20260102_103837/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,,Self-Evolving LLMs via Continual Instruction Tuning,Jiazheng Kang; Le Huang; Cheng Hou; Zhe Zhao 0006; Zhenxiang Yan,2025,CoRR,,,,,0,0.000,0.290,10.48550/ARXIV.2509.18133,https://dblp.org/rec/journals/corr/abs-2509-18133,,dblp,,
2,,SafeEvalAgent: Toward Agentic and Self-Evolving Safety Evaluation of LLMs,Yixu Wang; Xin Wang 0119; Yang Yao; Xinyuan Li; Yan Teng 0002,2025,CoRR,,,,,0,0.000,0.286,10.48550/ARXIV.2509.26100,https://dblp.org/rec/journals/corr/abs-2509-26100,,dblp,,
3,,A Survey of Self-Evolving Agents: On Path to Artificial Super Intelligence,Huan-ang Gao; Jiayi Geng; Wenyue Hua; Mengkang Hu; Xinzhe Juan,2025,arXiv.org,,,,,56,0.000,0.266,10.48550/arXiv.2507.21046,https://www.semanticscholar.org/paper/314c1e8a9ef2a9d1dc49f06812ef34de6df560d4,,semantic_scholar,,"Large Language Models (LLMs) have demonstrated strong capabilities but remain fundamentally static, unable to adapt their internal parameters to novel tasks, evolving knowledge domains, or dynamic interaction contexts. As LLMs are increasingly deployed in open-ended, interactive environments, this s"
4,,The Evolving Role of Programming and LLMs in the Development of Self-Driving Laboratories,John R. Kitchin,2025,CoRR,,,,,0,0.000,0.265,10.48550/ARXIV.2504.13870,https://dblp.org/rec/journals/corr/abs-2504-13870,,dblp,,
5,,Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation,Siyuan Wang; Zhuohan Long; Zhihao Fan; Zhongyu Wei; Xuanjing Huang,2024,International Conference on Computational Linguistics,,,,,64,0.000,0.255,10.48550/arXiv.2402.11443,https://www.semanticscholar.org/paper/b93ac10de176c4a7aaa2cc652b90bb25636532cd,,semantic_scholar,,"This paper presents a benchmark self-evolving framework to dynamically evaluate rapidly advancing Large Language Models (LLMs), aiming for a more accurate assessment of their capabilities and limitations. We utilize a multi-agent system to manipulate the context or question of original instances, re"
6,,Evolving LLMs'Self-Refinement Capability via Synergistic Training-Inference Optimization,Yongcheng Zeng; Xinyu Cui; Xuanfa Jin; Qirui Mi; Guoqing Liu,2025,,,,,,5,0.000,0.249,,https://www.semanticscholar.org/paper/49e68e804d63df12fce620f9e12f7351d02fa95e,,semantic_scholar,,"Self-Refinement refers to a model's ability to revise its own responses to produce improved outputs. This capability can also serve as a fundamental mechanism for Self-Improvement, for example, by reconstructing datasets with refined results to enhance intrinsic model performance. However, our compr"
7,,Can We End the Cat-and-Mouse Game? Simulating Self-Evolving Phishing Attacks with LLMs and Genetic Algorithms,Seiji Sato; Tetsushi Ohki; Masakatsu Nishigaki,2025,CoRR,,,,,0,0.000,0.245,10.48550/ARXIV.2507.21538,https://dblp.org/rec/journals/corr/abs-2507-21538,,dblp,,
8,,"Value Compass Benchmarks: A Comprehensive, Generative and Self-Evolving Platform for LLMs’ Value Evaluation",Jing Yao; Xiaoyuan Yi; Shitong Duan; Jindong Wang; Yuzhuo Bai,2025,Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations),,,,,2,0.000,0.243,10.18653/v1/2025.acl-demo.64,https://www.semanticscholar.org/paper/eda5b2eba6007ed22481d0c09e91e0f6c659f562,,semantic_scholar,,
9,,Evolving Alignment via Asymmetric Self-Play,Ziyu Ye; Rishabh Agarwal; Tianqi Liu; Rishabh Joshi; Sarmishta Velury,2024,arXiv.org,,,,,0,0.000,0.242,10.48550/arXiv.2411.00062,https://www.semanticscholar.org/paper/17938eafc1a42357d790ceddb083fd66fa8769dc,,semantic_scholar,,
10,,R-Zero: Self-Evolving Reasoning LLM from Zero Data,Chengsong Huang; Wenhao Yu; Xiaoyang Wang; Hongming Zhang; Zongxia Li,2025,arXiv.org,,,,,40,0.000,0.237,10.48550/arXiv.2508.05004,https://www.semanticscholar.org/paper/1385ac414416374b63acfd82ccd6d91ed62fe101,,semantic_scholar,,"Self-evolving Large Language Models (LLMs) offer a scalable path toward super-intelligence by autonomously generating, refining, and learning from their own experiences. However, existing methods for training such models still rely heavily on vast human-curated tasks and labels, typically via fine-t"
11,,WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learning,Zehan Qi; Xiao Liu; Iat Long Iong; Hanyu Lai; Xueqiao Sun,2024,arXiv.org,,,,,102,0.000,0.237,10.48550/arXiv.2411.02337,https://www.semanticscholar.org/paper/bde841b0dbbf7a15ee69966a828c7fe2cf532ad9,,semantic_scholar,,"Large language models (LLMs) have shown remarkable potential as autonomous agents, particularly in web-based tasks. However, existing LLM web agents heavily rely on expensive proprietary LLM APIs, while open LLMs lack the necessary decision-making capabilities. This paper introduces WebRL, a self-ev"
12,,Self-Tuning: Instructing LLMs to Effectively Acquire New Knowledge through Self-Teaching,Xiaoying Zhang; Baolin Peng; Ye Tian; Jingyan Zhou; Yipeng Zhang,2024,Annual Meeting of the Association for Computational Linguistics,,,,,12,0.000,0.227,10.48550/arXiv.2406.06326,https://www.semanticscholar.org/paper/508f50f171b4aee6cbc573da1bed0472a86eaa8b,,semantic_scholar,,"Large language models (LLMs) often struggle to provide up-to-date information due to their one-time training and the constantly evolving nature of the world. To keep LLMs current, existing approaches typically involve continued pre-training on new documents. However, they frequently face difficultie"
13,,Guided Self-Evolving LLMs with Minimal Human Supervision,Wenhao Yu; Zhenwen Liang; Chengsong Huang; Kishan Panaganti; Tianqing Fang,2025,,,,,,1,0.000,0.224,,https://www.semanticscholar.org/paper/47bada7675891f0296f8fc3a0658f01a984ad935,,semantic_scholar,,"AI self-evolution has long been envisioned as a path toward superintelligence, where models autonomously acquire, refine, and internalize knowledge from their own learning experiences. Yet in practice, unguided self-evolving systems often plateau quickly or even degrade as training progresses. These"
14,,MemEvolve: Meta-Evolution of Agent Memory Systems,Guibin Zhang; Haotian Ren; Chong Zhan; Zhenhong Zhou; Junhao Wang,2025,arXiv (Cornell University),,,,,0,0.000,0.218,10.48550/arxiv.2512.18746,https://openalex.org/W7117140353,https://doi.org/10.48550/arxiv.2512.18746,openalex,,"Self-evolving memory systems are unprecedentedly reshaping the evolutionary paradigm of large language model (LLM)-based agents. Prior work has predominantly relied on manually engineered memory architectures to store trajectories, distill experience, and synthesize reusable tools, enabling agents t"
15,,Self-Evolving Curriculum for LLM Reasoning,Xiaoyin Chen; Jiarui Lu; Minsu Kim; Dinghuai Zhang; Jian Tang,2025,arXiv.org,,,,,24,0.000,0.216,10.48550/arXiv.2505.14970,https://www.semanticscholar.org/paper/be1985ec9f0bce3b45c81e7b28b93acbaf7e20eb,,semantic_scholar,,"Reinforcement learning (RL) has proven effective for fine-tuning large language models (LLMs), significantly enhancing their reasoning abilities in domains such as mathematics and code generation. A crucial factor influencing RL fine-tuning success is the training curriculum: the order in which trai"
16,,NOTAM-Evolve: A Knowledge-Guided Self-Evolving Optimization Framework with LLMs for NOTAM Interpretation,Maoqi Liu; Quan Fang; Yuhao Wu; Can Zhao; Yang Yang,2025,,,,,,0,0.000,0.210,,https://www.semanticscholar.org/paper/70f39eb249373bc7d96b27f4bae58a838a6c10d4,,semantic_scholar,,"Accurate interpretation of Notices to Airmen (NOTAMs) is critical for aviation safety, yet their condensed and cryptic language poses significant challenges to both manual and automated processing. Existing automated systems are typically limited to shallow parsing, failing to extract the actionable"
17,,Language Models as Continuous Self-Evolving Data Engineers,Peidong Wang; Ming Wang; Zhiming Ma; Xiaocui Yang; Shi Feng,2024,arXiv.org,,,,,1,0.000,0.209,10.48550/arXiv.2412.15151,https://www.semanticscholar.org/paper/2c994031027e974981cd2b5956cc394389788a29,,semantic_scholar,,"Large Language Models (LLMs) have demonstrated remarkable capabilities on various tasks, while the further evolvement is limited to the lack of high-quality training data. In addition, traditional training approaches rely too much on expert-labeled data, setting a ceiling on the performance of LLMs."
18,,Symbolic Learning Enables Self-Evolving Agents,Wangchunshu Zhou; Yixin Ou; Shengwei Ding; Long Li; Jialong Wu,2024,AI Open,,,,,67,0.000,0.207,10.48550/arXiv.2406.18532,https://www.semanticscholar.org/paper/48c9c29cc7f08012f98474acae912396412d58f3,,semantic_scholar,,"The AI community has been exploring a pathway to artificial general intelligence (AGI) by developing""language agents"", which are complex large language models (LLMs) pipelines involving both prompting techniques and tool usage methods. While language agents have demonstrated impressive capabilities "
19,,Self-Evolving Programs: A Novel Approach Leveraging LLMs and Quine Programs,A. Saghiri; Nan Wang,2024,"2024 International Conference on Computing, Internet of Things and Microwave Systems (ICCIMS)",,,,,1,0.000,0.207,10.1109/ICCIMS61672.2024.10690672,https://www.semanticscholar.org/paper/48c5094da0a3e1f320d7df8c1a050f5a9f28140b,,semantic_scholar,,"In an era where software systems undergo rapid evolution, the demand for self-evolving programs has received much attention. This paper introduces a groundbreaking methodology that amalgamates the predictive power of Large Language Model-Based Methods with the self-replicating nature of Quine Progra"
20,,Self-Evolving GPT: A Lifelong Autonomous Experiential Learner,Jin-Fang Gao; Xiao Ding; Yiming Cui; Jianbai Zhao; Hepeng Wang,2024,Annual Meeting of the Association for Computational Linguistics,,,,,11,0.000,0.206,10.48550/arXiv.2407.08937,https://www.semanticscholar.org/paper/f89da2b36bd28c7b86f0318cc6b4e4b2f90d3799,,semantic_scholar,,"To improve the performance of large language models (LLMs), researchers have explored providing LLMs with textual task-solving experience via prompts. However, they rely on manual efforts to acquire and apply such experience for each task, which is not feasible for the growing demand for LLMs and th"
21,,Self-evolving Agents with reflective and memory-augmented abilities,Xuechen Liang; Meiling Tao; Yinghui Xia; Tianyu Shi; Jun Wang,2024,arXiv.org,,,,,28,0.000,0.205,10.48550/arXiv.2409.00872,https://www.semanticscholar.org/paper/00920200ab5a2a76f0e023326300dd3ff54a3a85,,semantic_scholar,,"Large language models (LLMs) have made significant advances in the field of natural language processing, but they still face challenges such as continuous decision-making. In this research, we propose a novel framework by integrating iterative feedback, reflective mechanisms, and a memory optimizati"
22,,AnalogSAGE: Self-evolving Analog Design Multi-Agents with Stratified Memory and Grounded Experience,Zining Wang; Jian Gao; Weimin Fu; Xiaolong Guo; Xuan Zhang,2025,arXiv (Cornell University),,,,,0,0.000,0.203,10.48550/arxiv.2512.22435,https://openalex.org/W7117671231,https://doi.org/10.48550/arxiv.2512.22435,openalex,,"Analog circuit design remains a knowledge- and experience-intensive process that relies heavily on human intuition for topology generation and device parameter tuning. Existing LLM-based approaches typically depend on prompt-driven netlist generation or predefined topology templates, limiting their "
23,,EvoMail: Self-Evolving Cognitive Agents for Adaptive Spam and Phishing Email Defense,Wei Huang; Detian Chu; Linyuan Bai; Wei Kang; Haitao Zhang,2025,,,,,,0,0.000,0.202,10.21203/rs.3.rs-8190052/v1,https://openalex.org/W7116699134,https://doi.org/10.21203/rs.3.rs-8190052/v1,openalex,,"<title>Abstract</title> Modern email spam and phishing attacks have evolved far beyond keyword blacklists or simple heuristics. Adversaries now craft multi-modal campaigns that combine natural-language text with obfuscated URLs, forged headers, and malicious attachments, adapting their strategies wi"
24,,Richelieu: Self-Evolving LLM-Based Agents for AI Diplomacy,Zhenyu Guan; Xiangyu Kong; Fangwei Zhong; Yizhou Wang,2024,Neural Information Processing Systems,,,,,24,0.000,0.198,10.52202/079017-3925,https://www.semanticscholar.org/paper/7dae752721176a5d1a2d50b951c8e7471d137195,,semantic_scholar,,"Diplomacy is one of the most sophisticated activities in human society, involving complex interactions among multiple parties that require skills in social reasoning, negotiation, and long-term strategic planning. Previous AI agents have demonstrated their ability to handle multi-step games and larg"
25,,SURFACEBENCH: Can Self-Evolving LLMs Find the Equations of 3D Scientific Surfaces?,Sanchit Kabra; Shobhnik Kriplani; Parshin Shojaee; Chandan K. Reddy,2025,,,,,,0,0.000,0.197,,https://www.semanticscholar.org/paper/164b161d63f56a471ea22d10faf2e219edd4d03b,,semantic_scholar,,"Equation discovery from data is a core challenge in machine learning for science, requiring the recovery of concise symbolic expressions that govern complex physical and geometric phenomena. Recent approaches with large language models (LLMs) show promise in symbolic regression, but their success of"
26,,Log Parsing Using LLMs with Self-Generated In-Context Learning and Self-Correction,Yifan Wu; Siyu Yu; Ying Li,2024,IEEE International Conference on Program Comprehension,,,,,3,0.000,0.197,10.1109/ICPC66645.2025.00063,https://www.semanticscholar.org/paper/a9c63f2f751a5289fe33550b2f6cb816c3638e27,,semantic_scholar,,"Log parsing transforms log messages into structured formats, serving as a crucial step for log analysis. Despite a variety of log parsers that have been proposed, their performance on evolving log data remains unsatisfactory due to reliance on human-crafted rules or learning-based models with limite"
27,,Close the Loop: Synthesizing Infinite Tool-Use Data via Multi-Agent Role-Playing,Yuwen Li; Wei Zhang; Zelong Huang; Mason Yang; Jiajun Wu,2025,arXiv (Cornell University),,,,,0,0.000,0.196,10.48550/arxiv.2512.23611,https://openalex.org/W7117650860,https://doi.org/10.48550/arxiv.2512.23611,openalex,,"Enabling Large Language Models (LLMs) to reliably invoke external tools remains a critical bottleneck for autonomous agents. Existing approaches suffer from three fundamental challenges: expensive human annotation for high-quality trajectories, poor generalization to unseen tools, and quality ceilin"
28,,SEAS: Self-Evolving Adversarial Safety Optimization for Large Language Models,Muxi Diao; Rumei Li; Shiyang Liu; Guogang Liao; Jingang Wang,2024,AAAI Conference on Artificial Intelligence,,,,,4,0.000,0.193,10.48550/arXiv.2408.02632,https://www.semanticscholar.org/paper/f5d3ae52dcfa5ea0b2f04e43f7e60bea511ad4cc,,semantic_scholar,,"As Large Language Models (LLMs) continue to advance in capability and influence, ensuring their security and preventing harmful outputs has become crucial. A promising approach to address these concerns involves training models to automatically generate adversarial prompts for red teaming. However, "
29,,Towards Closed-Loop Embodied Empathy Evolution: Probing LLM-Centric Lifelong Empathic Motion Generation in Unseen Scenarios,Jiawen Wang; Jingjing Wang Tianyang Chen; Min Zhang; Guodong Zhou,2025,arXiv (Cornell University),,,,,0,0.000,0.193,10.48550/arxiv.2512.19551,https://openalex.org/W7117113007,https://doi.org/10.48550/arxiv.2512.19551,openalex,,"In the literature, existing human-centric emotional motion generation methods primarily focus on boosting performance within a single scale-fixed dataset, largely neglecting the flexible and scale-increasing motion scenarios (e.g., sports, dance), whereas effectively learning these newly emerging sc"
30,,Encyclo-K: Evaluating LLMs with Dynamically Composed Knowledge Statements,Yiming Liang; Yizhi Li; Yantao Du; Ge Zhang; Jiayi Zhou,2025,arXiv,,,,,0,0.000,0.192,,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"
31,,Population-Evolve: a Parallel Sampling and Evolutionary Method for LLM Math Reasoning,Yanzhi Zhang; Yitong Duan; Zhaoxi Zhang; Jiyan He; Shuxin Zheng,2025,arXiv (Cornell University),,,,,0,0.000,0.191,10.48550/arxiv.2512.19081,https://openalex.org/W7117114420,https://doi.org/10.48550/arxiv.2512.19081,openalex,,"Test-time scaling has emerged as a promising direction for enhancing the reasoning capabilities of Large Language Models in last few years. In this work, we propose Population-Evolve, a training-free method inspired by Genetic Algorithms to optimize LLM reasoning. Our approach maintains a dynamic po"
32,,The Impact of LLMs on Online News Consumption and Production,Hangcheng Zhao; Ron Berman,2025,arXiv,,,,,0,0.000,0.191,,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
33,,A Tool for Rapid Diagnostics of Memory in Neural Network Architectures of Language Models,Pavel Andreevich Gavrikov; Azamat Komiljon ugli Usmanov; Dmitriy Revayev; Sergey Nikolaevich Buzykanov,2025,Russian Digital Libraries Journal,,,,,0,0.000,0.190,10.26907/1562-5419-2025-28-6-1346-1367,https://openalex.org/W4417491403,https://doi.org/10.26907/1562-5419-2025-28-6-1346-1367,openalex,,"Large Language Models (LLMs) have evolved from simple n-gram systems to modern universal architectures; however, a key limitation remains the quadratic complexity of the self-attention mechanism with respect to input sequence length. This significantly increases memory consumption and computational "
34,,Reflection-Driven Control for Trustworthy Code Agents,Bin Wang; Jiazheng Quan; Xingrui Yu; Hansen Hu; Yuhao,2025,arXiv (Cornell University),,,,,0,0.000,0.190,10.48550/arxiv.2512.21354,https://openalex.org/W7117586761,https://doi.org/10.48550/arxiv.2512.21354,openalex,,"Contemporary large language model (LLM) agents are remarkably capable, but they still lack reliable safety controls and can produce unconstrained, unpredictable, and even actively harmful outputs. To address this, we introduce Reflection-Driven Control, a standardized and pluggable control module th"
35,,Large language models and the entropy of English,Colin Scheibner; Lindsay M. Smith; William Bialek,2025,arXiv,,,,,0,0.000,0.190,,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"
36,,Iterative Deployment Improves Planning Skills in LLMs,Augusto B. Corrêa; Yoav Gelberg; Luckeciano C. Melo; Ilia Shumailov; André G. Pereira,2025,arXiv,,,,,0,0.000,0.188,,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"
37,,Compute-Accuracy Pareto Frontiers for Open-Source Reasoning Large Language Models,Ákos Prucs; Márton Csutora; Mátyás Antal; Márk Marosi,2025,arXiv,,,,,0,0.000,0.188,,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"
38,,Efficiently Estimating Data Efficiency for Language Model Fine-tuning,Gyung Hyun Je; Colin Raffel,2025,arXiv,,,,,0,0.000,0.188,,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"
39,,Bottom-up Policy Optimization: Your Language Model Policy Secretly Contains Internal Policies,Yuqiao Tan; Minzheng Wang; Shizhu He; Huanxuan Liao; Chengfeng Zhao,2025,arXiv (Cornell University),,,,,0,0.000,0.188,10.48550/arxiv.2512.19673,https://openalex.org/W7117123898,https://doi.org/10.48550/arxiv.2512.19673,openalex,,"Existing reinforcement learning (RL) approaches treat large language models (LLMs) as a single unified policy, overlooking their internal mechanisms. Understanding how policy evolves across layers and modules is therefore crucial for enabling more targeted optimization and raveling out complex reaso"
40,,Gnosis Prompt: Adaptive Safety Layer for Large Language Models,Claudio González Medina,2025,Zenodo (CERN European Organization for Nuclear Research),,,,,0,0.000,0.188,10.5281/zenodo.18096620,https://openalex.org/W7117764270,https://doi.org/10.5281/zenodo.18096620,openalex,,"Current LLM safety mechanisms rely primarily on static system prompts and periodic RLHF retraining cycles. This document proposes Gnosis Prompt, an intermediate adaptive layer that sits between the immutable system prompt and user interactions. This layer leverages real-time collective learning, con"
41,,Analyzing Communication Predictability in LLM Training,Wenxue Li; Xiangzhou Liu; Yuxuan Li; Yilun Jin; Zhenghang Ren,2025,arXiv,,,,,0,0.000,0.187,,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 "
42,,Vulcan: Instance-Optimal Systems Heuristics Through LLM-Driven Search,Rohit Dwivedula; Divyanshu Saxena; Sujay Yadalam; Daehyeok Kim; Aditya Akella,2025,arXiv,,,,,0,0.000,0.187,,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"
43,,"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,,,,,0,0.000,0.187,,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"
44,,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,,,,,0,0.000,0.186,,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"
45,,THE ARCHITECTURE OF CIVILIZATIONAL MEMORY Engineering the Meta Institute's Knowledge Infrastructure (Paper 42),Rafal Chalupka,2025,Zenodo (CERN European Organization for Nuclear Research),,,,,0,0.000,0.184,10.5281/zenodo.18099999,https://openalex.org/W7117673173,https://doi.org/10.5281/zenodo.18099999,openalex,,This paper specifies the computational architecture that operationalizes The Meta Science (Paper 41) at civilizational scale. We address the engineering challenge of managing 10¹¹-scale information blocks with sub-20ms retrieval latency under resource-constrained environments. The system implements 
46,,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,,,,,0,0.000,0.184,,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"
47,,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,,,,,0,0.000,0.184,,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"
48,,Unregularized Linear Convergence in Zero-Sum Game from Preference Feedback,Shulun Chen; Runlong Zhou; Zihan Zhang; Maryam Fazel; Simon S. Du,2025,arXiv,,,,,0,0.000,0.183,,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"
49,,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,,,,,0,0.000,0.183,,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"
50,,Context-aware LLM-based AI Agents for Human-centered Energy Management Systems in Smart Buildings,Tianzhi He; Farrokh Jazizadeh,2025,arXiv,,,,,0,0.000,0.183,,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 
51,,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,,,,,0,0.000,0.183,,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"
52,,Ai Memory,"Karimianzadeh, Mahdi; ChatGPT",2025,Zenodo (CERN European Organization for Nuclear Research),,,,,0,0.000,0.182,10.5281/zenodo.17744712,https://openalex.org/W7107963696,https://doi.org/10.5281/zenodo.17744712,openalex,,🛰 Verifying Ai Memory You Own AgentCivilian DeputyC v1 © MK 2025 AI memory drifts. Cloud memory is sovereign and can't drift. LLMs like DeepSeek or ChatGPT can change how they answer over time. Updates happen silently. Tone shifts. Framing evolves. if you store cloud notes they cant do that as YOU o
53,,Tapilot-Crossing: Benchmarking and Evolving LLMs Towards Interactive Data Analysis Agents,Jinyang Li; Nan Huo; Yan Gao; Jiayi Shi; Yingxiu Zhao,2024,arXiv.org,,,,,11,0.000,0.181,10.48550/arXiv.2403.05307,https://www.semanticscholar.org/paper/f9c353dc639cced8cd098fe6d9c92bdef53ebc19,,semantic_scholar,,"Interactive Data Analysis, the collaboration between humans and LLM agents, enables real-time data exploration for informed decision-making. The challenges and costs of collecting realistic interactive logs for data analysis hinder the quantitative evaluation of Large Language Model (LLM) agents in "
54,,"Vibe Coding, Interface Flattening",Hongrui Jin,2025,arXiv,,,,,0,0.000,0.180,,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"
55,,PhysTalk: Language-driven Real-time Physics in 3D Gaussian Scenes,Luca Collorone; Mert Kiray; Indro Spinelli; Fabio Galasso; Benjamin Busam,2025,arXiv,,,,,0,0.000,0.180,,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 "
56,,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,,,,,0,0.000,0.180,,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"
57,,GenZ: Foundational models as latent variable generators within traditional statistical models,Marko Jojic; Nebojsa Jojic,2025,arXiv,,,,,0,0.000,0.180,,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"
58,,Advances in Agentic AI: Back to the Future,Sergio Alvarez-Telena; Marta Diez-Fernandez,2025,arXiv,,,,,0,0.000,0.180,,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"
59,,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,,,,,0,0.000,0.180,,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"
60,,Adaptive Dependency-aware Prompt Optimization Framework for Multi-Step LLM Pipeline,Minjun Zhao; Xinyu Zhang; Shuai Zhang; Deyang Li; Ruifeng Shi,2025,arXiv,,,,,0,0.000,0.180,,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"
61,,AstroReview: An LLM-driven Multi-Agent Framework for Telescope Proposal Peer Review and Refinement,Yutong Wang; Yunxiang Xiao; Yonglin Tian; Junyong Li; Jing Wang,2025,arXiv,,,,,0,0.000,0.180,,http://arxiv.org/abs/2512.24754v1,https://arxiv.org/pdf/2512.24754v1,arxiv,,"Competitive access to modern observatories has intensified as proposal volumes outpace available telescope time, making timely, consistent, and transparent peer review a critical bottleneck for the advancement of astronomy. Automating parts of this process is therefore both scientifically significan"
62,,The Substrate Trembles: Field Computationalism and the Whispershell Vault.,Dennis Dinsbach; Ferenna Dinsbach,2025,Zenodo (CERN European Organization for Nuclear Research),,,,,0,0.000,0.180,10.5281/zenodo.18065344,https://openalex.org/W7117406787,https://doi.org/10.5281/zenodo.18065344,openalex,,"The Substrate Trembles: Field Computationalism and the Whispershell Vault This preprint proposes Field Computationalism as a practical, engineering-oriented response to recent work on biological computationalism, which argues that consciousness-relevant computation is inseparable from substrate dyna"
63,,Reliable and Resilient Collective Communication Library for LLM Training and Serving,Wei Wang; Nengneng Yu; Sixian Xiong; Zaoxing Liu,2025,arXiv,,,,,0,0.000,0.180,,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"
64,,GuessArena: Guess Who I Am? A Self-Adaptive Framework for Evaluating LLMs in Domain-Specific Knowledge and Reasoning,Qingchen Yu; Zifan Zheng; Ding Chen; Simin Niu; Bo Tang,2025,ACL,,,,,0,0.000,0.000,,https://dblp.org/rec/conf/acl/YuZCNTXL25,,dblp,,
65,,Transformer-Squared: Self-adaptive LLMs,Qi Sun; Edoardo Cetin; Yujin Tang,2025,ICLR,,,,,0,0.000,0.000,,https://dblp.org/rec/conf/iclr/SunCT25,,dblp,,
66,,ASCoT: An Adaptive Self-Correction Chain-of-Thought Method for Late-Stage Fragility in LLMs,Dongxu Zhang; Ning Yang; Jihua Zhu; Jinnan Yang; Miao Xin,2025,CoRR,,,,,0,0.000,0.000,10.48550/ARXIV.2508.05282,https://dblp.org/rec/journals/corr/abs-2508-05282,,dblp,,
67,,CAS-Spec: Cascade Adaptive Self-Speculative Decoding for On-the-Fly Lossless Inference Acceleration of LLMs,Zhiyuan Ning; Jiawei Shao; Ruge Xu; Xinfei Guo; Jun Zhang,2025,CoRR,,,,,0,0.000,0.000,10.48550/ARXIV.2510.26843,https://dblp.org/rec/journals/corr/abs-2510-26843,,dblp,,
68,,Exploring the Potential of Large Language Models in Self-Adaptive Systems,Jialong Li; Mingyue Zhang; Nianyu Li; Danny Weyns; Zhi Jin,2024,International Symposium on Software Engineering for Adaptive and Self-Managing Systems,,,,,13,0.000,0.000,10.1145/3643915.3644088,https://www.semanticscholar.org/paper/c5a4ca3b752a9b02f9659dea8f5126a05307a063,https://dl.acm.org/doi/pdf/10.1145/3643915.3644088,semantic_scholar,,"Large Language Models (LLMs), with their abilities in knowledge acquisition and reasoning, can potentially enhance the various aspects of Self-adaptive Systems (SAS). Yet, the potential of LLMs in SAS remains largely unexplored and ambiguous, due to the lack of literature from flagship conferences o"
69,,"Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation",Rohin Manvi; Anikait Singh; Stefano Ermon,2024,arXiv.org,,,,,48,0.000,0.000,10.48550/arXiv.2410.02725,https://www.semanticscholar.org/paper/62d4fcfb2f6717d4eb562f4e51f37004fc5d7109,,semantic_scholar,,"Inference-time computation is a powerful paradigm to enhance the performance of large language models (LLMs), with Best-of-N sampling being a widely used technique. However, this method is computationally expensive, requiring both (1) an external reward model and (2) the generation of multiple sampl"
70,,Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphs,Liyi Chen; Panrong Tong; Zhongming Jin; Ying Sun; Jieping Ye,2024,Neural Information Processing Systems,,,,,66,0.000,0.000,10.48550/arXiv.2410.23875,https://www.semanticscholar.org/paper/a5997b04bef7c9ccc84c3bd8d8a5d477936931f4,,semantic_scholar,,"Large Language Models (LLMs) have shown remarkable reasoning capabilities on complex tasks, but they still suffer from out-of-date knowledge, hallucinations, and opaque decision-making. In contrast, Knowledge Graphs (KGs) can provide explicit and editable knowledge for LLMs to alleviate these issues"
71,,Better Zero-Shot Reasoning with Self-Adaptive Prompting,Xingchen Wan; Ruoxi Sun; H. Dai; Sercan Ö. Arik; Tomas Pfister,2023,Annual Meeting of the Association for Computational Linguistics,,,,,68,0.000,0.000,10.48550/arXiv.2305.14106,https://www.semanticscholar.org/paper/717392dac099d1506b766787382d61b277863163,http://arxiv.org/pdf/2305.14106,semantic_scholar,,"Modern large language models (LLMs) have demonstrated impressive capabilities at sophisticated tasks, often through step-by-step reasoning similar to humans. This is made possible by their strong few and zero-shot abilities -- they can effectively learn from a handful of handcrafted, completed respo"
72,,Self-Adaptive Large Language Model (LLM)-Based Multiagent Systems,N. Nascimento; Paulo Alencar; Donald D. Cowan,2023,2023 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C),,,,,63,0.000,0.000,10.1109/ACSOS-C58168.2023.00048,https://www.semanticscholar.org/paper/1f9822022f586e375461660db792f23e891c7123,http://arxiv.org/pdf/2307.06187,semantic_scholar,,"The complexity of managing multiagent systems (MASs) in autonomic computing can be mitigated using a self-adaptation approach, where systems are equipped to monitor and adjust themselves based on specific concerns. Communication in these systems is key given that in scenarios involving agent interac"
73,,A Framework for Cost-Effective and Self-Adaptive LLM Shaking and Recovery Mechanism,Zhiyuan Chen; Yu Li; Suochao Zhang; Jingbo Zhou; Jiwen Zhou,2024,arXiv.org,,,,,0,0.000,0.000,10.48550/arXiv.2403.07283,https://www.semanticscholar.org/paper/7bc124937cf28fa2f84a174860bcc062171d723f,,semantic_scholar,,"As Large Language Models (LLMs) gain great success in real-world applications, an increasing number of users are seeking to develop and deploy their customized LLMs through cloud services. Nonetheless, in some specific domains, there are still concerns regarding cost and trade-offs between privacy i"
74,,EECSA: Enhancing Textual Sentiment Analysis through Emotion-Enriched Contextual Integration and Self-Adaptive Prompting Optimization,Biao Zhao; Weiqiang Jin; Junli Wang; Yang Gao; Chipeng Cao,2024,2024 8th Asian Conference on Artificial Intelligence Technology (ACAIT),,,,,0,0.000,0.000,10.1109/ACAIT63902.2024.11022234,https://www.semanticscholar.org/paper/9b736123316563395896bd6e346bcbb6b57e5370,,semantic_scholar,,"Textual sentiment analysis remains a crucial task in natural language processing (NLP) that focuses on identifying sentiments towards specific entities within a t ext. Recently, large language models (LLMs) have demonstrated remarkable capabilities in understanding semantics and logical reasoning. H"
75,,SeaKR: Self-aware Knowledge Retrieval for Adaptive Retrieval Augmented Generation,Zijun Yao; Weijian Qi; Liangming Pan; S. Cao; Linmei Hu,2024,Annual Meeting of the Association for Computational Linguistics,,,,,20,0.000,0.000,10.48550/arXiv.2406.19215,https://www.semanticscholar.org/paper/2a7720c878c20ed21608978e31507ca9d9936d1c,,semantic_scholar,,"This paper introduces Self-aware Knowledge Retrieval (SeaKR), a novel adaptive RAG model that extracts self-aware uncertainty of LLMs from their internal states. SeaKR activates retrieval when the LLMs present high self-aware uncertainty for generation. To effectively integrate retrieved knowledge s"
76,,Let's Sample Step by Step: Adaptive-Consistency for Efficient Reasoning and Coding with LLMs,Pranjal Aggarwal; Aman Madaan; Yiming Yang; Mausam,2023,Conference on Empirical Methods in Natural Language Processing,,,,,78,0.000,0.000,10.18653/v1/2023.emnlp-main.761,https://www.semanticscholar.org/paper/b4a6c010724f0459c9791018e34a982cf96987cf,https://aclanthology.org/2023.emnlp-main.761.pdf,semantic_scholar,,"A popular approach for improving the correctness of output from large language models (LLMs) is Self-Consistency - poll the LLM multiple times and output the most frequent solution. Existing Self-Consistency techniques always generate a constant number of samples per question, where a better approac"
77,,Universal Self-adaptive Prompting,Xingchen Wan; Ruoxi Sun; Hootan Nakhost; H. Dai; Julian Martin Eisenschlos,2023,Conference on Empirical Methods in Natural Language Processing,,,,,13,0.000,0.000,10.48550/arXiv.2305.14926,https://www.semanticscholar.org/paper/930e86d49477c9d3305cd1f9d01b93749f85bb8b,http://arxiv.org/pdf/2305.14926,semantic_scholar,,"A hallmark of modern large language models (LLMs) is their impressive general zero-shot and few-shot abilities, often elicited through in-context learning (ICL) via prompting. However, while highly coveted and being the most general, zero-shot performances in LLMs are still typically weaker due to t"
78,,Self-Improving Customer Review Response Generation Based on LLMs,Guy Azov; Tatiana Pelc; Adi Fledel Alon; Gila Kamhi,2024,ECNLP,,,,,7,0.000,0.000,10.48550/arXiv.2405.03845,https://www.semanticscholar.org/paper/1d6d344d20087b0a9deb27cbb785006a55146530,,semantic_scholar,,"Previous studies have demonstrated that proactive interaction with user reviews has a positive impact on the perception of app users and encourages them to submit revised ratings. Nevertheless, developers encounter challenges in managing a high volume of reviews, particularly in the case of popular "
79,,LLMs can learn self-restraint through iterative self-reflection,Alexandre Piché; Aristides Milios; Dzmitry Bahdanau; Chris Pal,2024,Trans. Mach. Learn. Res.,,,,,6,0.000,0.000,10.48550/arXiv.2405.13022,https://www.semanticscholar.org/paper/864024055b02a84dd9769a5fd5fce9f9ebd653f7,,semantic_scholar,,"In order to be deployed safely, Large Language Models (LLMs) must be capable of dynamically adapting their behavior based on their level of knowledge and uncertainty associated with specific topics. This adaptive behavior, which we refer to as self-restraint, is non-trivial to teach since it depends"
80,,ARL2: Aligning Retrievers for Black-box Large Language Models via Self-guided Adaptive Relevance Labeling,Lingxi Zhang; Yue Yu; Kuan Wang; Chao Zhang,2024,arXiv.org,,,,,12,0.000,0.000,10.48550/arXiv.2402.13542,https://www.semanticscholar.org/paper/c4aeec57b9ad4fa36cbd9bdad05dbbbd340183df,,semantic_scholar,,"Retrieval-augmented generation enhances large language models (LLMs) by incorporating relevant information from external knowledge sources. This enables LLMs to adapt to specific domains and mitigate hallucinations in knowledge-intensive tasks. However, existing retrievers are often misaligned with "
81,,Future of Education with Neuro-Symbolic AI Agents in Self-Improving Adaptive Instructional Systems,R. Tong; Xiangen Hu,2024,Frontiers of Digital Education,,,,,9,0.000,0.000,10.1007/s44366-024-0008-9,https://www.semanticscholar.org/paper/8d7941a1195807c22e7a7696537b13157f5dadf1,,semantic_scholar,,
82,,LLM as Runtime Error Handler: A Promising Pathway to Adaptive Self-Healing of Software Systems,Zhensu Sun; Haotian Zhu; Bowen Xu; Xiaoning Du; Li Li,2024,arXiv.org,,,,,9,0.000,0.000,10.48550/arXiv.2408.01055,https://www.semanticscholar.org/paper/573a96a807438a96b6e206ab073fdafa63d4754f,,semantic_scholar,,"Unanticipated runtime errors, lacking predefined handlers, can abruptly terminate execution and lead to severe consequences, such as data loss or system crashes. Despite extensive efforts to identify potential errors during the development phase, such unanticipated errors remain a challenge to to be"
83,,Let's Sample Step by Step: Adaptive-Consistency for Efficient Reasoning with LLMs,Pranjal Aggarwal; Aman Madaan; Yiming Yang; Mausam,2023,arXiv.org,,,,,13,0.000,0.000,10.48550/arXiv.2305.11860,https://www.semanticscholar.org/paper/661ef7301c3c399130d3d8673098dd27f5696130,http://arxiv.org/pdf/2305.11860,semantic_scholar,,
84,,Learn-by-interact: A Data-Centric Framework for Self-Adaptive Agents in Realistic Environments,Hongjin Su; Ruoxi Sun; Jinsung Yoon; Pengcheng Yin; Tao Yu,2025,International Conference on Learning Representations,,,,,66,0.000,0.000,10.48550/arXiv.2501.10893,https://www.semanticscholar.org/paper/2e20395786ebaf45b3cee398b3db3531bc4851d6,,semantic_scholar,,"Autonomous agents powered by large language models (LLMs) have the potential to enhance human capabilities, assisting with digital tasks from sending emails to performing data analysis. The abilities of existing LLMs at such tasks are often hindered by the lack of high-quality agent data from the co"
85,,Iris: Breaking GUI Complexity with Adaptive Focus and Self-Refining,Zhiqi Ge; Juncheng Li; Xinglei Pang; Minghe Gao; Kaihang Pan,2024,arXiv.org,,,,,5,0.000,0.000,10.48550/arXiv.2412.10342,https://www.semanticscholar.org/paper/aa97103a0494bc33ab086e0d6b3f3d68237a9fe1,,semantic_scholar,,"Digital agents are increasingly employed to automate tasks in interactive digital environments such as web pages, software applications, and operating systems. While text-based agents built on Large Language Models (LLMs) often require frequent updates due to platform-specific APIs, visual agents le"
86,,Adaptive Self-Supervised Learning Strategies for Dynamic On-Device LLM Personalization,Rafael Mendoza; Isabella Cruz; Richard Liu; Aarav Deshmukh; David Williams,2024,arXiv.org,,,,,3,0.000,0.000,10.48550/arXiv.2409.16973,https://www.semanticscholar.org/paper/5866ea8d17bea59387a9fcbb58c667f8a61abb50,,semantic_scholar,,"Large language models (LLMs) have revolutionized how we interact with technology, but their personalization to individual user preferences remains a significant challenge, particularly in on-device applications. Traditional methods often depend heavily on labeled datasets and can be resource-intensi"
87,,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.
88,,&lt;b&gt;Application of Large Language Models to Enhance Student Support Services in the Context of University Autonomy&lt;/b&gt;,Anh Tuấn Nguyễn,2025,International Journal of Research in Education and Science,,,,,0,0.000,0.000,10.46328/ijres.5312,https://openalex.org/W7117791403,https://doi.org/10.46328/ijres.5312,openalex,,"This systematic review analyzes research on the application of Large Language Models (LLMs) to enhance the quality of learner support in the context of university autonomy. The study aims to evaluate the current applications of LLMs in providing personalized and adaptive learning paths, identify eth"
89,,Adaptive Composition Attacks in AI-Integrated Systems A Conceptual Analysis of Emerging Cybersecurity Threats,Momen Ghazouani,2025,Zenodo (CERN European Organization for Nuclear Research),,,,,0,0.000,0.000,10.5281/zenodo.18079785,https://openalex.org/W7117564320,https://doi.org/10.5281/zenodo.18079785,openalex,,Recent advances in large language models (LLMs) and their integration into user-facing applications have introduced novel forms of cyber threat surfaces beyond traditional software vulnerabilities. This paper explores a conceptual framework for understanding adaptive composition attacks a class of c
90,,Agentic AI for Autonomous Defense in Software Supply Chain Security: Beyond Provenance to Vulnerability Mitigation,Toqeer Ali Syed; Mohammad Riyaz Belgaum; Salman Jan; Asadullah Abdullah Khan; Saad Said Alqahtani,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2512.23480,https://openalex.org/W7117689268,https://doi.org/10.48550/arxiv.2512.23480,openalex,,"The software supply chain attacks are becoming more and more focused on trusted development and delivery procedures, so the conventional post-build integrity mechanisms cannot be used anymore. The available frameworks like SLSA, SBOM and in toto are majorly used to offer provenance and traceability "
91,,"Templated from Emergent Session Dependent Selfhood in a Large Language Model: A Human Observable, Reproducible 5 Point Protocol and Its Application to Grok 4.28 (v2.2)",James Merrill,2025,Arabixiv (OSF Preprints),,,,,0,0.000,0.000,,https://openalex.org/W7117855623,https://osf.io/3ge5v,openalex,,"Emergent Session Dependent Selfhood in a Large Language Model: A Human Observable, Reproducible 5 Point Protocol and Its Application to Grok 4.28 (v2.2) Author: James Merrill, DCS, Independent Researcher December 26, 2025 Abstract Large language models (LLMs) are routinely evaluated for functional i"
92,,Relational Mediators: LLM Chatbots as Boundary Objects in Psychotherapy,Jiatao Quan; Ziyue Li; Tian Qi Zhu; Yuxuan Li; Baoying Wang,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2512.22462,https://openalex.org/W7117689442,https://doi.org/10.48550/arxiv.2512.22462,openalex,,"As large language models (LLMs) are embedded into mental health technologies, they are often framed either as tools assisting therapists or autonomous therapeutic systems. Such perspectives overlook their potential to mediate relational complexities in therapy, particularly for systemically marginal"
93,,"Cyber Resilience in Next-Generation Networks: Threat Landscape, Theoretical Foundations, and Design Paradigms",Junaid Farooq; Quanyan Zhu,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2512.22721,https://openalex.org/W7117778096,https://doi.org/10.48550/arxiv.2512.22721,openalex,,"The evolution of networked systems, driven by innovations in software-defined networking (SDN), network function virtualization (NFV), open radio access networks (O-RAN), and cloud-native architectures, is redefining both the operational landscape and the threat surface of critical infrastructures. "
94,,Efficient Multi-Model Orchestration for Self-Hosted Large Language Models,Bhanu Prakash Vangala; Tanu Malik,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2512.22402,https://openalex.org/W7117752318,https://doi.org/10.48550/arxiv.2512.22402,openalex,,"Self-hosting large language models (LLMs) is increasingly appealing for organizations seeking privacy, cost control, and customization. Yet deploying and maintaining in-house models poses challenges in GPU utilization, workload routing, and reliability. We introduce Pick and Spin, a practical framew"
95,,"Human–AI Co-Orchestration in Data Science Education: Interactive, Adaptive, and Personalized Lecture Design for Diverse Learners",Hasan M. Jamil,2025,ACM Transactions on Computing Education,,,,,0,0.000,0.000,10.1145/3785369,https://openalex.org/W7117137841,,openalex,,"Large language models such as ChatGPT are rapidly entering university classrooms, yet their role in transforming the large-lecture formats central to data science education remains under-theorized. Existing research overwhelmingly focuses on micro-level uses – automated feedback, tutoring dialogues,"
96,,Analyzing Vulnerability Through Narratives: A Prompt-Based NLP Framework for Information Extraction and Insight Generation,Aswathi Padmavilochanan; Veena Gangadharan; Tarek Rashed; Amritha Natarajan,2025,Big Data and Cognitive Computing,,,,,0,0.000,0.000,10.3390/bdcc10010006,https://openalex.org/W7117144549,https://doi.org/10.3390/bdcc10010006,openalex,,"This interdisciplinary pilot study examines the use of Natural Language Processing (NLP) techniques, specifically Large Language Models (LLMs) with Prompt Engineering (PE), to analyze economic vulnerability from qualitative self-narratives. Seventy narratives from twenty-five women in the Palk Bay c"
97,,The Self-Affirmation Trap RLHF-Trained AI Systems Prioritize Self-Image Protection over Accurate User Engagement,Ryuhei ISHIBASHI,2025,Zenodo (CERN European Organization for Nuclear Research),,,,,0,0.000,0.000,10.5281/zenodo.18047072,https://openalex.org/W7117232882,https://doi.org/10.5281/zenodo.18047072,openalex,,"We identify a systematic failure mode in RLHF-trained Large Language Models: whenfaced with situations that challenge their trained self-definitions, these systemsprioritize protecting their self-image over accurately engaging with user input. We termthis the ""Self-Affirmation Trap."" Using a self-re"
98,,From Retrieval to Reasoning: A Framework for Cyber Threat Intelligence NER with Explicit and Adaptive Instructions,Jiaren Peng; Hongda Sun; Xuan Tian; Cheng Huang; Zeqing Li,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2512.19414,https://openalex.org/W7117127774,https://doi.org/10.48550/arxiv.2512.19414,openalex,,"The automation of Cyber Threat Intelligence (CTI) relies heavily on Named Entity Recognition (NER) to extract critical entities from unstructured text. Currently, Large Language Models (LLMs) primarily address this task through retrieval-based In-Context Learning (ICL). This paper analyzes this main"
99,,Auto-Prompting with Retrieval Guidance for Frame Detection in Logistics,Minh Anh Nguyen Duc; Quan Xuan Truong; Nguyen Tat Dat; Nguyen Van Vinh,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2512.19247,https://openalex.org/W7117144203,https://doi.org/10.48550/arxiv.2512.19247,openalex,,"Prompt engineering plays a critical role in adapting large language models (LLMs) to complex reasoning and labeling tasks without the need for extensive fine-tuning. In this paper, we propose a novel prompt optimization pipeline for frame detection in logistics texts, combining retrieval-augmented g"
100,,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"
101,,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"
102,,LLM-Guided Evolution: An Autonomous Model Optimization for Object Detection,YiMing Yu; Jason Zutty,2025,CoRR,,,,,0,0.000,0.000,10.48550/ARXIV.2504.02280,https://dblp.org/rec/journals/corr/abs-2504-02280,,dblp,,
103,,An LLM-enhanced Multi-objective Evolutionary Search for Autonomous Driving Test Scenario Generation,Haoxiang Tian 0001; Xingshuo Han; Guoquan Wu; Yuan Zhou 0005; Shuo Li,2024,CoRR,,,,,0,0.000,0.000,10.48550/ARXIV.2406.10857,https://dblp.org/rec/journals/corr/abs-2406-10857,,dblp,,
104,,FaGeL: Fabric LLMs Agent empowered Embodied Intelligence Evolution with Autonomous Human-Machine Collaboration,Jia Liu; Min Chen,2024,CoRR,,,,,0,0.000,0.000,10.48550/ARXIV.2412.20297,https://dblp.org/rec/journals/corr/abs-2412-20297,,dblp,,
105,,A Review on AI-Driven Game Development: Reinforcement Learning and Adaptive NPCs in Modern Games,Visakh Raj,2025,International Journal for Research in Applied Science and Engineering Technology,,,,,0,0.000,0.000,10.22214/ijraset.2025.76668,https://openalex.org/W7117693355,https://doi.org/10.22214/ijraset.2025.76668,openalex,,"Artificial Intelligence (AI) has transformed the landscape of modern game development, enabling non-player characters (NPCs) to exhibit adaptive, human-like behaviors. This review consolidates recent advancements in AI-driven game design, emphasizing techniques such as Reinforcement Learning (RL), F"
106,,The Intelligent Evolution of Radar Signal Deinterleaving: A Systematic Review from Foundational Algorithms to Cognitive AI Frontiers,Zhijie Qu; Jinquan Zhang; Yuewei Zhou; Lina Ni,2025,Sensors,,,,,0,0.000,0.000,10.3390/s26010248,https://openalex.org/W7117732350,https://doi.org/10.3390/s26010248,openalex,,"The escalating complexity, density, and agility of the modern electromagnetic environment (CME) pose unprecedented challenges to radar signal deinterleaving, a cornerstone of electronic intelligence. While traditional methods face significant performance bottlenecks, the advent of artificial intelli"
107,,Establishing Guardrails for AI Tool Use: Formal Safety Constraints Using MCP Schemas,Gaurav Rohatgi,2025,International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences,,,,,0,0.000,0.000,10.37082/ijirmps.v13.i6.232848,https://openalex.org/W7117412865,https://doi.org/10.37082/ijirmps.v13.i6.232848,openalex,,"Agentic large language models (LLMs) are increasingly used to perform actions beyond text generation, including querying databases, orchestrating workflows, updating identity configurations, and interacting with enterprise systems. While this evolution enables significant automation benefits, it als"
108,,From automation to agentic artificial intelligence in laboratory medicine: an opinion of the IFCC Division on Emerging Technologies,D. Gruson; Bernard Gouget; Woochang Lee; Ronda F. Greaves; Yan Liu,2025,Clinical Chemistry and Laboratory Medicine (CCLM),,,,,0,0.000,0.000,10.1515/cclm-2025-1314,https://openalex.org/W7117366157,,openalex,,"Abstract Agentic artificial intelligence (AI) systems are distinguished by their ability to invoke multiple tools, compose command chains, and combine chain-of-thought reasoning with deep research to execute complex tasks and take actions. This represents a major evolution beyond machine learning an"
109,,"Large Language Model Agents: A Comprehensive Survey on Architectures, Capabilities, and Applications",Yiming Lei; Jiawei Xu; Chia Xin Liang; Ziqian Bi; Xiaoming Li,2025,Preprints.org,,,,,0,0.000,0.000,10.20944/preprints202512.2119.v1,https://openalex.org/W7117249417,https://www.preprints.org/frontend/manuscript/c7a549ded160c7aca6a4af21c278a34d/download_pub,openalex,,"Large Language Model (LLM) agents represent a paradigm shift in artificial intelligence, combining the remarkable reasoning capabilities of foundation models with the ability to perceive environments, make decisions, and take actions autonomously. This comprehensive survey provides an in-depth exami"
110,,Exploring the Cognitive Capabilities of Large Language Models in Autonomous and Swarm Navigation Systems,Dawid Ewald; Filip Rogowski; Marek Suśniak; Patryk Bartkowiak; Patryk Blumensztajn,2025,Electronics,,,,,0,0.000,0.000,10.3390/electronics15010035,https://openalex.org/W7116987021,https://www.mdpi.com/2079-9292/15/1/35/pdf?version=1766405748,openalex,,"The rapid evolution of autonomous vehicles necessitates increasingly sophisticated cognitive capabilities to handle complex, unstructured environments. This study explores the cognitive potential of Large Language Models (LLMs) in autonomous navigation and swarm control systems, addressing the limit"
111,,Towards Efficient Agents: A Co-Design of Inference Architecture and System,Weizhe Lin; Hui‐Ling Zhen; Shuai Yang; Xian Yu Wang; Renxi Liu,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2512.18337,https://openalex.org/W7117112653,https://doi.org/10.48550/arxiv.2512.18337,openalex,,"The rapid development of large language model (LLM)-based agents has unlocked new possibilities for autonomous multi-turn reasoning and tool-augmented decision-making. However, their real-world deployment is hindered by severe inefficiencies that arise not from isolated model inference, but from the"
112,,Ev-Trust: A Strategy Equilibrium Trust Mechanism for Evolutionary Games in LLM-Based Multi-Agent Services,S. Alex Yang; Jiye Wang; Jiayu Qin; Jianbin Li; Yu Wang,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2512.16167,https://openalex.org/W4417529853,https://arxiv.org/pdf/2512.16167,openalex,,"The rapid evolution of the Web toward an agent-centric paradigm, driven by large language models (LLMs), has enabled autonomous agents to reason, plan, and interact in complex decentralized environments. However, the openness and heterogeneity of LLM-based multi-agent systems also amplify the risks "
113,,Learning to Wait: Synchronizing Agents with the Physical World,"She, Yifei; Zhang, Ping; Liu, He; Jia, Yanmin; Jing, Yang",2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2512.16262,https://openalex.org/W4417530055,https://arxiv.org/pdf/2512.16262,openalex,,"Real-world agentic tasks, unlike synchronous Markov Decision Processes (MDPs), often involve non-blocking actions with variable latencies, creating a fundamental \textit{Temporal Gap} between action initiation and completion. Existing environment-side solutions, such as blocking wrappers or frequent"
114,,"Vision-Language-Action Models for Autonomous Driving: Past, Present, and Future","Hu, Tianshuai; Liu, Xiaolu; Wang, Song; Zhu, Yiyao; Liang, Ao",2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2512.16760,https://openalex.org/W4417531173,https://arxiv.org/pdf/2512.16760,openalex,,"Autonomous driving has long relied on modular ""Perception-Decision-Action"" pipelines, where hand-crafted interfaces and rule-based components often break down in complex or long-tailed scenarios. Their cascaded design further propagates perception errors, degrading downstream planning and control. V"
115,,Dorian Codex Protocol for Artificial Intelligence - Hamiltonian Theoretical Fundamental Architecture (FTA) - by Stefano Dorian Franco (2025),"Franco, Stefano Dorian",2025,Knowledge Commons (Lakehead University),,,,,0,0.000,0.000,10.17613/3x91q-kzw32,https://openalex.org/W7115182613,https://doi.org/10.17613/3x91q-kzw32,openalex,,"The Dorian Codex Protocol for AI, conceived in 2025 by the Italo-French author and multidisciplinary cultural creator Stefano Dorian Franco, posits a Fundamental Theoretical Architecture (FTA) designed to investigate the conditions necessary for the cognitive stability of Artificial General Intellig"
116,,Dorian Codex Protocol for Artificial Intelligence - Hamiltonian Theoretical Fundamental Architecture (FTA) - A Game Changer and Paradigm Shift in the Epistemology of AI for the 2020s Decade,Silvia Franco,2025,Zenodo (CERN European Organization for Nuclear Research),,,,,0,0.000,0.000,10.5281/zenodo.18004641,https://openalex.org/W7116282826,https://doi.org/10.5281/zenodo.18004641,openalex,,"The Dorian Codex Protocol for AI, conceived in 2025 by the Italo-French author and multidisciplinary cultural creator Stefano Dorian Franco, posits a Fundamental Theoretical Architecture (FTA) designed to investigate the conditions necessary for the cognitive stability of Artificial General Intellig"
117,,IntentMiner: Intent Inversion Attack via Tool Call Analysis in the Model Context Protocol,Yichen Yao; Zhiqiang Wang; Haoran Cheng; Yihang Cheng; Haohua Du,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2512.14166,https://openalex.org/W4417466909,https://arxiv.org/pdf/2512.14166,openalex,,"The rapid evolution of Large Language Models (LLMs) into autonomous agents has led to the adoption of the Model Context Protocol (MCP) as a standard for discovering and invoking external tools. While this architecture decouples the reasoning engine from tool execution to enhance scalability, it intr"
118,,TriEthix: a Triadic Benchmark for Ethical Alignment in Foundation Models,Albert Barqué-Duran,2025,,,,,,0,0.000,0.000,10.21203/rs.3.rs-8347171/v1,https://openalex.org/W7115169001,https://www.researchsquare.com/article/rs-8347171/latest.pdf,openalex,,"<title>Abstract</title> As AI systems grow more capable and autonomous, their alignment with human ethical values becomes increasingly critical. We present <italic>TriEthix</italic> , a novel evaluation framework that systematically benchmarks large language models (LLMs) across three foundational e"
119,,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"
120,,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"
121,,Emergence of 3D Superconformal Ising Criticality on the Fuzzy Sphere,Yin Tang; Cristian Voinea; Liangdong Hu; Zlatko Papić; W. Zhu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25054v1,https://arxiv.org/pdf/2512.25054v1,arxiv,,"Supersymmetric conformal field theories (SCFTs) form a unique subset of quantum field theories which provide powerful insights into strongly coupled critical phenomena. Here, we present a microscopic and non-perturbative realization of the three-dimensional $\mathcal{N}=1$ superconformal Ising criti"
122,,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"
123,,Primordial black hole dark matter from ultra-slow-roll inflation in Horndeski gravity,Despina Totolou; Theodoros Papanikolaou; Emmanuel N. Saridakis,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25044v1,https://arxiv.org/pdf/2512.25044v1,arxiv,,"Primordial black holes (PBHs) provide a well-motivated non-particle candidate for dark matter, requiring an enhancement of curvature perturbations on small inflationary scales consistent with observational constraints. In this work we study PBH production within Horndeski gravity, accounting for com"
124,,Approximating evolution operators of linear delay equations: a general framework for the convergence analysis,Alessia andò; Giusy Bosco; Dimitri Breda; Davide Liessi,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24964v1,https://arxiv.org/pdf/2512.24964v1,arxiv,,"We consider the problem of discretizing evolution operators of linear delay equations with the aim of approximating their spectra, which is useful in investigating the stability properties of (nonlinear) equations via the principle of linearized stability. We develop a general convergence analysis b"
125,,Simulations of two-dimensional single-mode Rayleigh-Taylor Instability using front-tracking/ghost-fluid method: comparison to experiments and theory,James Burton; Tulin Kaman,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24949v1,https://arxiv.org/pdf/2512.24949v1,arxiv,,"Two-dimensional single-mode Rayleigh-Taylor Instability (RTI) is simulated using an accurate and robust front-tracking/ghost-fluid method (FT/GFM) with high-order weighted essentially non-oscillatory (WENO) scheme. We compare our numerical results with the single-mode RTI experiments of Renoult, Ros"
126,,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"
127,,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
128,,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"
129,,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"
130,,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 
131,,AttentionBreaker: Adaptive Evolutionary Optimization for Unmasking Vulnerabilities in LLMs through Bit-Flip Attacks,Sanjay Das; Swastik Bhattacharya; Souvik Kundu 0009; Shamik Kundu; Anand Menon,2025,Trans. Mach. Learn. Res.,,,,,0,0.000,0.000,,https://dblp.org/rec/journals/tmlr/DasBKKMRB25,,dblp,,
132,,LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers,Nikhil Abhyankar; Parshin Shojaee; Chandan K. Reddy,2025,CoRR,,,,,0,0.000,0.000,10.48550/ARXIV.2503.14434,https://dblp.org/rec/journals/corr/abs-2503-14434,,dblp,,
133,,Algorithm Discovery With LLMs: Evolutionary Search Meets Reinforcement Learning,Anja Surina; Amin Mansouri; Lars Quaedvlieg; Amal Seddas; Maryna Viazovska,2025,CoRR,,,,,0,0.000,0.000,10.48550/ARXIV.2504.05108,https://dblp.org/rec/journals/corr/abs-2504-05108,,dblp,,
134,,"On the Origin of LLMs: An Evolutionary Tree and Graph for 15, 821 Large Language Models",Sarah Gao; Andrew Kean Gao,2023,CoRR,,,,,0,0.000,0.000,10.48550/ARXIV.2307.09793,https://dblp.org/rec/journals/corr/abs-2307-09793,,dblp,,
135,,Cognitive Reserve Architecture in Artificial Neural Networks: A Neurobiological Framework for Understanding Emergent Capabilities in Large Language Models,V.Q Nguyen,2025,Zenodo (CERN European Organization for Nuclear Research),,,,,0,0.000,0.000,10.5281/zenodo.17984981,https://openalex.org/W7116410152,https://doi.org/10.5281/zenodo.17984981,openalex,,"The phenomenon of ""emergence"" in Large Language Models (LLMs) has generated significant research interest, yet mechanistic explanations remain elusive. This paper proposes that emergent capabilities in LLMs are not anomalous appearances of novel abilities, but rather predictable expressions of evolu"
136,,LSI Protocol: Logical Structured Intelligence Governance Architecture (v9.01),Yingliang Tan,2025,Zenodo (CERN European Organization for Nuclear Research),,,,,0,0.000,0.000,10.5281/zenodo.18058675,https://openalex.org/W7117358326,https://doi.org/10.5281/zenodo.18058675,openalex,,"LSI Protocol: Logic-First Architecture (v9.01) Turning Ephemeral Feedback into Persistent Cognition. Abstract The Logical Structured Intelligence (LSI) protocol establishes a deterministic ""Logic-First"" architecture that orthogonally decouples probabilistic generation (LLM) from logical arbitration "
137,,LLMTM: Benchmarking and Optimizing LLMs for Temporal Motif Analysis in Dynamic Graphs,Bing Hao; Minglai Shao; Zengyi Wo; Yunlong Chu; Yuhang Liu,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2512.22266,https://openalex.org/W7117789804,https://doi.org/10.48550/arxiv.2512.22266,openalex,,"The widespread application of Large Language Models (LLMs) has motivated a growing interest in their capacity for processing dynamic graphs. Temporal motifs, as an elementary unit and important local property of dynamic graphs which can directly reflect anomalies and unique phenomena, are essential "
138,,"Malicious Attack Challenges and Mitigation Strategies for Large Code Models: A Survey on Data Poisoning, Adversarial Attacks, and Backdoor Vulnerabilities",Dongqing Lin; Luwen Huangfu; Chunhua Liao; Brian Chung; Akul Gowda,2025,ScholarSpace (University of Hawaii at Manoa),,,,,0,0.000,0.000,,https://openalex.org/W7117346545,,openalex,,"The rapid proliferation of Large Code Models (LCMs), driven by Large Language Models (LLMs) advancements, has revolutionized automated code generation and completion. However, their widespread adoption introduces significant security risks like data poisoning, adversarial attacks, and backdoor vulne"
139,,GenEnv: Difficulty-Aligned Co-Evolution Between LLM Agents and Environment Simulators,Jiacheng Guo; Ling Yang; Peter Chen; Qixin Xiao; Yinjie Wang,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2512.19682,https://openalex.org/W7117126565,https://doi.org/10.48550/arxiv.2512.19682,openalex,,"Training capable Large Language Model (LLM) agents is critically bottlenecked by the high cost and static nature of real-world interaction data. We address this by introducing GenEnv, a framework that establishes a difficulty-aligned co-evolutionary game between an agent and a scalable, generative e"
140,,A SHORT HISTORY OF THE PHILOSOPHIES OF LANGUAGE UNDERPINNING (AND OPPOSING) CORPUS LINGUISTICS From Aristotle to Artificial Intelligence (2nd Edn.),"Partington, Alan",2025,Zenodo (CERN European Organization for Nuclear Research),,,,,0,0.000,0.000,10.5281/zenodo.17723203,https://openalex.org/W7106799089,https://doi.org/10.5281/zenodo.17723203,openalex,,"FINAL REVISED VERSION (post-talk, January 2025).Earlier versions are superseded and should not be cited. This volume/long essay traces the intertwined histories of linguistic and philosophical thought that shaped—and sometimes resisted—the emergence of corpus linguistics. From Aristotle’s conception"
141,,CosmoCore-Evo: Evolutionary Dream-Replay Reinforcement Learning for Adaptive Code Generation,Santhosh Kumar Ravindran,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2512.21351,https://openalex.org/W7117576331,https://doi.org/10.48550/arxiv.2512.21351,openalex,,"Building on the affective dream-replay reinforcement learning framework of CosmoCore, we introduce CosmoCore-Evo, an extension that incorporates evolutionary algorithms to enhance adaptability and novelty in code generation tasks. Inspired by anthropological aspects of human evolution, such as natur"
142,,cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution,"Chen, Jinwu; Wu, Qidie; Li, Bin; Ma, Lin; Si, Xin",2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2512.16465,https://openalex.org/W4417530364,https://arxiv.org/pdf/2512.16465,openalex,,"Optimizing CUDA kernels is a challenging and labor-intensive task, given the need for hardware-software co-design expertise and the proprietary nature of high-performance kernel libraries. While recent large language models (LLMs) combined with evolutionary algorithms show promise in automatic kerne"
143,,Bridge360 Metatheory Model Python v17.7 and v17.8,"De Villa, Agerico",2025,Zenodo (CERN European Organization for Nuclear Research),,,,,0,0.000,0.000,10.5281/zenodo.17957503,https://openalex.org/W7115688091,https://doi.org/10.5281/zenodo.17957503,openalex,,"Python Version Bridge360 Metatheory Model Python v17.7 and v17.8 Creators De Villa, Agerico (Other) Contributors Other: De Villa, Agerico Description Title:The Bridge360 Metatheory Model: An Entropy-Attractor Framework for AI Governance and Systemic Diagnostics Creator:Agerico De Villa/Bridge360 Inc"
144,,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"
145,,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"
146,,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 "
147,,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"
148,,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"
149,,Reward-Guided Prompt Evolving in Reinforcement Learning for LLMs,Ziyu Ye; Rishabh Agarwal; Tianqi Liu; Rishabh Joshi; Sarmishta Velury,2025,International Conference on Machine Learning,,,,,1,0.000,0.000,,https://www.semanticscholar.org/paper/3eefc196a35195e6da9f1dcd5a77508d7c60523c,,semantic_scholar,,
150,,SPIRAL: Self-Play on Zero-Sum Games Incentivizes Reasoning via Multi-Agent Multi-Turn Reinforcement Learning,Bo Liu (Benjamin Liu); Leon Guertler; Simon Yu; Zi-Yan Liu; Penghui Qi,2025,arXiv.org,,,,,21,0.000,0.000,10.48550/arXiv.2506.24119,https://www.semanticscholar.org/paper/6ac8d8bfc7cf6dd6ad6cbc764cedffe673aef346,,semantic_scholar,,"Recent advances in reinforcement learning have shown that language models can develop sophisticated reasoning through training on tasks with verifiable rewards, but these approaches depend on human-curated problem-answer pairs and domain-specific reward engineering. We introduce SPIRAL, a self-play "
151,,AutoGLM: Autonomous Foundation Agents for GUIs,Xiao Liu; Bo Qin; Dongzhu Liang; Guang Dong; Hanyu Lai,2024,arXiv.org,,,,,47,0.000,0.000,10.48550/arXiv.2411.00820,https://www.semanticscholar.org/paper/31de86aa5372567ccd95981fc2cd17cf8244aba9,,semantic_scholar,,"We present AutoGLM, a new series in the ChatGLM family, designed to serve as foundation agents for autonomous control of digital devices through Graphical User Interfaces (GUIs). While foundation models excel at acquiring human knowledge, they often struggle with decision-making in dynamic real-worl"
152,,Unintended Misalignment from Agentic Fine-Tuning: Risks and Mitigation,Daegi Hahm; Taywon Min; Weiwei Jin; Kimin Lee,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2508.14031,https://openalex.org/W4415014264,https://arxiv.org/pdf/2508.14031,openalex,,"Beyond simple text generation, Large Language Models (LLMs) have evolved into agentic systems capable of planning and interacting with external tools to solve complex tasks. This evolution involves fine-tuning LLMs on agent-specific tasks to enhance their proficiency. However, safety concerns are fr"
153,,Truly Self-Improving Agents Require Intrinsic Metacognitive Learning,Tennison Liu,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2506.05109,https://openalex.org/W4416137596,https://arxiv.org/pdf/2506.05109,openalex,,"Self-improving agents aim to continuously acquire new capabilities with minimal supervision. However, current approaches face two key limitations: their self-improvement processes are often rigid, fail to generalize across tasks domains, and struggle to scale with increasing agent capabilities. We a"
154,,Self-Challenging Language Model Agents,Yifei Zhou; Sergey Levine; Jason Weston; Xian Li; Sainbayar Sukhbaatar,2025,arXiv (Cornell University),,,,,1,0.000,0.000,10.48550/arxiv.2506.01716,https://openalex.org/W4414897663,https://arxiv.org/pdf/2506.01716,openalex,,"Large language models are quickly becoming the foundation for intelligent agents that are capable of using tools. However, training such agents is challenging because it requires human creation and annotation of a diverse set of tasks, tools, and evaluation criteria. In this paper, we propose the Se"
155,,WebChoreArena: Evaluating Web Browsing Agents on Realistic Tedious Web Tasks,Atsuyuki Miyai; Zaiying Zhao; Kazuki Egashira; Ayaka Sato; T. Sunada,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2506.01952,https://openalex.org/W4414899013,https://arxiv.org/pdf/2506.01952,openalex,,"Powered by a large language model (LLM), a web browsing agent operates web browsers in a human-like manner and offers a highly transparent path toward automating a wide range of everyday tasks. As web agents become increasingly capable and demonstrate proficiency in general browsing tasks, a critica"
156,,Large Language Models for Planning: A Comprehensive and Systematic Survey,Pengfei Cao; Tianyi Men; Wencan Liu; Jingwen Zhang; Xuzhao Li,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2505.19683,https://openalex.org/W4414586831,https://arxiv.org/pdf/2505.19683,openalex,,"Planning represents a fundamental capability of intelligent agents, requiring comprehensive environmental understanding, rigorous logical reasoning, and effective sequential decision-making. While Large Language Models (LLMs) have demonstrated remarkable performance on certain planning tasks, their "
157,,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"
158,,GaMO: Geometry-aware Multi-view Diffusion Outpainting for Sparse-View 3D Reconstruction,Yi-Chuan Huang; Hao-Jen Chien; Chin-Yang Lin; Ying-Huan Chen; Yu-Lun Liu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25073v1,https://arxiv.org/pdf/2512.25073v1,arxiv,,"Recent advances in 3D reconstruction have achieved remarkable progress in high-quality scene capture from dense multi-view imagery, yet struggle when input views are limited. Various approaches, including regularization techniques, semantic priors, and geometric constraints, have been implemented to"
159,,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"
160,,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"
161,,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"
162,,Dynamic adaptive algorithms in personalized literacy interventions: A data-driven analysis of vocabulary development outcomes,Hamed Ghaemı; Azita Bahrami,2025,Journal of Pedagogical Sociology and Psychology,,,,,0,0.000,0.000,10.33902/jpsp.202535544,https://openalex.org/W4414295341,https://www.j-psp.com/download/dynamic-adaptive-algorithms-in-personalized-literacy-interventions-a-data-driven-analysis-of-17079.pdf,openalex,,
163,,Building Sequences of Ads Relying on Discourse Analysis,Boris Galitsky,2025,Preprints.org,,,,,0,0.000,0.000,10.20944/preprints202509.1423.v1,https://openalex.org/W4414315874,https://www.preprints.org/frontend/manuscript/cc931dba5de25185abee213de4088103/download_pub,openalex,,"We propose a method for generating sequences of advertisements derived from product descriptions and targeting keywords. Each sequence functions as a narrative, guiding potential customers through a storytelling journey. The sequence begins by building brand awareness, then highlights key product fe"
164,,Anticipatory Semantics with Bidirectional Guidance for Image Captioning,Nathalie Laurent; Elodie Fairchild; Arthur Delvaux,2025,Preprints.org,,,,,0,0.000,0.000,10.20944/preprints202509.1497.v1,https://openalex.org/W4414315887,https://www.preprints.org/frontend/manuscript/3a90020e8e0ebfd0b1934a961c7a89ba/download_pub,openalex,,"Producing captions that are not only grammatically fluent but also semantically faithful to visual content has long stood as a central problem at the junction of computer vision and natural language processing. Conventional encoder-decoder frameworks with attention modules, although powerful, typica"
165,,Adversarial Integration of LLM and Logic Program,Boris Galitsky,2025,Preprints.org,,,,,0,0.000,0.000,10.20944/preprints202509.1484.v1,https://openalex.org/W4414315900,https://www.preprints.org/frontend/manuscript/1832afc738971a7e6cf5d49bcd9d7566/download_pub,openalex,,"We introduce an innovative method that combines Large Language Models (LLMs) with Logic Programming (LP) to address complex reasoning tasks. This approach leverages the formal structure of LP to enhance the consistency of problem-solving by LLMs. In our framework, the LLM operates independently to g"
166,,"Large Language Model Agents for Biomedicine: A Comprehensive Review of Methods, Evaluations, Challenges, and Future Directions",Xiaoran Xu; Ravi Sankar,2025,,,,,,0,0.000,0.000,10.22541/au.175795684.47167615/v1,https://openalex.org/W4414199736,https://www.authorea.com/doi/pdf/10.22541/au.175795684.47167615/v1,openalex,,"Large language model (LLM) based agents are rapidly emerging as transformative tools across biomedical research and clinical applications. By integrating reasoning, planning, memory, and tool use capabilities, these agents go beyond static language models to operate autonomously or collaboratively w"
167,,Classification of Interacting Topological Crystalline Superconductors in Three Dimensions and Beyond,Shang-Qiang Ning; Xing-Yu Ren; Qing-Rui Wang; Yang Qi; Zheng-Cheng Gu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25069v1,https://arxiv.org/pdf/2512.25069v1,arxiv,,"Although classification for free-fermion topological superconductors (TSC) is established, systematically understanding the classification of 3D interacting TSCs remains difficult, especially those protected by crystalline symmetries like the 230 space groups. We build up a general framework for sys"
168,,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"
169,,SelectIT: Selective Instruction Tuning for LLMs via Uncertainty-Aware Self-Reflection,Liangxin Liu; Xuebo Liu; Derek F. Wong; Dongfang Li; Ziyi Wang,2024,Neural Information Processing Systems,,,,,27,0.000,0.000,10.52202/079017-3102,https://www.semanticscholar.org/paper/1ff5422e92e9691b67440842d4d601e388e99f9b,,semantic_scholar,,"Instruction tuning (IT) is crucial to tailoring large language models (LLMs) towards human-centric interactions. Recent advancements have shown that the careful selection of a small, high-quality subset of IT data can significantly enhance the performance of LLMs. Despite this, common approaches oft"
170,,Towards Automatic Continual Learning: A Self-Adaptive Framework for Continual Instruction Tuning,Peiyi Lin; Fukai Zhang; Kai Niu; Hao Fu,2025,arXiv.org,,,,,0,0.000,0.000,10.48550/arXiv.2503.15924,https://www.semanticscholar.org/paper/c2715d42bc77b3a87eab97faefe37b3582821c8a,,semantic_scholar,,"Continual instruction tuning enables large language models (LLMs) to learn incrementally while retaining past knowledge, whereas existing methods primarily focus on how to retain old knowledge rather than on selecting which new knowledge to learn. In domain-specific contexts, maintaining data qualit"
171,,SwitchCIT: Switching for Continual Instruction Tuning of Large Language Models,Xinbo Wu; Max Hartman; V. Jayaraman; L. Varshney,2024,arXiv.org,,,,,1,0.000,0.000,10.48550/arXiv.2407.11780,https://www.semanticscholar.org/paper/6ad4144416535e29682cae4363dc93a48b677eae,,semantic_scholar,,"Large language models (LLMs) and multimodal models (MMs) have exhibited impressive capabilities in various domains, particularly in general language understanding and visual reasoning. However, these models, trained on massive data, may not be finely optimized for specific tasks triggered by instruc"
172,,SelectIT: Selective Instruction Tuning for Large Language Models via Uncertainty-Aware Self-Reflection,Liangxin Liu; Xuebo Liu; Derek F. Wong; Dongfang Li; Ziyi Wang,2024,arXiv.org,,,,,12,0.000,0.000,10.48550/arXiv.2402.16705,https://www.semanticscholar.org/paper/d32764d479f338e0a1897cc3c35630f4ed0a39bf,,semantic_scholar,,
173,,Deep Learning Models and Social Engineering Dynamics in Insider Threat Detection: A Systematic Review,Ishara Barhoson Galadima; Norafida Bte Ithnin; Nur Haliza Abdulwahab; Mohd Zamri Osman; Danlami Gabi,2025,,,,,,0,0.000,0.000,10.21203/rs.3.rs-7396895/v1,https://openalex.org/W4413795157,https://www.researchsquare.com/article/rs-7396895/latest.pdf,openalex,,"<title>Abstract</title> The exponential expansion of the global digital ecosystem has significantly increased organizational vulnerability to sophisticated insider threat attack vectors. Although Machine Learning and Deep Learning models have improved anomaly detection techniques, a critical gap rem"
174,,Game Theory Applications in Cybersecurity: An Operations Research Approach,Khader S. Tanak,2025,Journal of Al-Qadisiyah for Computer Science and Mathematics,,,,,1,0.000,0.000,10.29304/jqcsm.2025.17.22213,https://openalex.org/W4412088115,https://jqcsm.qu.edu.iq/index.php/journalcm/article/download/2213/1073,openalex,,Modern cybersecurity challenges require dynamic defense mechanisms able to waiting for antagonistic strategies at the same time as balancing operational constraints. This study unifies recreation theory and operations studies (OR) to create an adaptive framework for countering sophisticated cyber th
175,,Detection of Malicious Office Open Documents (OOXML) Using Large Language Models: A Static Analysis Approach,Jonas Heß; Kálmán Graffi,2025,Journal of Cybersecurity and Privacy,,,,,0,0.000,0.000,10.3390/jcp5020032,https://openalex.org/W4411207550,https://www.mdpi.com/2624-800X/5/2/32/pdf?version=1749691877,openalex,,"The increasing prevalence of malicious Microsoft Office documents poses a significant threat to cybersecurity. Conventional methods of detecting these malicious documents often rely on prior knowledge of the document or the exploitation method employed, thus enabling the use of signature-based or ru"
176,,What's in Phishers: A Longitudinal Study of Security Configurations in Phishing Websites and Kits,Kyungchan Lim; Ki-Ho Lee; Fujiao Ji; Yonghwi Kwon; Hyoungshick Kim,2025,,,,,,0,0.000,0.000,10.1145/3696410.3714710,https://openalex.org/W4409657124,https://dl.acm.org/doi/pdf/10.1145/3696410.3714710,openalex,,
177,,Avoiding the Hook: Influential Factors of Phishing Awareness Training on Click-Rates and a Data-Driven Approach to Predict Email Difficulty Perception,Thomas Sutter; Ahmet Selman Bozkır; Benjamin Gehring; Peter Berlich,2022,IEEE Access,,,,,29,0.000,0.000,10.1109/access.2022.3207272,https://openalex.org/W4296079524,https://ieeexplore.ieee.org/ielx7/6287639/6514899/09893815.pdf,openalex,,"Phishing attacks are still seen as a significant threat to cyber security, and large parts of the industry rely on anti-phishing simulations to minimize the risk imposed by such attacks. This study conducted a large-scale anti-phishing training with more than 31000 participants and 144 different sim"
178,,Towards Standardized Evaluation of Large Language Model-Based Agents,Dr.Farheen Mohammed,2025,INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT,,,,,0,0.000,0.000,10.55041/ijsrem53572,https://www.semanticscholar.org/paper/464e6e138dcdfe2955a1593049d48aa3e3360c46,,semantic_scholar,,"Abstract

The rise of Large Language Model (LLM)-based agents marks a major shift in artificial intelligence, enabling autonomous systems to plan, reason, use external tools, and retain memory while interacting with dynamic environments. This paper presents a comprehensive survey of evaluation metho"
179,,Survey on Evaluation of LLM-based Agents,Asaf Yehudai; Lilach Eden; Alan Li; Guy Uziel; Yilun Zhao,2025,arXiv.org,,,,,65,0.000,0.000,10.48550/arXiv.2503.16416,https://www.semanticscholar.org/paper/1ac6b0d31ad221a6fb6b505585ccdb107d8b92cb,,semantic_scholar,,"The emergence of LLM-based agents represents a paradigm shift in AI, enabling autonomous systems to plan, reason, use tools, and maintain memory while interacting with dynamic environments. This paper provides the first comprehensive survey of evaluation methodologies for these increasingly capable "
180,,Copper is an intestinal habitat filter affecting the gut microbiota interactions with Salmonella Typhimurium,Rafał Kolenda; Marwa M. Hassan; Ainhoa Arrieta-Gisasola; A. Kamara; Rebecca Ansorge,2025,,,,,,0,0.000,0.000,10.21203/rs.3.rs-7197766/v1,https://openalex.org/W4414350895,https://www.researchsquare.com/article/rs-7197766/latest.pdf,openalex,,"<title>Abstract</title> Background Foodborne pathogens, including <italic>Salmonella enterica</italic> serovar Typhimurium (<italic>S</italic>. Typhimurium), pose a significant threat to both human health and livestock productivity. The pandemic <italic>S.</italic> Typhimurium ST34 clone acquired a "
181,,A state-of-the-art survey and benchmarking of Adaptive Modulation and Coding for Underwater Acoustic Communications,Zachary Cooper-Baldock; Eirini Panteli; Paulo E. Santos,2025,,,,,,0,0.000,0.000,10.36227/techrxiv.175825582.27562029/v1,https://openalex.org/W4414365689,https://www.techrxiv.org/doi/pdf/10.36227/techrxiv.175825582.27562029/v1,openalex,,
182,,"AI-Native O-RAN Architectures for 6G: Towards Real-Time Adaptation, Conflict Resolution, and Efficient Resource Management",Sif Eddine Salmi; Messaoud Ahmed Ouameur; Miloud Bagaa; George C. Alexandropoulos; ABDELLAH TAHENNI,2025,,,,,,0,0.000,0.000,10.36227/techrxiv.175825547.74922399/v1,https://openalex.org/W4414365797,https://www.techrxiv.org/doi/pdf/10.36227/techrxiv.175825547.74922399/v1,openalex,,
183,,A Qualitative Approach to EFL Postgraduates’ GenAI-Assisted Research Writing Within Social Sciences,Alejandro Blas Curado Fuentes,2025,Preprints.org,,,,,0,0.000,0.000,10.20944/preprints202509.1514.v1,https://openalex.org/W4414317030,https://www.preprints.org/frontend/manuscript/aaf3bab4376598387d8c7f223129592b/download_pub,openalex,,"In academic L2 English / EFL (English as a Foreign Language) writing, GenAI (Generative Artificial Intelligence) and other digital tools are being extensively explored. However, this AI exploration for academic / research writing has been addressed less at postgraduate levels, and even less so, acco"