating formal specifications as "
706,,3D Semantic Segmentation for Post-Disaster Assessment,Nhut Le; Maryam Rahnemoonfar,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24593v1,https://arxiv.org/pdf/2512.24593v1,arxiv,,"The increasing frequency of natural disasters poses severe threats to human lives and leads to substantial economic losses. While 3D semantic segmentation is crucial for post-disaster assessment, existing deep learning models lack datasets specifically designed for post-disaster environments. To add"
707,,SliceLens: Fine-Grained and Grounded Error Slice Discovery for Multi-Instance Vision Tasks,Wei Zhang; Chaoqun Wang; Zixuan Guan; Sam Kao; Pengfei Zhao,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24592v1,https://arxiv.org/pdf/2512.24592v1,arxiv,,"Systematic failures of computer vision models on subsets with coherent visual patterns, known as error slices, pose a critical challenge for robust model evaluation. Existing slice discovery methods are primarily developed for image classification, limiting their applicability to multi-instance task"
708,,Improving Few-Shot Change Detection Visual Question Answering via Decision-Ambiguity-guided Reinforcement Fine-Tuning,Fuyu Dong; Ke Li; Di Wang; Nan Luo; Yiming Zhang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24591v1,https://arxiv.org/pdf/2512.24591v1,arxiv,,Change detection visual question answering (CDVQA) requires answering text queries by reasoning about semantic changes in bi-temporal remote sensing images. A straightforward approach is to boost CDVQA performance with generic vision-language models via supervised fine-tuning (SFT). Despite recent p
709,,MultiRisk: Multiple Risk Control via Iterative Score Thresholding,Sunay Joshi; Yan Sun; Hamed Hassani; Edgar Dobriban,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24587v1,https://arxiv.org/pdf/2512.24587v1,arxiv,,"As generative AI systems are increasingly deployed in real-world applications, regulating multiple dimensions of model behavior has become essential. We focus on test-time filtering: a lightweight mechanism for behavior control that compares performance scores to estimated thresholds, and modifies o"
710,,Resource Allocation via Backscatter-Aware Transmit Antenna Selection for Low-PAPR and Ultra-Reliable WSNs,Rahul Gulia; Ashish Sheikh; Feyisayo Favour Popoola; Serisha Vadlamudi,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24583v1,https://arxiv.org/pdf/2512.24583v1,arxiv,,This paper addresses a fundamental physical layer conflict in hybrid Wireless Sensor Networks (WSNs) between high-throughput primary communication and the stringent power envelope requirements of passive backscatter sensors. We propose a Backscatter-Constrained Transmit Antenna Selection (BC-TAS) fr
711,,Robust Bayesian Dynamic Programming for On-policy Risk-sensitive Reinforcement Learning,Shanyu Han; Yangbo He; Yang Liu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24580v1,https://arxiv.org/pdf/2512.24580v1,arxiv,,We propose a novel framework for risk-sensitive reinforcement learning (RSRL) that incorporates robustness against transition uncertainty. We define two distinct yet coupled risk measures: an inner risk measure addressing state and cost randomness and an outer risk measure capturing transition dynam