20260101_221047/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,RBssYVpQEr,RealMath: A Continuous Benchmark for Evaluating Language Models on Research-Level Mathematics,Jie Zhang; Cezara Petrui; Kristina Nikolić; Florian Tramèr,2025,NIPS 2025,Datasets & Benchmarks,Poster,evaluation,Mathematics;LLM reasoning,0,15.818,0.000,,https://openreview.net/forum?id=RBssYVpQEr,,offline_nips,,"Existing benchmarks for evaluating mathematical reasoning in large language models (LLMs) rely primarily on competition problems, formal proofs, or artificially challenging questions---failing to capture the nature of mathematics encountered in actual research environments. We introduce \textsc{Real"
2,vqBatdoexa,Finding the Cracks: Improving LLMs Reasoning with Paraphrastic Probing and Consistency Verification,,2026,ICLR 2026,main,Active,"foundation or frontier models, including LLMs",large language model;reasoning;critical token,0,15.598,0.000,,https://openreview.net/forum?id=vqBatdoexa,,offline_iclr,,"Large language models (LLMs) have demonstrated impressive performance across a variety of reasoning tasks in domains such as mathematics, coding, and planning, particularly when guided by chain-of-thought prompting to elicit intermediate reasoning steps. However, their problem-solving ability often "
3,nDTvP6tBMd,HARDMath: A Benchmark Dataset for Challenging Problems in Applied Mathematics,Jingxuan Fan; Sarah Martinson; Erik Y. Wang; Kaylie Hausknecht; Jonah Brenner,2025,ICLR 2025,main,Poster,datasets and benchmarks,math;benchmark;dataset;few-shot learning;reasoning,0,15.475,0.000,,https://iclr.cc/virtual/2025/poster/28419,https://openreview.net/pdf?id=nDTvP6tBMd,offline_iclr,,"Advanced applied mathematics problems are underrepresented in existing Large Language Model (LLM) benchmark datasets. To address this, we introduce $\textbf{HARDMath}$, a dataset inspired by a graduate course on asymptotic methods, featuring challenging applied mathematics problems that require anal"
4,71RJC2vTjQ,Do LLMs Perform Multilingual Multi-step Reasoning?,,2026,ICLR 2026,main,Active,"foundation or frontier models, including LLMs",Multilingual,0,15.475,0.000,,https://openreview.net/forum?id=71RJC2vTjQ,,offline_iclr,,"Ideally, large language models (LLMs) should be able to exploit information sources from all available languages to achieve strong performance for diverse tasks, including reasoning. However, most evaluations of multilingual reasoning focus on symbolic domains, e.g., mathematics and coding, and it r"
5,I4meJN28Ol,CellDuality: Unlocking Biological Reasoning in LLMs with Self-Supervised RLVR,,2026,ICLR 2026,main,Active,"applications to physical sciences (physics, chemistry, biology, etc.)",Reinforcement Learning;Biological Reasoning;Foundation Models;Single-Cell Biology,0,15.382,0.000,,https://openreview.net/forum?id=I4meJN28Ol,,offline_iclr,,"\begin{abstract}
Developing generalist large language models (LLMs) capable of complex biological reasoning is a central challenge in computational biology. While existing LLMs excel at predictive tasks like cell type annotation and logically-constrained problems, enabling open-ended and mechanistic"
6,PPsiS5nSlv,Improving Rationality in the Reasoning Process of Language Models through Self-playing Game,Pinzheng Wang; Juntao Li; Zecheng Tang; Haijia Gui; Min zhang,2025,ICML 2025,main,Poster,deep_learning->large_language_models,Self-play;Large Language Models;LLM Reasoning,0,15.330,0.000,,https://icml.cc/virtual/2025/poster/45387,https://openreview.net/pdf?id=PPsiS5nSlv,offline_icml,,"Large language models (LLMs) have demonstrated considerable reasoning abilities in various tasks such as mathematics and coding.
However, recent studies indicate that even the best models lack true comprehension of their reasoning processes.
In this paper, we explore how self-play can enhance the ra"
7,baNBqpzvMT,Mixing Expert Knowledge: Bring Human Thoughts Back To the Game of Go,Yichuan Ma; Linyang Li; Yongkang Chen; Peiji Li; Jiasheng Ye,2025,NIPS 2025,main,Poster,deep_learning,LLM reasoning;Reinforcement Learning;Go,0,15.289,0.000,,https://openreview.net/forum?id=baNBqpzvMT,,offline_nips,,"Large language models (LLMs) have demonstrated exceptional performance in reasoning tasks such as mathematics and coding, matching or surpassing human capabilities. However, these impressive reasoning abilities face significant challenges in specialized domains. Taking Go as an example, although Alp"
8,hDkLpu1E64,FEABench: Evaluating Language Models on Real World Physics Reasoning Ability,Nayantara Mudur; Hao Cui; Subhashini Venugopalan; Paul Raccuglia; Michael Brenner,2025,ICLR 2025,main,Reject,datasets and benchmarks,numerical analysis;finite element;benchmark;agents,0,15.029,0.000,,https://openreview.net/forum?id=hDkLpu1E64,,offline_iclr,,"Building precise simulations of the real world and invoking numerical solvers to answer quantitative problems is an essential requirement in engineering and science. We present FEABench, a benchmark to evaluate the ability of large language models (LLMs) and LLM agents to simulate and solve physics,"
9,fjEZ2LPceZ,CS-Bench: A Comprehensive Benchmark for Large Language Models towards Computer Science Mastery,Xiaoshuai Song; Muxi Diao; Guanting Dong; Zhengyang Wang; Yujia Fu,2025,ICLR 2025,main,Poster,datasets and benchmarks,large language model;evaluation;computer science,0,14.912,0.000,,https://iclr.cc/virtual/2025/poster/28862,https://openreview.net/pdf?id=fjEZ2LPceZ,offline_iclr,,"Large language models (LLMs) have demonstrated significant potential in advancing various fields of research and society. However, the current community of LLMs overly focuses on benchmarks for analyzing specific foundational skills (e.g. mathematics and code generation), neglecting an all-round eva"
10,V5tdi14ple,Don't Trust: Verify -- Grounding LLM Quantitative Reasoning with Autoformalization,Jin Peng Zhou; Charles E Staats; Wenda Li; Christian Szegedy; Kilian Q Weinberger,2024,ICLR 2024,main,Poster,"neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)",mathematical reasoning;autoformalization;automated theorem proving;quantitative reasoning,0,14.575,0.000,,https://iclr.cc/virtual/2024/poster/18508,https://openreview.net/pdf?id=V5tdi14ple,offline_iclr,,"Large language models (LLM), such as Google's Minerva and OpenAI's GPT families, are becoming increasingly capable of solving mathematical quantitative reasoning problems. However, they still make unjustified logical and computational errors in their reasoning steps and answers. In this paper, we le"
11,KBknLdXxTa,From Abstract to Contextual: What LLMs Still Cannot Do in Mathematics,,2026,ICLR 2026,main,Active,datasets and benchmarks,Large Language Models;Mathematical Reasoning;Evaluation,0,14.457,0.000,,https://openreview.net/forum?id=KBknLdXxTa,,offline_iclr,,"Large language models now solve many benchmark math problems at near‑expert levels, yet this progress has not fully translated into reliable performance in real‑world applications. We study this gap through contextual mathematical reasoning, where the mathematical core must be formulated from descri"
12,LlvOaj1bqF,Think in Games: Learning to Reason in Games via Reinforcement Learning with Large Language Models,Yi Liao; Yu Gu; Yuan Sui; Zining Zhu; Yifan Lu,2026,ICLR 2026,main,Withdraw,"foundation or frontier models, including LLMs",Large Language Model;Reinforcement Learning;Agent;Games,0,14.395,0.000,,https://openreview.net/forum?id=LlvOaj1bqF,,offline_iclr,,"Large language models (LLMs) excel at complex reasoning tasks such as mathematics and coding, yet they frequently struggle with simple interactive tasks that young children perform effortlessly. This discrepancy highlights a critical gap between declarative knowledge (knowing about something) and pr"
13,mvahXacGWg,Beyond Chemical QA: Evaluating LLM's Chemical Reasoning with Modular Chemical Operations,Li Hao; He CAO; Bin Feng; Daniel Shao; Xiangru Tang,2025,NIPS 2025,Datasets & Benchmarks,Poster,datasets_&_benchmarks_for_language,Large Language Models;Chain-of-Thoughts;Chemical Benchmark.,0,14.395,0.000,,https://openreview.net/forum?id=mvahXacGWg,,offline_nips,,"While large language models (LLMs) with Chain-of-Thought (CoT) reasoning excel in mathematics and coding, their potential for systematic reasoning in chemistry, a domain demanding rigorous structural analysis for real-world tasks like drug design and reaction engineering, remains untapped. Current b"
14,4PZMeopXzP,PRISM-Physics: Causal DAG-Based Process Evaluation for Physics Reasoning,,2026,ICLR 2026,main,Active,datasets and benchmarks,Physics Reasoning;Process-Level Evaluation;Symbolic Equivalence;Scientific Problem Solving,0,14.389,0.000,,https://openreview.net/forum?id=4PZMeopXzP,,offline_iclr,,"Benchmarks for competition-style reasoning have advanced evaluation in mathematics and programming, yet physics remains comparatively underexplored. Most existing physics benchmarks evaluate only final answers, which fail to capture reasoning processes, while recent stepwise methods rely on heuristi"
15,nQq4bMcP7L,SolidGeo: Measuring Multimodal Spatial Math Reasoning in Solid Geometry,Peijie Wang; Chao Yang; Zhong-Zhi Li; Fei Yin; Dekang Ran,2025,NIPS 2025,Datasets & Benchmarks,Poster,datasets_&_benchmarks_for_language,Solid Geometry;MLLM;Math Reasoning;Spatial Intelligence,0,14.338,0.000,,https://openreview.net/forum?id=nQq4bMcP7L,,offline_nips,,"Geometry is a fundamental branch of mathematics and plays a crucial role in evaluating the reasoning capabilities of multimodal large language models (MLLMs). However, existing multimodal mathematics benchmarks mainly focus on plane geometry and largely ignore solid geometry, which requires spatial "
16,v3DwQlyGbv,Paramanu-Ganita: An Efficient Pre-trained Generative Mathematics Language Model with Chain-of-Thought Instruction Fine-Tuning,Mitodru Niyogi; Arnab Bhattacharya,2025,ICLR 2025,main,Reject,"foundation or frontier models, including LLMs",reasoning;language models;pretraining;CoT fine-tuning;AI4Math,0,14.279,0.000,,https://openreview.net/forum?id=v3DwQlyGbv,,offline_iclr,,"In this paper, we pose the following question: whether domain specific pretraining of tiny generative language models from scratch with domain specialized tokenizer and Chain-of-Thought (CoT) instruction fine-tuning results in very competitive performance on mathematical reasoning than LLMs which ar"
17,IDSXUFQeZO5,NeuralPCG: Learning Preconditioner for Solving Partial Differential Equations with Graph Neural Network,Yichen Li; Tao Du; Peter Yichen Chen; Wojciech Matusik,2023,ICLR 2023,main,Reject,,Physics Simulation;Graph Neural Network;Applied Mathematics,0,14.210,0.000,,https://openreview.net/forum?id=IDSXUFQeZO5,,offline_iclr,,Fast and accurate partial differential equation (PDE) solvers empower scientific and engineering research. Classic numerical solvers provide unparalleled accuracy but often require extensive computation time. Machine learning solvers are significantly faster but lack convergence and accuracy guarant
18,bA51NZVNFT,Voice Evaluation of Reasoning Ability: Diagnosing the Modality-Induced Performance Gap,,2026,ICLR 2026,main,Active,datasets and benchmarks,voice language models;voice reasoning gap;real-time conversation,0,14.194,0.000,,https://openreview.net/forum?id=bA51NZVNFT,,offline_iclr,,"We present Voice Evaluation of Reasoning Ability (VERA), a benchmark for evaluating reasoning ability in voice-interactive systems under real-time conversational constraints. VERA comprises 2,931 voice-native episodes derived from established text benchmarks and organized into five tracks (Math, Web"
19,WvH8ZVw3m9,CombiGraph-Vis: A Multimodal Olympiad Benchmark for Discrete Mathematical Reasoning,Hamed Mahdavi; Pouria Mahdavinia; Alireza Farhadi; Pegah Mohammadipour; Samira Malek,2026,ICLR 2026,main,Withdraw,datasets and benchmarks,Mathematical reasoning;Multimodal benchmark;Discrete mathematics Benchmark;Olympiad Benchmark,0,14.191,0.000,,https://openreview.net/forum?id=WvH8ZVw3m9,,offline_iclr,,"Progress on math-reasoning benchmarks such as GSM8K and MATH500 has eroded their ability to discriminate among strong systems, motivating harder tests that separate capabilities more sharply. We introduce CombiGraph-Vis, an Olympiad-style benchmark of 1,135 short-answer, multiple-choice, and yes/no "
20,EVS7SeKBqI,Knowledge-to-Verification: Unlocking Reinforcement Learning with Verifiable Rewards for LLMs in Knowledge-Intensive Domains,,2026,ICLR 2026,main,Active,"foundation or frontier models, including LLMs",Large Language models;Reinforcement Learning with Verifiable Rewards;Knowledge-Intensive Domains;Reasoning,0,14.183,0.000,,https://openreview.net/forum?id=EVS7SeKBqI,,offline_iclr,,"Reinforcement learning with verifiable rewards (RLVR) has demonstrated promising potential to enhance the reasoning capabilities of large language models in domains such as mathematics and coding. However, its application has not been effectively extended to knowledge-intensive domains due to the is"
21,31pzK2VSQQ,VisioMath: Benchmarking Figure-based Mathematical Reasoning in LMMs,,2026,ICLR 2026,main,Active,datasets and benchmarks,Figure-based Mathematical Reasoning;Large Multimodal Models;Mathematical Benchmark,0,14.008,0.000,,https://openreview.net/forum?id=31pzK2VSQQ,,offline_iclr,,"Large Multimodal Models have achieved remarkable progress in integrating vision and language, enabling strong performance across perception, reasoning, and domain-specific tasks. However, their capacity to reason over multiple, visually similar inputs remains insufficiently explored. Such fine-grain"
22,yiSoT2pHfk,CLAWS:Creativity detection for LLM-generated solutions using Attention Window of Sections,Keuntae Kim; Eunhye Jeong; Sehyeon Lee; Seohee Yoon; Yong Suk Choi,2025,NIPS 2025,main,Poster,deep_learning,llm;reasoning;math;creativity;hallucination,0,13.802,0.000,,https://openreview.net/forum?id=yiSoT2pHfk,,offline_nips,,"Recent advances in enhancing the reasoning ability of Large Language Models (LLMs) have been remarkably successful. LLMs trained with Reinforcement Learning (RL) for reasoning demonstrate strong performance in challenging tasks such as mathematics and coding, even with relatively small model sizes. "
23,H1gR5iR5FX,Analysing Mathematical Reasoning Abilities of Neural Models,David Saxton; Edward Grefenstette; Felix Hill; Pushmeet Kohli,2019,ICLR 2019,main,Poster,,mathematics;dataset;algebraic;reasoning,0,13.718,0.000,,https://iclr.cc/virtual/2019/poster/933,https://openreview.net/pdf?id=H1gR5iR5FX,offline_iclr,"A dataset for testing mathematical reasoning (and algebraic generalization), and results on current sequence-to-sequence models.","Mathematical reasoning---a core ability within human intelligence---presents some unique challenges as a domain: we do not come to understand and solve mathematical problems primarily on the back of experience and evidence, but on the basis of inferring, learning, and exploiting laws, axioms, and sy"
24,wGqf7YMF8R,HDFlow: Enhancing LLM Complex Problem-Solving with Hybrid Thinking and Dynamic Workflows,Wenlin Yao; Haitao Mi; Dong Yu,2025,ICLR 2025,main,Reject,"foundation or frontier models, including LLMs",Large Language Models (LLMs);Complex Reasoning;Hybrid Thinking;Symbolic Reasoning,0,13.702,0.000,,https://openreview.net/forum?id=wGqf7YMF8R,,offline_iclr,,"Despite recent advancements in large language models (LLMs), their performance on complex reasoning problems requiring multi-step thinking and combining various skills is still limited. To address this, we propose a novel framework HDFlow for complex reasoning with LLMs that combines fast and slow t"
25,Dxns0cj15A,EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements,,2026,ICLR 2026,main,Active,datasets and benchmarks,Financial Large language models;Financial benchmark;Accounting fraud detection;Earnings forecast,0,13.684,0.000,,https://openreview.net/forum?id=Dxns0cj15A,,offline_iclr,,"Large Language Models (LLMs) have made remarkable progress, surpassing human performance on several benchmarks in domains such as mathematics and coding. A key driver of this progress has been the development of benchmark datasets. In contrast, the financial domain poses higher entry barriers due to"
26,TfwePIKzXJ,Scaling up Multi-Turn Off-Policy RL and Multi-Agent Tree Search for LLM Step-Provers,,2026,ICLR 2026,main,Active,"neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)",Large Language Models; Expert Iteration; Tree Search; Theorem Proving; Formal Mathematics,0,13.644,0.000,,https://openreview.net/forum?id=TfwePIKzXJ,,offline_iclr,,"The integration of Large Language Models (LLMs) with automated theorem proving has shown immense promise, yet is constrained by challenges in scaling up both training-time reinforcement learning (RL) and inference-time compute. This paper introduces BFS-Prover-V2, a step-level theorem proving system"
27,1lo778KztK,Scaling Physical Reasoning with the PHYSICS Dataset,Shenghe Zheng; Qianjia Cheng; Junchi Yao; Mengsong Wu; haonan he,2025,NIPS 2025,Datasets & Benchmarks,Poster,datasets_&_benchmarks_for_language,Physics Dataset;Physics Evaluation,0,13.615,0.000,,https://openreview.net/forum?id=1lo778KztK,,offline_nips,,"Large Language Models (LLMs) have achieved remarkable progress on advanced reasoning tasks such as mathematics and coding competitions. Meanwhile, physics, despite being both reasoning-intensive and essential to real-world understanding, received limited academic and industrial attention. This paper"
28,tmReo37kl2,IndiMathBench: Autoformalizing Mathematical Reasoning Problems with a Human Touch,,2026,ICLR 2026,main,Active,"neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)",autoformalization;automated theorem proving;human-ai collaboration;benchmark;lean,0,13.615,0.000,,https://openreview.net/forum?id=tmReo37kl2,,offline_iclr,,Reliable autoformalization remains an elusive goal even in the era of large language models (LLMs). Even the best LLMs struggle to translate natural language into formal constructs in languages like Lean. High-quality data has been a key bottleneck given the resource costs associated with manual cur
29,OEPP8T0zUX,TopoAlign: A Framework for Aligning Code to Math via Topological Decomposition,,2026,ICLR 2026,main,Active,"neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)",autoformalisation;formal reasoning;code data,0,13.608,0.000,,https://openreview.net/forum?id=OEPP8T0zUX,,offline_iclr,,"Large Language Models (LLMs) excel at both informal and formal (e.g. Lean 4) mathematical reasoning but still struggle with autoformalisation, the task of transforming informal into formal mathematical statements. Autoformalisation helps pair the informal reasoning of LLMs with formal proof assistan"
30,mb2rHLcKN5,SubgoalXL: Subgoal-based Expert Learning for Theorem Proving,Xueliang Zhao; Lin Zheng; Haige Bo; Changran Hu; Urmish Thakker,2025,ICLR 2025,main,Withdraw,"neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)",theorem proving;subgoal-based proofs;expert learning,0,13.461,0.000,,https://openreview.net/forum?id=mb2rHLcKN5,,offline_iclr,,"Formal theorem proving, a field at the intersection of mathematics and computer science, has seen renewed interest with advancements in large language models (LLMs). This paper introduces SubgoalXL, a novel approach that synergizes subgoal-based proofs with expert learning to enhance LLMs' capabilit"
31,XbiluWvVWO,TCMReasonSet: A Dataset for Explainable Medical Reasoning in Traditional Chinese Medicine,,2026,ICLR 2026,main,Active,"foundation or frontier models, including LLMs",Dataset;Traditional Chinese Medicine;Large Language Model Reasoning,0,13.444,0.000,,https://openreview.net/forum?id=XbiluWvVWO,,offline_iclr,,"Large language models (LLMs) excel in structured tasks such as mathematics and programming but remain limited in knowledge-intensive domains like Traditional Chinese Medicine (TCM), which require complex reasoning.The primary bottleneck stems from the scarcity of high-quality training corpora that a"
32,0fa6d6e73a,Convergence Properties of the K-Means Algorithms,Léon Bottou; Yoshua Bengio,1994,NIPS 1994,main,Poster,,,0,13.441,0.000,,https://papers.nips.cc/paper_files/paper/1994/hash/a1140a3d0df1c81e24ae954d935e8926-Abstract.html,https://papers.nips.cc/paper_files/paper/1994/file/a1140a3d0df1c81e24ae954d935e8926-Paper.pdf,offline_nips,,This paper studies the convergence properties of the well known  K-Means clustering algorithm. The K-Means algorithm can be de(cid:173) scribed either as a gradient descent algorithm or by slightly extend(cid:173) ing the mathematics of the EM algorithm to this hard threshold  case. We show that the
33,uNKlTQ8mBD,Learning Formal Mathematics From Intrinsic Motivation,Gabriel Poesia; David Broman; Nick Haber; Noah Goodman,2024,NIPS 2024,main,Oral,machine_learning_for_other_sciences_and_fields,reasoning;reinforcement learning;formal mathematics;logic,0,13.396,0.000,,https://neurips.cc/virtual/2024/poster/93276,https://openreview.net/pdf?id=uNKlTQ8mBD,offline_nips,,How did humanity coax mathematics from the aether? We explore the Platonic view that mathematics can be discovered from its axioms---a game of conjecture and proof. We describe an agent that jointly learns to pose challenging problems for itself (conjecturing) and solve them (theorem proving). Given
34,S1eZYeHFDS,Deep Learning For Symbolic Mathematics,Guillaume Lample; François Charton,2020,ICLR 2020,main,Spotlight,,symbolic;math;deep learning;transformers,0,13.125,0.000,,https://openreview.net/forum?id=S1eZYeHFDS,,offline_iclr,"We train a neural network to compute function integrals, and to solve complex differential equations.","Neural networks have a reputation for being better at solving statistical or approximate problems than at performing calculations or working with symbolic data. In this paper, we show that they can be surprisingly good at more elaborated tasks in mathematics, such as symbolic integration and solving"
35,9ZPegFuFTFv,miniF2F: a cross-system benchmark for formal Olympiad-level mathematics,Kunhao Zheng; Jesse Michael Han; Stanislas Polu,2022,ICLR 2022,main,Poster,,Neural theorem proving;Benchmark dataset,0,12.986,0.000,,https://iclr.cc/virtual/2022/poster/6258,https://openreview.net/pdf?id=9ZPegFuFTFv,offline_iclr,,"We present $\textsf{miniF2F}$, a dataset of formal Olympiad-level mathematics problems statements intended to provide a unified cross-system benchmark for neural theorem proving. The $\textsf{miniF2F}$ benchmark currently targets Metamath, Lean, Isabelle (partially) and HOL Light (partially) and con"
36,Ccu21bGqRv,Generative Adversarial Optimization: Dual-Reward Reinforcement Learning for Mathematics Reasoning,,2026,ICLR 2026,main,Active,reinforcement learning,LLM Reasoning Model;Math,0,12.630,0.000,,https://openreview.net/forum?id=Ccu21bGqRv,,offline_iclr,,"Despite recent progress achieved by large language models (LLMs), their remarkable mathematics reasoning abilities are largely dependent on fine-tuning on the annotated data, lacking generalization on out-of-distribution tasks. To address this, current methods adopt reinforcement learning (RL) to in"
37,Zix86UbMGh,ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics,Zhangir Azerbayev; Bartosz Piotrowski; Hailey Schoelkopf; Edward William Ayers; Dragomir Radev,2024,ICLR 2024,main,Reject,"neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)",autoformalization;theorem proving,0,12.445,0.000,,https://openreview.net/forum?id=Zix86UbMGh,,offline_iclr,,"We introduce ProofNet, a benchmark for autoformalization and formal proving of undergraduate-level mathematics. The ProofNet benchmarks consists of 371 examples, each consisting of a formal theorem statement in Lean 3, a natural language theorem statement, and a natural language proof. The problems "
38,F7_odJIeQ26,Pretrained Language Models are Symbolic Mathematics Solvers too!,Kimia Noorbakhsh; Modar Sulaiman; Mahdi Sharifi; KALLOL ROY; Pooyan Jamshidi,2022,ICLR 2022,main,Reject,,,0,12.438,0.000,,https://openreview.net/forum?id=F7_odJIeQ26,,offline_iclr,,"Solving symbolic mathematics has always been of in the arena of human ingenuity that needs compositional reasoning and recurrence. However, recent studies have shown that large scale language models such as transformers are universal and surprisingly can be trained as  a sequence-to-sequence task to"
39,fsDZwS49uY,OptiBench Meets ReSocratic: Measure and Improve LLMs for Optimization Modeling,Zhicheng Yang; Yiwei Wang; Yinya Huang; Zhijiang Guo; Wei Shi,2025,ICLR 2025,main,Poster,datasets and benchmarks,large language models; optimization problem; data synthesis,0,12.179,0.000,,https://iclr.cc/virtual/2025/poster/28850,https://openreview.net/pdf?id=fsDZwS49uY,offline_iclr,,"Large language models (LLMs) have exhibited their problem-solving abilities in mathematical reasoning. Solving realistic optimization (OPT) problems in application scenarios requires advanced and applied mathematics ability. However, current OPT benchmarks that merely solve linear programming are fa"
40,pKu077C57fH,Towards a Mathematics Formalisation Assistant using Large Language Models,Ayush Agrawal; Siddhartha Gadgil; Navin Goyal; Ashvni Narayanan; Anand Tadipatri,2023,ICLR 2023,main,Reject,,Large Language Models;Mathematics Formalisation,0,11.999,0.000,,https://openreview.net/forum?id=pKu077C57fH,,offline_iclr,Large language models have the potential to be useful for mathematics formalization,"Mathematics formalisation is the task of writing mathematics (i.e., definitions, theorem statements, proofs) in natural language, as found in books and papers, into a formal language that can then be checked for correctness by a program. It is a thriving activity today, however formalisation remains"
41,4WnqRR915j,Llemma: An Open Language Model for Mathematics,Zhangir Azerbayev; Hailey Schoelkopf; Keiran Paster; Marco Dos Santos; Stephen Marcus McAleer,2024,ICLR 2024,main,Poster,generative models,reasoning;language models;pretraining,0,11.923,0.000,,https://iclr.cc/virtual/2024/poster/19459,https://openreview.net/pdf?id=4WnqRR915j,offline_iclr,,"We present Llemma, a large language model for mathematics. We continue pretraining Code Llama on the Proof-Pile-2, a mixture of scientific papers, web data containing mathematics, and mathematical code, yielding Llemma. On the MATH benchmark Llemma outperforms all known openly released models, as we"
42,tlniJJFUW2,Machine Learning meets Algebraic Combinatorics: A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematics,Herman Chau; Helen Jenne; Davis Brown; Jesse He; Mark Raugas,2025,ICML 2025,main,Oral,applications->everything_else,Datasets;AI for math;Mathematical reasoning and conjecturing;Algebraic combinatorics,0,11.833,0.000,,https://icml.cc/virtual/2025/poster/43763,https://openreview.net/pdf?id=tlniJJFUW2,offline_icml,,"With recent dramatic increases in AI system capabilities, there has been growing interest in utilizing machine learning for reasoning-heavy, quantitative tasks, particularly mathematics. While there are many resources capturing mathematics at the high-school, undergraduate, and graduate level, there"
43,cNZEPEKKEh,UpSkill: Mutual Information Skill learning for Structured Response Diversity in LLMs,,2026,ICLR 2026,main,Active,"foundation or frontier models, including LLMs",Language Models;Mutual Information;Reasoning;Response Diversity;RLVR,0,11.606,0.000,,https://openreview.net/forum?id=cNZEPEKKEh,,offline_iclr,,"Reinforcement Learning with Verifiable Rewards (RLVR) has improved the reasoning abilities of large language models (LLMs) on mathematics and programming tasks, often by maximizing *pass@1* correctness. However, optimizing single-attempt accuracy can inadvertently suppress response diversity across "
44,-P7G-8dmSh4,Formal Mathematics Statement Curriculum Learning,Stanislas Polu; Jesse Michael Han; Kunhao Zheng; Mantas Baksys; Igor Babuschkin,2023,ICLR 2023,main,Top-25%,,neural theorem proving;formal mathematics;language modeling;expert iteration,0,11.575,0.000,,https://iclr.cc/virtual/2023/poster/11923,https://openreview.net/pdf?id=-P7G-8dmSh4,offline_iclr,,"We explore the use of expert iteration in the context of language modeling applied to formal mathematics. We show that at same compute budget, expert iteration, by which we mean proof search interleaved with learning, dramatically outperforms proof search only. We also observe that when applied to a"
45,dqcl5SNbc8,AdvPrompter: Fast Adaptive Adversarial Prompting for LLMs,Anselm Paulus; Arman Zharmagambetov; Chuan Guo; Brandon Amos; Yuandong Tian,2025,ICML 2025,main,Poster,social_aspects->security,adversarial attacks;prompt optimization;red-teaming LLMs,0,11.535,0.000,,https://icml.cc/virtual/2025/poster/44613,https://openreview.net/pdf?id=dqcl5SNbc8,offline_icml,,"Large Language Models (LLMs) are vulnerable to **jailbreaking attacks** that lead to generation of inappropriate or harmful content. 
Manual red-teaming requires a time-consuming search for adversarial prompts, whereas automatic adversarial prompt generation often leads to semantically meaningless a"
46,FiyS0ecSm0,Proving Olympiad Inequalities by Synergizing LLMs and Symbolic Reasoning,Zenan Li; Zhaoyu Li; Wen Tang; Xian Zhang; Yuan Yao,2025,ICLR 2025,main,Poster,"neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)",Neuro-symbolic theorem proving;Olympiad inequalities;Large language model;Symbolic method,0,11.526,0.000,,https://iclr.cc/virtual/2025/poster/30331,https://openreview.net/pdf?id=FiyS0ecSm0,offline_iclr,,"Large language models (LLMs) can prove mathematical theorems formally by generating proof steps (\textit{a.k.a.} tactics) within a proof system. However, the space of possible tactics is vast and complex, while the available training data for formal proofs is limited, posing a significant challenge "
47,o7avj3PWNC,BrokenMath: A Benchmark for Sycophancy in Theorem Proving with LLMs,,2026,ICLR 2026,main,Active,datasets and benchmarks,llm;reasoning;math;mathematics;sycophancy;hallucinations;trustworthy,0,11.477,0.000,,https://openreview.net/forum?id=o7avj3PWNC,,offline_iclr,,"Large language models (LLMs) have recently shown strong performance on mathematical benchmarks. At the same time, they are prone to hallucination and sycophancy, often providing convincing but flawed proofs for incorrect mathematical statements provided by users. This significantly limits the applic"
48,IpOlERVGgv,RefGrader: Automated Grading of Mathematical Competition Proofs using Agentic Workflows,Hamed Mahdavi; Pouria Mahdavinia; Samira Malek; Pegah Mohammadipour; Alireza Farhadi,2026,ICLR 2026,main,Withdraw,"foundation or frontier models, including LLMs",Mathematical proof assessment;Automated grading;Olympiad mathematics LLM evaluation;Proof verification,0,11.302,0.000,,https://openreview.net/forum?id=IpOlERVGgv,,offline_iclr,,"State-of-the-art (SOTA) LLMs have progressed from struggling on proof-based Olympiad problems to solving most of the IMO 2025 problems, with leading systems reportedly handling 5 of 6 problems. Given this progress, we assess how well these models can grade proofs: detecting errors, judging their sev"
49,LV3DpKD08B,xLSTM 7B: A Recurrent LLM for Fast and Efficient Inference,Maximilian Beck; Korbinian Pöppel; Phillip Lippe; Richard Kurle; Patrick M Blies,2025,ICML 2025,main,Poster,deep_learning->large_language_models,xLSTM;LLM;inference;inference time;inference speed;Transformer,0,11.234,0.000,,https://icml.cc/virtual/2025/poster/45587,https://openreview.net/pdf?id=LV3DpKD08B,offline_icml,,"Recent breakthroughs in solving reasoning, math and coding problems with Large Language Models (LLMs) have been enabled by investing substantial computation budgets at inference time. Therefore, inference speed is one of the most critical properties of LLM architectures, and there is a growing need "
50,ZarM_uLVyGw,Contrastive Reinforcement Learning of Symbolic Reasoning Domains,Gabriel Poesia; WenXin Dong; Noah Goodman,2021,NIPS 2021,main,Poster,,reinforcement learning;education;contrastive learning;symbolic reasoning,0,11.219,0.000,,https://nips.cc/virtual/2021/poster/26839,https://openreview.net/pdf?id=ZarM_uLVyGw,offline_nips,We propose a novel algorithm for reinforcement learning in discrete symbolic domains based on contrastive learning.,"Abstract symbolic reasoning, as required in domains such as mathematics and logic, is a key component of human intelligence. Solvers for these domains have important applications, especially to computer-assisted education. But learning to solve symbolic problems is challenging for machine learning a"
51,jJskRGo4N5,Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for LLMs,,2026,ICLR 2026,main,Active,"foundation or frontier models, including LLMs",Model Fusion,0,11.142,0.000,,https://openreview.net/forum?id=jJskRGo4N5,,offline_iclr,,"Large Language Models (LLMs) have achieved success across various domains. However, their applicability tends to degrade when confronted with different types of data inputs, especially for LLMs that have been fine-tuned for specific tasks. Despite its importance, the study of knowledge sharing among"
52,ChKCF75Ocd,PutnamBench: Evaluating Neural Theorem-Provers on the Putnam Mathematical Competition,George Tsoukalas; Jasper Lee; John Jennings; Jimmy Xin; Michelle Ding,2024,NIPS 2024,Datasets & Benchmarks,Poster,,neural theorem proving;formal methods;large language models;mathematical reasoning;theorem proving;Lean;Isabelle;Coq;AI for Math;formal reasoning,0,11.103,0.000,,https://neurips.cc/virtual/2024/poster/97811,https://openreview.net/pdf?id=ChKCF75Ocd,offline_nips,,"We present PutnamBench, a new multi-language benchmark for evaluating the ability of neural theorem-provers to solve competition mathematics problems. PutnamBench consists of 1692 hand-constructed formalizations of 640 theorems sourced from the William Lowell Putnam Mathematical Competition, the pre"
53,348hfcprUs,Fast Best-of-N Decoding via Speculative Rejection,Hanshi Sun; Momin Haider; Ruiqi Zhang; Huitao Yang; Jiahao Qiu,2024,NIPS 2024,main,Poster,generative_models,alignment;large language models;rejection sampling;best-of-n;acceleration,0,11.086,0.000,,https://neurips.cc/virtual/2024/poster/96774,https://openreview.net/pdf?id=348hfcprUs,offline_nips,,"The safe and effective deployment of Large Language Models (LLMs) involves a critical step called alignment, which ensures that the model's responses are in accordance with human preferences. Prevalent alignment techniques, such as DPO, PPO and their variants, align LLMs by changing the pre-trained "
54,849,Developing Bug-Free Machine Learning Systems With Formal Mathematics,Daniel Selsam; Percy Liang; David L. Dill,2017,ICML 2017,main,Poster,,,0,11.060,0.000,,https://icml.cc/virtual/2017/poster/849,http://proceedings.mlr.press/v70/selsam17a/selsam17a.pdf,offline_icml,,"Noisy data, non-convex objectives, model misspecification, and numerical instability can all cause undesired behaviors in machine learning systems. As a result, detecting actual implementation errors can be extremely difficult. We demonstrate a methodology in which developers use an interactive proo"
55,30iBKSQMXn,Retro-R1: LLM-based Agentic Retrosynthesis,Wei Liu; Jiangtao Feng; Hongli Yu; Yuxuan Song; Yuqiang Li,2025,NIPS 2025,main,Poster,deep_learning,Reinforcement Learning;Retrosynthesis Planning;LLM-based Agent,0,11.021,0.000,,https://openreview.net/forum?id=30iBKSQMXn,,offline_nips,,"Retrosynthetic planning is a fundamental task in chemical discovery. Due to the vast combinatorial search space, identifying viable synthetic routes remains a significant challenge--even for expert chemists. Recent advances in Large Language Models (LLMs), particularly equipped with reinforcement le"
56,jW8nBi6y9F,Large Language Models Think Too Fast To Explore Effectively,Lan Pan; Hanbo Xie; Robert Wilson,2025,NIPS 2025,main,Poster,neuroscience_and_cognitive_science,Large Language Models;Exploration;Empowerment;Uncertainty;Reasoning,0,10.952,0.000,,https://openreview.net/forum?id=jW8nBi6y9F,,offline_nips,,"Large Language Models (LLMs) have emerged with many intellectual capacities. While numerous benchmarks assess their intelligence, limited attention has been given to their ability to explore—an essential capacity for discovering new information and adapting to novel environments in both natural and "
57,zQGHxC6hr0,Usefulness-driven Learning of Formal Mathematics,,2026,ICLR 2026,main,Active,"neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)",reasoning;formal mathematics;logic;conjecturing;theorem proving;reinforcement learning,0,10.944,0.000,,https://openreview.net/forum?id=zQGHxC6hr0,,offline_iclr,,"Creating an AI that can truly ""do"" mathematics requires more than just solving isolated problems. It must mimic the creative, progressive nature of human mathematicians, who build upon previous work to generate new knowledge. A crucial part of this process is proposing theorems that serve as useful "
58,ToVvoHpk4L,$\texttt{CLR-Bench}$: Evaluating Large Language Models in College-Level Reasoning,Junnan Dong; Zijin Hong; Yuanchen Bei; Feiran Huang; Xinrun Wang,2025,ICLR 2025,main,Reject,datasets and benchmarks,Large Language Models Evaluation;Benchark and dataset;College-level Reasoning,0,10.867,0.000,,https://openreview.net/forum?id=ToVvoHpk4L,,offline_iclr,,"Large language models (LLMs) have demonstrated their remarkable performance across various language understanding tasks. While emerging benchmarks have been proposed to evaluate LLMs in various domains such as mathematics and computer science, they merely measure the accuracy in terms of the final p"
59,t33kMzEAg8,SwiReasoning: Switch-Thinking in Latent and Explicit for Pareto-Superior Reasoning LLMs,,2026,ICLR 2026,main,Active,"foundation or frontier models, including LLMs",LLM;Reasoning,0,10.850,0.000,,https://openreview.net/forum?id=t33kMzEAg8,,offline_iclr,,"Recent work shows that, beyond discrete reasoning through explicit chain-of-thought steps, which are limited by the boundaries of natural languages, large language models (LLMs) can also reason continuously in latent space, allowing richer information per step and thereby improving token efficiency."
60,tgJubHar53,Which LLMs Get the Joke? Probing Non-STEM Reasoning Abilities with HumorBench,,2026,ICLR 2026,main,Active,datasets and benchmarks,humor comprehension;large language models;reasoning evaluation;benchmark dataset;transfer learning,0,10.800,0.000,,https://openreview.net/forum?id=tgJubHar53,,offline_iclr,,"We present HumorBench, a benchmark designed to evaluate large language models’ (LLMs) ability to reason about and explain sophisticated humor in cartoon captions. As reasoning models increasingly saturate existing benchmarks in mathematics and science, novel and challenging evaluations of model inte"
61,Hjk1tWIdvL,Hierarchy-Aided Sparse Attention For Fast LLMs Prefilling Inference,Wenhao Li; Mingbao Lin; Zhanpeng Zeng; Shuicheng YAN; Rongrong Ji,2025,ICLR 2025,main,Reject,generative models,Long-Context LLM; Pre-Filling Acceleration; Sparse Attention,0,10.712,0.000,,https://openreview.net/forum?id=Hjk1tWIdvL,,offline_iclr,,"Pre-filling Large Language Models (LLMs) with long-context inputs is computationally expensive due to the quadratic complexity of full attention. While global attention is essential during decoding, its importance diminishes during pre-filling, where the focus is on contextualizing tokens rather tha"
62,xJ7YWXQOrg,Mathematical Capabilities of ChatGPT,Simon Frieder; Luca Pinchetti; Alexis Chevalier; Ryan-Rhys Griffiths; Tommaso Salvatori,2023,NIPS 2023,Datasets & Benchmarks,Poster,,datasets;LLMs;ChatGPT;mathematical capabilities;evaluation;benchmarking,0,10.663,0.000,,https://nips.cc/virtual/2023/poster/73421,https://openreview.net/pdf?id=xJ7YWXQOrg,offline_nips,,"We investigate the mathematical capabilities of two versions of ChatGPT (released 9-January-2023 and 30-January-2023) and of GPT-4 by testing them on publicly available datasets, as well as hand-crafted ones, using a novel evaluation scheme. In contrast to formal mathematics, where large databases o"
63,IUikebJ1Bf0,Autoformalization with Large Language Models,Yuhuai Wu; Albert Qiaochu Jiang; Wenda Li; Markus Norman Rabe; Charles E Staats,2022,NIPS 2022,main,Accept,,Large language models;Autoformalization;Formal Math;miniF2F.,0,10.643,0.000,,https://nips.cc/virtual/2022/poster/52916,https://openreview.net/pdf?id=IUikebJ1Bf0,offline_nips,"Large language models can be used to do autoformalization, allowing us to achieve in a new SOTA on miniF2F benchmark.","Autoformalization is the process of automatically translating from natural language mathematics to formal specifications and proofs. A successful autoformalization system could advance the fields of formal verification, program synthesis, and artificial intelligence.
While the long-term goal of auto"
64,Oomuu63IqO,HARDMath2: A Benchmark for Applied Mathematics Built by Students as Part of a Graduate Class,James V Roggeveen; Erik Y. Wang; David Ettel; Will Flintoft; Peter Donets,2025,NIPS 2025,Datasets & Benchmarks,Poster,evaluation,math;benchmark;dataset;AI for education,0,10.604,0.000,,https://openreview.net/forum?id=Oomuu63IqO,,offline_nips,,"Large language models (LLMs) have shown remarkable progress in mathematical problem-solving, but evaluation has largely focused on problems that have exact analytical solutions or involve formal proofs, often overlooking approximation-based problems ubiquitous in applied science and engineering. To "
65,CEvGuwMum0,JudgeRail: Harnessing Open-Source LLMs for Fast Harmful Text Detection with Judicial Prompting and Logit Rectification,Zhongjie Ba; Hongye Fu; Yiqi Yang; Hui Chen; Qinglong Wang,2025,ICLR 2025,main,Reject,"alignment, fairness, safety, privacy, and societal considerations",Large Language Model;Harmful Text Detection;Toxic Speech Detection;Content Moderation,0,10.584,0.000,,https://openreview.net/forum?id=CEvGuwMum0,,offline_iclr,,"Large language models (LLMs) simultaneously facilitate the generation and detection of harmful text. Leading LLM developers, such as OpenAI, Meta, and Google, are driving a paradigm shift in the detection of harmful text, moving from conventional detectors to fine-tuned LLMs. However, these newly re"
66,Af16P0ODP6,Reforming the Mechanism: Editing Reasoning Patterns in LLMs with Circuit Reshaping,,2026,ICLR 2026,main,Active,interpretability and explainable AI,Mechanistic Interpretability;Model Editing;Circuit Reshaping,0,10.496,0.000,,https://openreview.net/forum?id=Af16P0ODP6,,offline_iclr,,"Large language models (LLMs) often exhibit flawed reasoning ability that undermines reliability. Existing approaches to improving reasoning typically treat it as a general and monolithic skill, applying broad training that is inefficient and unable to target specific reasoning errors. We introduce R"
67,gBxpAKxg22,"OMEGA: Can LLMs Reason Outside the Box in Math? Evaluating Exploratory, Compositional, and Transformative Generalization",Yiyou Sun; Shawn Hu; Georgia Zhou; Ken Jiankun Zheng; Hannaneh Hajishirzi,2025,NIPS 2025,Datasets & Benchmarks,Poster,datasets_&_benchmarks_for_language,Large Language Model; Reasoning; Generalization;,0,10.485,0.000,,https://openreview.net/forum?id=gBxpAKxg22,,offline_nips,,"Recent large language models (LLMs) with long-chain-of-thought reasoning—such as DeepSeek-R1—have achieved impressive results on Olympiad-level mathematics benchmarks. However, they often rely on a narrow set of strategies and struggle with problems that require a novel way of thinking. To systemati"
68,uZ5K4HeNwd,Beyond Autoregression: Fast LLMs via Self-Distillation Through Time,Justin Deschenaux; Caglar Gulcehre,2025,ICLR 2025,main,Poster,generative models,language modeling;LLM;diffusion models;discrete diffusion models;diffusion language models;distillation,0,10.408,0.000,,https://iclr.cc/virtual/2025/poster/27972,https://openreview.net/pdf?id=uZ5K4HeNwd,offline_iclr,,"Autoregressive (AR) Large Language Models (LLMs) have demonstrated significant success across numerous tasks. However, the AR modeling paradigm presents certain limitations; for instance, contemporary autoregressive LLMs are trained to generate one token at a time, which can result in noticeable lat"
69,,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"
70,,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"
71,,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"
72,,No-cost Bell Nonlocality Certification from Quantum Tomography and Its Applications in Quantum Magic Witnessing,Pawel Cieslinski; Lukas Knips; Harald Weinfurter; Wieslaw Laskowski,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25068v1,https://arxiv.org/pdf/2512.25068v1,arxiv,,"Tomographic measurements are the standard tool for characterizing quantum states, yet they are usually regarded only as means for state reconstruction or fidelity measurement. Here, we show that the same Pauli-basis measurements (X, Y, Z) can be directly employed for the certification of nonlocality"
73,,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"
74,,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"
75,,Vulcan: Instance-Optimal Systems Heuristics Through LLM-Driven Search,Rohit Dwivedula; Divyanshu Saxena; Sujay Yadalam; Daehyeok Kim; Aditya Akella,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25065v1,https://arxiv.org/pdf/2512.25065v1,arxiv,,"Resource-management tasks in modern operating and distributed systems continue to rely primarily on hand-designed heuristics for tasks such as scheduling, caching, or active queue management. Designing performant heuristics is an expensive, time-consuming process that we are forced to continuously g"
76,,On the geometry and topology of representations: the manifolds of modular addition,Gabriela Moisescu-Pareja; Gavin McCracken; Harley Wiltzer; Vincent Létourneau; Colin Daniels,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25060v1,https://arxiv.org/pdf/2512.25060v1,arxiv,,"The Clock and Pizza interpretations, associated with architectures differing in either uniform or learnable attention, were introduced to argue that different architectural designs can yield distinct circuits for modular addition. In this work, we show that this is not the case, and that both unifor"
77,,Reliable and Resilient Collective Communication Library for LLM Training and Serving,Wei Wang; Nengneng Yu; Sixian Xiong; Zaoxing Liu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25059v1,https://arxiv.org/pdf/2512.25059v1,arxiv,,"Modern ML training and inference now span tens to tens of thousands of GPUs, where network faults can waste 10--15\% of GPU hours due to slow recovery. Common network errors and link fluctuations trigger timeouts that often terminate entire jobs, forcing expensive checkpoint rollback during training"
78,,The variety of orthogonal frames,Laura Casabella; Alessio Sammartano,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25058v1,https://arxiv.org/pdf/2512.25058v1,arxiv,,"An orthogonal n-frame is an ordered set of n pairwise orthogonal vectors. The set of all orthogonal n-frames in a d-dimensional quadratic vector space is an algebraic variety V(d,n). In this paper, we investigate the variety V(d,n) as well as the quadratic ideal I(d,n) generated by the orthogonality"
79,,The Logical Structure of Physical Laws: A Fixed Point Reconstruction,Eren Volkan Küçük,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25057v1,https://arxiv.org/pdf/2512.25057v1,arxiv,,"We formalise the self referential definition of physical laws using monotone operators on a lattice of theories, resolving the pathologies of naive set theoretic formulations. By invoking Tarski fixed point theorem, we identify physical theories as least fixed points of admissibility constraints der"
80,,Sequential Bayesian parameter-state estimation in dynamical systems with noisy and incomplete observations via a variational framework,Liliang Wang; Alex Gorodetsky,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25056v1,https://arxiv.org/pdf/2512.25056v1,arxiv,,"Online joint estimation of unknown parameters and states in a dynamical system with uncertainty quantification is crucial in many applications. For example, digital twins dynamically update their knowledge of model parameters and states to support prediction and decision-making. Reliability and comp"
81,,Context-aware LLM-based AI Agents for Human-centered Energy Management Systems in Smart Buildings,Tianzhi He; Farrokh Jazizadeh,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25055v1,https://arxiv.org/pdf/2512.25055v1,arxiv,,This study presents a conceptual framework and a prototype assessment for Large Language Model (LLM)-based Building Energy Management System (BEMS) AI agents to facilitate context-aware energy management in smart buildings through natural language interaction. The proposed framework comprises three 
82,,Bilinear tau forms of quantum Painlevé equations and $\mathbb{C}^2/\mathbb{Z}_2$ blowup relations in SUSY gauge theories,Giulio Bonelli; Anton Shchechkin; Alessandro Tanzini,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25051v1,https://arxiv.org/pdf/2512.25051v1,arxiv,,"We derive bilinear tau forms of the canonically quantized Painlevé equations, thereby relating them to those previously obtained from the $\mathbb{C}^2/\mathbb{Z}_2$ blowup relations for the $\mathcal{N}=2$ supersymmetric gauge theory partition functions on a general $Ω$-background. We fully fix the"
83,,The PDE-ODI principle and cylindrical mean curvature flows,Richard H. Bamler; Yi Lai,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25050v1,https://arxiv.org/pdf/2512.25050v1,arxiv,,"We introduce a new approach for analyzing ancient solutions and singularities of mean curvature flow that are locally modeled on a cylinder. Its key ingredient is a general mechanism, called the \emph{PDE--ODI principle}, which converts a broad class of parabolic differential equations into systems "
84,,Arithmetic with spatiotemporal optical vortex of integer and fractional topological charges,Hsiao-Chih Huang; Chen-Ting Liao; Hui Min Leung,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25049v1,https://arxiv.org/pdf/2512.25049v1,arxiv,,"Spatiotemporal optical vortices carry transverse orbital angular momentum (t-OAM), which give rise to spatiotemporal topological charge (ST-TC). To unleash the full potential of t-OAM in expanding the capacity of communication and computing, we demonstrate the first optical information-processing pi"
85,,Bayesian Elastic Net Regression with Structured Prior Dependence,Christopher M. Hans; Ningyi Liu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25045v1,https://arxiv.org/pdf/2512.25045v1,arxiv,,"Many regularization priors for Bayesian regression assume the regression coefficients are a priori independent. In particular this is the case for standard Bayesian treatments of the lasso and the elastic net. While independence may be reasonable in some data-analytic settings, incorporating depende"
86,,Compound Estimation for Binomials,Yan Chen; Lihua Lei,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25042v1,https://arxiv.org/pdf/2512.25042v1,arxiv,,"Many applications involve estimating the mean of multiple binomial outcomes as a common problem -- assessing intergenerational mobility of census tracts, estimating prevalence of infectious diseases across countries, and measuring click-through rates for different demographic groups. The most standa"
87,,On exact Observability for Compactly perturbed infinite dimension system,Nisrine Charaf; Faouzi Triki,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25041v1,https://arxiv.org/pdf/2512.25041v1,arxiv,,"In this paper, we study the observability of compactly perturbed infinite dimensional systems. Assuming that a given infinite-dimensional system with self-adjoint generator is exactly observable we derive sufficient conditions on a compact self adjoint perturbation to guarantee that the perturbed sy"
88,,Towards precision cosmology with Voids x CMB correlations (I): Roman-Agora mock catalogs and pipeline validation,Mar Pérez Sar; Carlos Hernández Monteagudo; András Kovács; Alice Pisani,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25040v1,https://arxiv.org/pdf/2512.25040v1,arxiv,,"We construct and validate a set of multi-purpose mock galaxy catalogs designed to capture, to different degrees of accuracy, the main characteristics of the Nancy Grace Roman Space Telescope survey. These catalogs provide a foundation for void statistics and various CMB cross-correlation analyses. O"
89,,The Hochschild homology of a noncommutative symmetric quotient stack,Rina Anno; Vladimir Baranovsky; Timothy Logvinenko,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25039v1,https://arxiv.org/pdf/2512.25039v1,arxiv,,"We prove an orbifold type decomposition theorem for the Hochschild homology of the symmetric powers of a small DG category $\mathcal{A}$. In noncommutative geometry, these can be viewed as the noncommutative symmetric quotient stacks of $\mathcal{A}$. We use this decomposition to show that the total"
90,,Universal polar dual pairs of spherical codes found in $E_8$ and $Λ_{24}$,S. V. Borodachov; P. G. Boyvalenkov; P. D. Dragnev; D. P. Hardin; E. B. Saff,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25037v1,https://arxiv.org/pdf/2512.25037v1,arxiv,,"We identify universal polar dual pairs of spherical codes $C$ and $D$ such that for a large class of potential functions $h$ the minima of the discrete $h$-potential of $C$ on the sphere occur at the points of $D$ and vice versa. Moreover, the minimal values of their normalized potentials are equal."
91,,Testing Monotonicity in a Finite Population,Jiafeng Chen; Jonathan Roth; Jann Spiess,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25032v1,https://arxiv.org/pdf/2512.25032v1,arxiv,,"We consider the extent to which we can learn from a completely randomized experiment whether everyone has treatment effects that are weakly of the same sign, a condition we call monotonicity. From a classical sampling perspective, it is well-known that monotonicity is untestable. By contrast, we sho"
92,,Multivariate Generalized Counting Process via Gamma Subordination,Manisha Dhillon; Kuldeep Kumar Kataria; Shyan Ghosh,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25030v1,https://arxiv.org/pdf/2512.25030v1,arxiv,,"In this paper, we study a multivariate gamma subordinator whose components are independent gamma processes subject to a random time governed by an independent negative binomial process. We derive the explicit expressions for its joint Laplace-Stieltjes transform, its probability density function and"
93,,Mod $p$ Poincaré duality for $p$-adic period domains,Guillaume Pignon-Ywanne,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25029v1,https://arxiv.org/pdf/2512.25029v1,arxiv,,"In this article, we introduce a new class of smooth partially proper rigid analytic varieties over a $p$-adic field that satisfy Poincaré duality for étale cohomology with mod $p$-coefficients : the varieties satisfying ""primitive comparison with compact support"". We show that almost proper varietie"
94,,Modewise Additive Factor Model for Matrix Time Series,Elynn Chen; Yuefeng Han; Jiayu Li; Ke Xu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25025v1,https://arxiv.org/pdf/2512.25025v1,arxiv,,"We introduce a Modewise Additive Factor Model (MAFM) for matrix-valued time series that captures row-specific and column-specific latent effects through an additive structure, offering greater flexibility than multiplicative frameworks such as Tucker and CP factor models. In MAFM, each observation d"
95,,On Nonlinear Inertial Transformations,Nicholas Agia,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25024v1,https://arxiv.org/pdf/2512.25024v1,arxiv,,"It is often assumed that the most general transformation between two inertial reference frames is affine linear in their Cartesian coordinates, an assumption which is however not true. We provide a complete derivation of the most general inertial frame transformation, which is indeed nonlinear; alon"
96,,ResponseRank: Data-Efficient Reward Modeling through Preference Strength Learning,Timo Kaufmann; Yannick Metz; Daniel Keim; Eyke Hüllermeier,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25023v1,https://arxiv.org/pdf/2512.25023v1,arxiv,,"Binary choices, as often used for reinforcement learning from human feedback (RLHF), convey only the direction of a preference. A person may choose apples over oranges and bananas over grapes, but which preference is stronger? Strength is crucial for decision-making under uncertainty and generalizat"
97,,Real Riemann Surfaces: Smooth and Discrete,Johanna Düntsch; Felix Günther,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25022v1,https://arxiv.org/pdf/2512.25022v1,arxiv,,This paper develops a discrete theory of real Riemann surfaces based on quadrilateral cellular decompositions (quad-graphs) and a linear discretization of the Cauchy-Riemann equations. We construct a discrete analogue of an antiholomorphic involution and classify the topological types of discrete re
98,,Approximation Algorithms for Fair Repetitive Scheduling,Danny Hermelin; Danny Segev; Dvir Shabtay,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25020v1,https://arxiv.org/pdf/2512.25020v1,arxiv,,"We consider a recently introduced fair repetitive scheduling problem involving a set of clients, each asking for their associated job to be daily scheduled on a single machine across a finite planning horizon. The goal is to determine a job processing permutation for each day, aiming to minimize the"
99,,Strengthening Dual Bounds for Multicommodity Capacitated Network Design with Unsplittable Flow Constraints,Lacy M. Greening; Santanu S. Dey; Alan L. Erera,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25018v1,https://arxiv.org/pdf/2512.25018v1,arxiv,,"Multicommodity capacitated network design (MCND) models can be used to optimize the consolidation of shipments within e-commerce fulfillment networks. In practice, fulfillment networks require that shipments with the same origin and destination follow the same transfer path. This unsplittable flow r"
100,,Convergence of the generalization error for deep gradient flow methods for PDEs,Chenguang Liu; Antonis Papapantoleon; Jasper Rou,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25017v1,https://arxiv.org/pdf/2512.25017v1,arxiv,,The aim of this article is to provide a firm mathematical foundation for the application of deep gradient flow methods (DGFMs) for the solution of (high-dimensional) partial differential equations (PDEs). We decompose the generalization error of DGFMs into an approximation and a training error. We f
101,,"Approximations for the Weighted Reversal, Transposition, and Indel Distance Problem with Intergenic Region Information",Gabriel Siqueira; Alexsandro Oliveira Alexandrino; Zanoni Dias,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25016v1,https://arxiv.org/pdf/2512.25016v1,arxiv,,"Genome rearrangement distances are an established method in genome comparison. Works in this area may include various rearrangement operations representing large-scale mutations, gene orientation information, the number of nucleotides in intergenic regions, and weights reflecting the expected freque"
102,,MAMA-Memeia! Multi-Aspect Multi-Agent Collaboration for Depressive Symptoms Identification in Memes,Siddhant Agarwal; Adya Dhuler; Polly Ruhnke; Melvin Speisman; Md Shad Akhtar,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25015v1,https://arxiv.org/pdf/2512.25015v1,arxiv,,"Over the past years, memes have evolved from being exclusively a medium of humorous exchanges to one that allows users to express a range of emotions freely and easily. With the ever-growing utilization of memes in expressing depressive sentiments, we conduct a study on identifying depressive sympto"
103,,A note on semistable unitary operators on $L^2(\mathbb{R})$,Xianghong Chen,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25013v1,https://arxiv.org/pdf/2512.25013v1,arxiv,,"In this note, we present a characterization of semistable unitary operators on $L^2(\mathbb{R})$, under the assumption that the operator is (i) translation-invariant, (ii) symmetric, and (iii) locally uniformly continuous (LUC) under dilation. As a consequence, we characterize one-parameter groups f"
104,,At the intersection of Numerical Analysis and Spectral Geometry,Nilima Nigam,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25012v1,https://arxiv.org/pdf/2512.25012v1,arxiv,,"How do the geometric properties of a domain impact the spectrum of an operator defined on it? How do we compute accurate and reliable approximations of these spectra? The former question is studied in spectral geometry, and the latter is a central concern in numerical analysis. In this short exposit"
105,,Bounding regularity of $\mathrm{VI}^m$-modules,Wee Liang Gan; Khoa Ta,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25010v1,https://arxiv.org/pdf/2512.25010v1,arxiv,,Fix a finite field $\mathbb{F}$. Let $\mathrm{VI}$ be a skeleton of the category of finite dimensional $\mathbb{F}$-vector spaces and injective $\mathbb{F}$-linear maps. We study $\mathrm{VI}^m$-modules over a noetherian commutative ring in the nondescribing characteristic case. We prove that if a f
106,,The splitting field and generators of the elliptic surface $Y^2=X^3 +t^{360} +1$,Sajad Salami,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25009v1,https://arxiv.org/pdf/2512.25009v1,arxiv,,"The splitting field of an elliptic surface $\mathcal{E}/\mathbb{Q}(t)$ is the smallest finite extension $\mathcal{K} \subset \mathbb{C}$ such that all $\mathbb{C}(t)$-rational points are defined over $\mathcal{K}(t)$. In this paper, we provide a symbolic algorithmic approach to determine the splitti"
107,,FoundationSLAM: Unleashing the Power of Depth Foundation Models for End-to-End Dense Visual SLAM,Yuchen Wu; Jiahe Li; Fabio Tosi; Matteo Poggi; Jin Zheng,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25008v1,https://arxiv.org/pdf/2512.25008v1,arxiv,,"We present FoundationSLAM, a learning-based monocular dense SLAM system that addresses the absence of geometric consistency in previous flow-based approaches for accurate and robust tracking and mapping. Our core idea is to bridge flow estimation with geometric reasoning by leveraging the guidance f"
108,,Fast Poisson brackets and constraint algebras in canonical gravity,Will Barker,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25007v1,https://arxiv.org/pdf/2512.25007v1,arxiv,,"In the study of alternative or extended theories of gravity, Dirac's Hamiltonian constraint algorithm is invaluable for enumerating the propagating modes and gauge symmetries. For gravity, this canonical approach is frequently applied as a means for finding pathologies such as strongly coupled modes"
109,,Limit Theorems for Fixed Point Biased Pattern Avoiding Involutions,Jungeun Park; Douglas Rizzolo,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25006v1,https://arxiv.org/pdf/2512.25006v1,arxiv,,We study fixed point biased involutions that avoid a pattern. For every pattern of length three we obtain limit theorems for the asymptotic distribution of the (appropriately centered and scaled) number of fixed points of a random fixed point biased involution avoiding that pattern. When the pattern
110,,Grassmannian Geometries for Non-Planar On-Shell Diagrams,Artyom Lisitsyn; Umut Oktem; Melissa Sherman-Bennett; Jaroslav Trnka,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25005v1,https://arxiv.org/pdf/2512.25005v1,arxiv,,"On-shell diagrams are gauge invariant quantities which play an important role in the description of scattering amplitudes. Based on the principles of generalized unitarity, they are given by products of elementary three-point amplitudes where the kinematics of internal on-shell legs are determined b"
111,,Uniqueness for stochastic differential equations in Hilbert spaces with irregular drift,Lukas Anzeletti; Oleg Butkovsky; Máté Gerencsér; Alexander Shaposhnikov,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25003v1,https://arxiv.org/pdf/2512.25003v1,arxiv,,"We present a versatile framework to study strong existence and uniqueness for stochastic differential equations (SDEs) in Hilbert spaces with irregular drift. We consider an SDE in a separable Hilbert space $H$ \begin{equation*} dX_t= (A X_t + b(X_t))dt +(-A)^{-γ/2}dW_t,\quad X_0=x_0 \in H, \end{equ"
112,,The local limit of weighted spanning trees on balanced networks,Ágnes Kúsz,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25001v1,https://arxiv.org/pdf/2512.25001v1,arxiv,,"We prove that the local limit of the weighted spanning trees on any simple connected high degree almost regular sequence of electric networks is the Poisson(1) branching process conditioned to survive forever, by generalizing [NP22] and closing a gap in their proof. We also study the local statistic"
113,,Bi-C2R: Bidirectional Continual Compatible Representation for Re-indexing Free Lifelong Person Re-identification,Zhenyu Cui; Jiahuan Zhou; Yuxin Peng,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25000v1,https://arxiv.org/pdf/2512.25000v1,arxiv,,"Lifelong person Re-IDentification (L-ReID) exploits sequentially collected data to continuously train and update a ReID model, focusing on the overall performance of all data. Its main challenge is to avoid the catastrophic forgetting problem of old knowledge while training on new data. Existing L-R"
114,,Basic Inequalities for First-Order Optimization with Applications to Statistical Risk Analysis,Seunghoon Paik; Kangjie Zhou; Matus Telgarsky; Ryan J. Tibshirani,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24999v1,https://arxiv.org/pdf/2512.24999v1,arxiv,,"We introduce \textit{basic inequalities} for first-order iterative optimization algorithms, forming a simple and versatile framework that connects implicit and explicit regularization. While related inequalities appear in the literature, we isolate and highlight a specific form and develop it as a w"
115,,Numerical study of boson mixtures with multi-component continuous matrix product states,Wei Tang; Benoît Tuybens; Jutho Haegeman,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24998v1,https://arxiv.org/pdf/2512.24998v1,arxiv,,"The continuous matrix product state (cMPS) ansatz is a promising numerical tool for studying quantum many-body systems in continuous space. Although it provides a clean framework that allows one to directly simulate continuous systems, the optimization of cMPS is known to be a very challenging task,"
116,,Manifold classification from the descriptive viewpoint,Jeffrey Bergfalk; Iian B. Smythe,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24996v1,https://arxiv.org/pdf/2512.24996v1,arxiv,,"We consider classification problems for manifolds and discrete subgroups of Lie groups from a descriptive set-theoretic point of view. This work is largely foundational in conception and character, recording both a framework for general study and Borel complexity computations for some of the most fu"
117,,Efficiently Estimating Data Efficiency for Language Model Fine-tuning,Gyung Hyun Je; Colin Raffel,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24991v1,https://arxiv.org/pdf/2512.24991v1,arxiv,,"While large language models (LLMs) demonstrate reasonable zero-shot capability across many downstream tasks, fine-tuning is a common practice to improve their performance. However, a task's data efficiency--i.e., the number of fine-tuning examples needed to achieve a desired level of performance--is"
118,,The Fourier extension conjecture for the paraboloid,Cristian Rios; Eric T. Sawyer,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24990v1,https://arxiv.org/pdf/2512.24990v1,arxiv,,We give a proof of Fourier extension conjecture on the paraboloid in all dimensions bigger than 2 that begins with a decomposition suggested in Sawyer [Saw8] of writing a smooth Alpert projection as a sum of pieces whose Fourier extensions are localized. This is then used in the case d=3 to establis
119,,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"
120,,A guide to the $2$-generated axial algebras of Monster type,Justin McInroy; Abdul Wajid Mir,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24987v1,https://arxiv.org/pdf/2512.24987v1,arxiv,,"Axial algebras of Monster type are a class of non-associative algebras which generalise the Griess algebra, whose automorphism group is the largest sporadic simple group, the Monster. The $2$-generated algebras, which are the building blocks from which all algebras in this class can be constructed, "
121,,PhysTalk: Language-driven Real-time Physics in 3D Gaussian Scenes,Luca Collorone; Mert Kiray; Indro Spinelli; Fabio Galasso; Benjamin Busam,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24986v1,https://arxiv.org/pdf/2512.24986v1,arxiv,,"Realistic visual simulations are omnipresent, yet their creation requires computing time, rendering, and expert animation knowledge. Open-vocabulary visual effects generation from text inputs emerges as a promising solution that can unlock immense creative potential. However, current pipelines lack "
122,,DarkEQA: Benchmarking Vision-Language Models for Embodied Question Answering in Low-Light Indoor Environments,Yohan Park; Hyunwoo Ha; Wonjun Jo; Tae-Hyun Oh,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24985v1,https://arxiv.org/pdf/2512.24985v1,arxiv,,"Vision Language Models (VLMs) are increasingly adopted as central reasoning modules for embodied agents. Existing benchmarks evaluate their capabilities under ideal, well-lit conditions, yet robust 24/7 operation demands performance under a wide range of visual degradations, including low-light cond"
123,,A Modal Logic for Possibilistic Reasoning with Fuzzy Formal Contexts,Prosenjit Howlader; Churn-Jung Liau,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24980v1,https://arxiv.org/pdf/2512.24980v1,arxiv,,We introduce a two-sort weighted modal logic for possibilistic reasoning with fuzzy formal contexts. The syntax of the logic includes two types of weighted modal operators corresponding to classical necessity ($\Box$) and sufficiency ($\boxminus$) modalities and its formulas are interpreted in fuzzy
124,,Multi-Frequency Study of FRB20201124A with the uGMRT,C. Dudeja; J. Roy; U. Panda; S. Bhattacharyya,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24978v1,https://arxiv.org/pdf/2512.24978v1,arxiv,,"We present results from multi-epoch observations of the repeating fast radio burst FRB 20201124A with the upgraded Giant Metrewave Radio Telescope (uGMRT) during its active phase between 8 May and 28 May 2021. The bursts exhibit significant morphological diversity, including multiple sub-bursts, dow"
125,,Graphicality of power-law and double power-law degree sequences,Pietro Valigi; M. Ángeles Serrano; Claudio Castellano; Lorenzo Cirigliano,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24976v1,https://arxiv.org/pdf/2512.24976v1,arxiv,,"The graphicality problem -- whether or not a sequence of integers can be used to create a simple graph -- is a key question in network theory and combinatorics, with many important practical applications. In this work, we study the graphicality of degree sequences distributed as a power-law with a s"
126,,From Complex-Analytic Models to Sparse Domination: A Dyadic Approach of Hypersingular Operators via Bourgain's Interpolation Method,Bingyang Hu; Xiaojing Zhou,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24972v1,https://arxiv.org/pdf/2512.24972v1,arxiv,,"Motivated by the work of Cheng--Fang--Wang--Yu on the hypersingular Bergman projection, we develop a real-variable and dyadic framework for hypersingular operators in regimes where strong-type estimates fail at the critical line. The main new input is a hypersingular sparse domination principle comb"
127,,Large language models and the entropy of English,Colin Scheibner; Lindsay M. Smith; William Bialek,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24969v1,https://arxiv.org/pdf/2512.24969v1,arxiv,,"We use large language models (LLMs) to uncover long-ranged structure in English texts from a variety of sources. The conditional entropy or code length in many cases continues to decrease with context length at least to $N\sim 10^4$ characters, implying that there are direct dependencies or interact"
128,,The Impact of LLMs on Online News Consumption and Production,Hangcheng Zhao; Ron Berman,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24968v1,https://arxiv.org/pdf/2512.24968v1,arxiv,,Large language models (LLMs) change how consumers acquire information online; their bots also crawl news publishers' websites for training data and to answer consumer queries; and they provide tools that can lower the cost of content creation. These changes lead to predictions of adverse impact on n
129,,Cartier duality for gerbes of vector bundles,Juan Esteban Rodríguez Camargo,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.24967v1,https://arxiv.org/pdf/2512.24967v1,arxiv,,"We prove a Cartier duality for gerbes of algebraic and analytic vector bundles as an anti-equivalence of Hopf algebras in the category of kernels of analytic stacks. As an application, we prove that the category of solid quasi-coherent sheaves on the Hodge-Tate stack of a smooth rigid variety over a"
130,,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"
131,,Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory,Mutian Yang; Jiandong Gao; Ji Wu,2025,arXiv.org,,,,,0,0.000,0.000,10.48550/arXiv.2507.18178,https://www.semanticscholar.org/paper/7edf4896643cf4df934e846cb1217d7c06e3b591,,semantic_scholar,,"While large language models (LLMs) leverage both knowledge and reasoning during inference, the capacity to distinguish between them plays a pivotal role in model analysis, interpretability, and development. Inspired by dual-system cognitive theory, we propose a cognition attribution framework to dec"
132,,From System 1 to System 2: A Survey of Reasoning Large Language Models,Zhong-Zhi Li; Duzhen Zhang; Ming-Liang Zhang; Jiaxin Zhang; Zengyan Liu,2025,IEEE Transactions on Pattern Analysis and Machine Intelligence,,,,,177,0.000,0.000,10.48550/arXiv.2502.17419,https://www.semanticscholar.org/paper/f4195d4e283e289665cfc7a65fde2fa7b8814091,,semantic_scholar,,"Achieving human-level intelligence requires refining the transition from the fast, intuitive System 1 to the slower, more deliberate System 2 reasoning. While System 1 excels in quick, heuristic decisions, System 2 relies on logical reasoning for more accurate judgments and reduced biases. Foundatio"
133,,Pangu Embedded: An Efficient Dual-system LLM Reasoner with Metacognition,Hanting Chen; Yasheng Wang; Kai Han; Dong Li; Lin Li,2025,arXiv.org,,,,,9,0.000,0.000,10.48550/arXiv.2505.22375,https://www.semanticscholar.org/paper/c2699ff6ea7c112c7e6fc38d4306593c28e711f4,,semantic_scholar,,"This work presents Pangu Embedded, an efficient Large Language Model (LLM) reasoner developed on Ascend Neural Processing Units (NPUs), featuring flexible fast and slow thinking capabilities. Pangu Embedded addresses the significant computational costs and inference latency challenges prevalent in e"
134,,MASA: LLM-Driven Multi-Agent Systems for Autoformalization,Lan Zhang; Marco Valentino; Andre Freitas,2025,Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations,,,,,0,0.000,0.000,10.18653/v1/2025.emnlp-demos.44,https://www.semanticscholar.org/paper/2566390661f701cf95eb420df337fa7a566409b8,,semantic_scholar,,"Autoformalization serves a crucial role in connecting natural language and formal reasoning. This paper presents MASA, a novel framework for building multi-agent systems for autoformalization driven by Large Language Models (LLMs). MASA leverages collaborative agents to convert natural language stat"
135,,"HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs",Junying Chen; Zhenyang Cai; Ke Ji; Xidong Wang; Wanlong Liu,2024,arXiv.org,,,,,154,0.000,0.000,10.48550/arXiv.2412.18925,https://www.semanticscholar.org/paper/a9161bb1bf727532e1a4aacb7ba1d7cf175edde0,,semantic_scholar,,"The breakthrough of OpenAI o1 highlights the potential of enhancing reasoning to improve LLM. Yet, most research in reasoning has focused on mathematical tasks, leaving domains like medicine underexplored. The medical domain, though distinct from mathematics, also demands robust reasoning to provide"
136,,Dualformer: Controllable Fast and Slow Thinking by Learning with Randomized Reasoning Traces,DiJia Su; Sainbayar Sukhbaatar; Michael Rabbat; Yuandong Tian; Qinqing Zheng,2024,International Conference on Learning Representations,,,,,42,0.000,0.000,10.48550/arXiv.2410.09918,https://www.semanticscholar.org/paper/48472a217174053a146b0ebd3821e71b53f05a36,,semantic_scholar,,"In cognition theory, human thinking is governed by two systems: the fast and intuitive System 1 and the slower but more deliberative System 2. Analogously, Large Language Models (LLMs) can operate in two reasoning modes: outputting only the solutions (\emph{fast mode}) or both the reasoning chain an"
137,,What Happened in LLMs Layers when Trained for Fast vs. Slow Thinking: A Gradient Perspective,Ming Li; Yanhong Li; Tianyi Zhou,2024,arXiv.org,,,,,24,0.000,0.000,10.48550/arXiv.2410.23743,https://www.semanticscholar.org/paper/64f5265fa0cf844fe6a61c0889e4e8edf29da6a2,,semantic_scholar,,"What makes a difference in the post-training of LLMs? We investigate the training patterns of different layers in large language models (LLMs) through the lens of the gradient. We are specifically interested in how fast vs. slow thinking affects the layer-wise gradients, given the recent popularity "
138,,Can We Further Elicit Reasoning in LLMs? Critic-Guided Planning with Retrieval-Augmentation for Solving Challenging Tasks,Xingxuan Li; Weiwen Xu; Ruochen Zhao; Fangkai Jiao; Shafiq R. Joty,2024,Annual Meeting of the Association for Computational Linguistics,,,,,24,0.000,0.000,10.48550/arXiv.2410.01428,https://www.semanticscholar.org/paper/8e90bba98fdd41a9046ba00ad527441a447c56bb,,semantic_scholar,,State-of-the-art large language models (LLMs) exhibit impressive problem-solving capabilities but may struggle with complex reasoning and factual correctness. Existing methods harness the strengths of chain-of-thought and retrieval-augmented generation (RAG) to decompose a complex problem into simpl
139,,Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs,Shashank Gupta; Vaishnavi Shrivastava; A. Deshpande; A. Kalyan; Peter Clark,2023,International Conference on Learning Representations,,,,,167,0.000,0.000,10.48550/arXiv.2311.04892,https://www.semanticscholar.org/paper/1b0e3360b3341fc411a6c7841173a8d78ac2ab43,,semantic_scholar,,"Recent works have showcased the ability of LLMs to embody diverse personas in their responses, exemplified by prompts like 'You are Yoda. Explain the Theory of Relativity.' While this ability allows personalization of LLMs and enables human behavior simulation, its effect on LLMs' capabilities remai"
140,,Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?,Yang Yue; Zhiqi Chen; Rui Lu; Andrew Zhao; Zhaokai Wang,2025,arXiv.org,,,,,422,0.000,0.000,10.48550/arXiv.2504.13837,https://www.semanticscholar.org/paper/143e18bfd7c356592e7c1439738a3525d3e16279,,semantic_scholar,,"Reinforcement Learning with Verifiable Rewards (RLVR) has recently demonstrated notable success in enhancing the reasoning performance of large language models (LLMs), particularly on mathematics and programming tasks. Similar to how traditional RL helps agents explore and learn new strategies, RLVR"
141,,CMM-Math: A Chinese Multimodal Math Dataset To Evaluate and Enhance the Mathematics Reasoning of Large Multimodal Models,Wentao Liu; Qianjun Pan; Yi Zhang; Zhuo Liu; Ji Wu,2024,Proceedings of the 33rd ACM International Conference on Multimedia,,,,,13,0.000,0.000,10.1145/3746027.3758193,https://www.semanticscholar.org/paper/344a9642b76c83143289db8f9fc535e74afd0421,,semantic_scholar,,"Large language models (LLMs) have obtained promising results in mathematical reasoning, a foundational human intelligence skill. Most previous studies focus on improving or measuring the performance of LLMs via textual math datasets (e.g., MATH, GSM8K). In this paper, we release a Chinese multimodal"
142,,Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMs,Xin Lai; Zhuotao Tian; Yukang Chen; Senqiao Yang; Xiangru Peng,2024,arXiv.org,,,,,205,0.000,0.000,10.48550/arXiv.2406.18629,https://www.semanticscholar.org/paper/501f8a0200a12a6f3906c1e4f3f40715e0e7d23a,,semantic_scholar,,"Mathematical reasoning presents a significant challenge for Large Language Models (LLMs) due to the extensive and precise chain of reasoning required for accuracy. Ensuring the correctness of each reasoning step is critical. To address this, we aim to enhance the robustness and factuality of LLMs by"
143,,Assessing the Reasoning Capabilities of LLMs in the context of Evidence-based Claim Verification,John Dougrez-Lewis; Mahmud Elahi Akhter; Yulan He; M. Liakata,2024,Annual Meeting of the Association for Computational Linguistics,,,,,6,0.000,0.000,10.18653/v1/2025.findings-acl.1059,https://www.semanticscholar.org/paper/a0a495e4bab4d236fd284dc0edc62062761b0a09,,semantic_scholar,,"Although LLMs have shown great performance on Mathematics and Coding related reasoning tasks, the reasoning capabilities of LLMs regarding other forms of reasoning are still an open problem. Here, we examine the issue of reasoning from the perspective of claim verification. We propose a framework de"
144,,DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement learning,Daya Guo; Dejian Yang; Haowei Zhang; Jun-Mei Song; Peiyi Wang,2025,Nature,,,,,302,0.000,0.000,10.1038/s41586-025-09422-z,https://www.semanticscholar.org/paper/829b25b13de12d81be2eedda52a2309b8a8fccd4,,semantic_scholar,,"General reasoning represents a long-standing and formidable challenge in artificial intelligence (AI). Recent breakthroughs, exemplified by large language models (LLMs)1,2 and chain-of-thought (CoT) prompting3, have achieved considerable success on foundational reasoning tasks. However, this success"
145,,Advancing Multimodal LLMs: A Focus on Geometry Problem Solving Reasoning and Sequential Scoring,Raj Jaiswal; Avinash Anand; R. Shah,2024,ACM Multimedia Asia,,,,,3,0.000,0.000,10.1145/3696409.3700262,https://www.semanticscholar.org/paper/f2707632bef94d88f41f181b162476c5adbeef0d,,semantic_scholar,,"This paper presents GPSM4K, a comprehensive geometry multimodal dataset tailored to augment the problem-solving capabilities of Large Vision Language Models (LVLMs). GPSM4K encompasses 1480 multimodal question-answer pairs manually extracted from mathematics textbooks spanning grades 7-12 and is fur"
146,,CriticBench: Benchmarking LLMs for Critique-Correct Reasoning,Zicheng Lin; Zhibin Gou; Tian Liang; Ruilin Luo; Haowei Liu,2024,Annual Meeting of the Association for Computational Linguistics,,,,,78,0.000,0.000,10.48550/arXiv.2402.14809,https://www.semanticscholar.org/paper/6f24e0782300dca8a4cefcb5a3ccba94bfbb1395,,semantic_scholar,,"The ability of Large Language Models (LLMs) to critique and refine their reasoning is crucial for their application in evaluation, feedback provision, and self-improvement. This paper introduces CriticBench, a comprehensive benchmark designed to assess LLMs' abilities to critique and rectify their r"
147,,MathGenie: Generating Synthetic Data with Question Back-translation for Enhancing Mathematical Reasoning of LLMs,Zimu Lu; Aojun Zhou; Houxing Ren; Ke Wang; Weikang Shi,2024,Annual Meeting of the Association for Computational Linguistics,,,,,79,0.000,0.000,10.48550/arXiv.2402.16352,https://www.semanticscholar.org/paper/135da052891fdcf3842901b6b7585ca8c258f1c2,,semantic_scholar,,"Large language models (LLMs) have exhibited great potential in mathematical reasoning. However, there remains a performance gap in this area between existing open-source models and closed-source models such as GPT-4. In this paper, we introduce MathGenie, a novel method for generating diverse and re"
148,,How Numerical Precision Affects Arithmetical Reasoning Capabilities of LLMs,Guhao Feng; Kai Yang; Yuntian Gu; Xinyue Ai; Shengjie Luo,2024,Annual Meeting of the Association for Computational Linguistics,,,,,20,0.000,0.000,10.18653/v1/2025.findings-acl.3,https://www.semanticscholar.org/paper/aad23e8f699bec8c1c6ebf2636f5446ec970033e,,semantic_scholar,,"Despite the remarkable success of Transformer-based large language models (LLMs) across various domains, understanding and enhancing their mathematical capabilities remains a significant challenge. In this paper, we conduct a rigorous theoretical analysis of LLMs'mathematical abilities, with a speci"
149,,Tree-of-Mixed-Thought: Combining Fast and Slow Thinking for Multi-hop Visual Reasoning,Pengbo Hu; Jingxian Qi; Xingyu Li; Hong Li; Xinqi Wang,2023,arXiv.org,,,,,19,0.000,0.000,10.48550/arXiv.2308.09658,https://www.semanticscholar.org/paper/ecfe2becc1040d3810c10f006d4be43b4eef3c41,https://arxiv.org/pdf/2308.09658,semantic_scholar,,"There emerges a promising trend of using large language models (LLMs) to generate code-like plans for complex inference tasks such as visual reasoning. This paradigm, known as LLM-based planning, provides flexibility in problem solving and endows better interpretability. However, current research is"
150,,From Next-Token to Mathematics: The Learning Dynamics of Mathematical Reasoning in Language Models,Shubhra Mishra; Gabriel Poesia; Noah D. Goodman,2024,,,,,,4,0.000,0.000,,https://www.semanticscholar.org/paper/4d44edd5c040f215bb04b99298c6f2ba5f8150b7,,semantic_scholar,,Large Language Models (LLMs) solely trained on next-token prediction learn to solve a wide range of problems involving mathematical reasoning. But how does this ability evolve during training? We show the first analysis of how mathematical reasoning abilities of several open-weight LLMs develop duri
151,,Can Language Models Rival Mathematics Students? Evaluating Mathematical Reasoning through Textual Manipulation and Human Experiments,Andrii D. Nikolaiev; Y. Stathopoulos; Simone Teufel,2024,arXiv.org,,,,,2,0.000,0.000,10.48550/arXiv.2412.11908,https://www.semanticscholar.org/paper/41158974c85e97f0c5575c0b5b3413640c14c567,,semantic_scholar,,"In this paper we look at the ability of recent large language models (LLMs) at solving mathematical problems in combinatorics. We compare models LLaMA-2, LLaMA-3.1, GPT-4, and Mixtral against each other and against human pupils and undergraduates with prior experience in mathematical olympiads. To f"
152,,Evaluating LLMs' Mathematical Reasoning in Financial Document Question Answering,Pragya Srivastava; Manuj Malik; Vivek Gupta; Tanuja Ganu; Dan Roth,2024,Annual Meeting of the Association for Computational Linguistics,,,,,37,0.000,0.000,10.48550/arXiv.2402.11194,https://www.semanticscholar.org/paper/a4feb9b216ac5c84ac636fdd2f608105709cf979,,semantic_scholar,,"Large Language Models (LLMs), excel in natural language understanding, but their capability for complex mathematical reasoning with an amalgamation of structured tables and unstructured text is uncertain. This study explores LLMs'mathematical reasoning on four financial tabular question-answering da"
153,,CHAMP: A Competition-level Dataset for Fine-Grained Analyses of LLMs' Mathematical Reasoning Capabilities,Yujun Mao; Yoon Kim; Yilun Zhou,2024,Annual Meeting of the Association for Computational Linguistics,,,,,37,0.000,0.000,10.48550/arXiv.2401.06961,https://www.semanticscholar.org/paper/bc8e76e9541dd07a094540d262a864b65505cab7,,semantic_scholar,,"Recent large language models (LLMs) have shown indications of mathematical reasoning ability on challenging competition-level problems, especially with self-generated verbalizations of intermediate reasoning steps (i.e., chain-of-thought prompting). However, current evaluations mainly focus on the e"
154,,MindStar: Enhancing Math Reasoning in Pre-trained LLMs at Inference Time,Jikun Kang; Xin Zhe Li; Xi Chen; Amirreza Kazemi; Boxing Chen,2024,arXiv.org,,,,,34,0.000,0.000,10.48550/arXiv.2405.16265,https://www.semanticscholar.org/paper/36e6a2fecd40f41e3c45d79d3ed44e505ea10ac3,,semantic_scholar,,"Although Large Language Models (LLMs) achieve remarkable performance across various tasks, they often struggle with complex reasoning tasks, such as answering mathematical questions. Recent efforts to address this issue have primarily focused on leveraging mathematical datasets through supervised fi"
155,,LLMs can Find Mathematical Reasoning Mistakes by Pedagogical Chain-of-Thought,Zhuoxuan Jiang; Haoyuan Peng; Shanshan Feng; Fan Li; Dongsheng Li,2024,International Joint Conference on Artificial Intelligence,,,,,28,0.000,0.000,10.48550/arXiv.2405.06705,https://www.semanticscholar.org/paper/e9aef5349df9eaf151b6cbdb60a2e269206e70b7,,semantic_scholar,,"Self-correction is emerging as a promising approach to mitigate the issue of hallucination in Large Language Models (LLMs). To facilitate effective self-correction, recent research has proposed mistake detection as its initial step. However, current literature suggests that LLMs often struggle with "
156,,BIDTrainer: An LLMs-driven Education Tool for Enhancing the Understanding and Reasoning in Bio-inspired Design,Liuqing Chen; Zhaojun Jiang; Duowei Xia; Zebin Cai; Lingyun Sun,2024,International Conference on Human Factors in Computing Systems,,,,,25,0.000,0.000,10.1145/3613904.3642887,https://www.semanticscholar.org/paper/d673bf9be37a7c6b41b39439846eac444d894125,https://dl.acm.org/doi/pdf/10.1145/3613904.3642887,semantic_scholar,,"Bio-inspired design (BID) fosters innovations in engineering. Learning BID is crucial for developing multidisciplinary innovation skills of designers and engineers. Current BID education aims to enhance learners’ understanding and analogical reasoning skills. However, it often heavily relies on the "
157,,MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning,Ke Wang; Houxing Ren; Aojun Zhou; Zimu Lu; Sichun Luo,2023,International Conference on Learning Representations,,,,,161,0.000,0.000,10.48550/arXiv.2310.03731,https://www.semanticscholar.org/paper/cddb552f6c3464a54a02b0b64b2d1af56c086606,https://arxiv.org/pdf/2310.03731,semantic_scholar,,"The recently released GPT-4 Code Interpreter has demonstrated remarkable proficiency in solving challenging math problems, primarily attributed to its ability to seamlessly reason with natural language, generate code, execute code, and continue reasoning based on the execution output. In this paper,"
158,,Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs,Yucong Luo; Yitong Zhou; Ming-Zhen Cheng; Jiahao Wang; Daoyu Wang,2025,arXiv.org,,,,,10,0.000,0.000,10.48550/arXiv.2506.10630,https://www.semanticscholar.org/paper/14998ec52d42803c891295d00e1dbd8183427342,,semantic_scholar,,"To advance time series forecasting (TSF), various methods have been proposed to improve prediction accuracy, evolving from statistical techniques to data-driven deep learning architectures. Despite their effectiveness, most existing methods still adhere to a fast thinking paradigm-relying on extract"
159,,Reasoning on a Spectrum: Aligning LLMs to System 1 and System 2 Thinking,Alireza S. Ziabari; Nona Ghazizadeh; Zhivar Sourati; Farzan Karimi-Malekabadi; Payam Piray,2025,arXiv.org,,,,,13,0.000,0.000,10.48550/arXiv.2502.12470,https://www.semanticscholar.org/paper/e446584d601cba65c5155b2fc4cf110fc0c7da79,,semantic_scholar,,"Large Language Models (LLMs) exhibit impressive reasoning abilities, yet their reliance on structured step-by-step processing reveals a critical limitation. In contrast, human cognition fluidly adapts between intuitive, heuristic (System 1) and analytical, deliberative (System 2) reasoning depending"
160,,A Survey of Slow Thinking-based Reasoning LLMs using Reinforced Learning and Inference-time Scaling Law,Qianjun Pan; Wenkai Ji; Yuyang Ding; Junsong Li; Shilian Chen,2025,arXiv.org,,,,,12,0.000,0.000,10.48550/arXiv.2505.02665,https://www.semanticscholar.org/paper/c2feda1e804700d0980d71cfc71ce66d369b6b6c,,semantic_scholar,,"This survey explores recent advancements in reasoning large language models (LLMs) designed to mimic""slow thinking""- a reasoning process inspired by human cognition, as described in Kahneman's Thinking, Fast and Slow. These models, like OpenAI's o1, focus on scaling computational resources dynamical"
161,,TIME: A Multi-level Benchmark for Temporal Reasoning of LLMs in Real-World Scenarios,Shaohang Wei; Wei Li; Feifan Song; Wen Luo; Tianyi Zhuang,2025,arXiv.org,,,,,5,0.000,0.000,10.48550/arXiv.2505.12891,https://www.semanticscholar.org/paper/61cf800a8669b76592a08b3e84bd24a6aac26a9c,,semantic_scholar,,"Temporal reasoning is pivotal for Large Language Models (LLMs) to comprehend the real world. However, existing works neglect the real-world challenges for temporal reasoning: (1) intensive temporal information, (2) fast-changing event dynamics, and (3) complex temporal dependencies in social interac"
162,,Win Fast or Lose Slow: Balancing Speed and Accuracy in Latency-Sensitive Decisions of LLMs,Hao Kang; Qingru Zhang; Han Cai; Weiyuan Xu; Tushar Krishna,2025,arXiv.org,,,,,3,0.000,0.000,10.48550/arXiv.2505.19481,https://www.semanticscholar.org/paper/c34cee11069f9181e83b8dfc3023b2fb6bac0811,,semantic_scholar,,"Large language models (LLMs) have shown remarkable performance across diverse reasoning and generation tasks, and are increasingly deployed as agents in dynamic environments such as code generation and recommendation systems. However, many real-world applications, such as high-frequency trading and "
163,,ABench-Physics: Benchmarking Physical Reasoning in LLMs via High-Difficulty and Dynamic Physics Problems,Yiming Zhang; Yingfan Ma; Yanmei Gu; Zhengkai Yang; Yihong Zhuang,2025,arXiv.org,,,,,6,0.000,0.000,10.48550/arXiv.2507.04766,https://www.semanticscholar.org/paper/1a7b627b5a905eab8a799000475143a4abc1da85,,semantic_scholar,,"Large Language Models (LLMs) have shown impressive performance in domains such as mathematics and programming, yet their capabilities in physics remain underexplored and poorly understood. Physics poses unique challenges that demand not only precise computation but also deep conceptual understanding"
164,,"One Example Shown, Many Concepts Known! Counterexample-Driven Conceptual Reasoning in Mathematical LLMs",Yinghui Li; Jiayi Kuang; Haojing Huang; Zhikun Xu; Xinnian Liang,2025,International Conference on Machine Learning,,,,,12,0.000,0.000,10.48550/arXiv.2502.10454,https://www.semanticscholar.org/paper/15eee39bb4d976b31d34beac789e7077fc4794e7,,semantic_scholar,,Leveraging mathematical Large Language Models (LLMs) for proof generation is a fundamental topic in LLMs research. We argue that the ability of current LLMs to prove statements largely depends on whether they have encountered the relevant proof process during training. This reliance limits their dee
165,,PDE-Controller: LLMs for Autoformalization and Reasoning of PDEs,Mauricio Soroco; Jialin Song; Mengzhou Xia; Kye Emond; Weiran Sun,2025,International Conference on Machine Learning,,,,,11,0.000,0.000,10.48550/arXiv.2502.00963,https://www.semanticscholar.org/paper/f4fc225ffe0b925f04ae1929f15d5406971f56b3,,semantic_scholar,,"While recent AI-for-math has made strides in pure mathematics, areas of applied mathematics, particularly PDEs, remain underexplored despite their significant real-world applications. We present PDE-Controller, a framework that enables large language models (LLMs) to control systems governed by part"
166,,Direct Reasoning Optimization: LLMs Can Reward And Refine Their Own Reasoning for Open-Ended Tasks,Yifei Xu; Tusher Chakraborty; Srinagesh Sharma; Leonardo Nunes; Emre Kiciman,2025,arXiv.org,,,,,9,0.000,0.000,10.48550/arXiv.2506.13351,https://www.semanticscholar.org/paper/1d1ae58b4c12c0f8d9774d0ce1410d34a3773422,,semantic_scholar,,"Recent advances in Large Language Models (LLMs) have showcased impressive reasoning abilities in structured tasks like mathematics and programming, largely driven by Reinforcement Learning with Verifiable Rewards (RLVR), which uses outcome-based signals that are scalable, effective, and robust again"
167,,EPO: Explicit Policy Optimization for Strategic Reasoning in LLMs via Reinforcement Learning,Xiaoqian Liu; Ke Wang; Yongbin Li; Yuchuan Wu; Wen-Cheng Ma,2025,Annual Meeting of the Association for Computational Linguistics,,,,,5,0.000,0.000,10.48550/arXiv.2502.12486,https://www.semanticscholar.org/paper/e495cdd1c62281b67677103650b885d4eaf151c5,,semantic_scholar,,"Large Language Models (LLMs) have shown impressive reasoning capabilities in well-defined problems with clear solutions, such as mathematics and coding. However, they still struggle with complex real-world scenarios like business negotiations, which require strategic reasoning-an ability to navigate"
168,,"Psyche-R1: Towards Reliable Psychological LLMs through Unified Empathy, Expertise, and Reasoning",Chongyuan Dai; Jinpeng Hu; Hongchang Shi; Zhuo Li; Xun Yang,2025,arXiv.org,,,,,4,0.000,0.000,10.48550/arXiv.2508.10848,https://www.semanticscholar.org/paper/16bc1e802ebe56fc6aba0e0f5fa9a9d9cf20c355,,semantic_scholar,,"Amidst a shortage of qualified mental health professionals, the integration of large language models (LLMs) into psychological applications offers a promising way to alleviate the growing burden of mental health disorders. Recent reasoning-augmented LLMs have achieved remarkable performance in mathe"
169,,Benchmarking LLMs'Mathematical Reasoning with Unseen Random Variables Questions,Zijin Hong; Hao Wu; Su Dong; Junnan Dong; Yilin Xiao,2025,,,,,,4,0.000,0.000,,https://www.semanticscholar.org/paper/ebec6f3b2780ebc3e2b6e7761bffc876fed505d2,,semantic_scholar,,"Recent studies have raised significant concerns regarding the reliability of current mathematics benchmarks, highlighting issues such as simplistic design and potential data contamination. Consequently, developing a reliable benchmark that effectively evaluates large language models'(LLMs) genuine c"
170,,HiPO: Hybrid Policy Optimization for Dynamic Reasoning in LLMs,Ken Deng; Zizheng Zhan; Wen Xiang; Wen-ya Zhu; Tianhao Peng,2025,arXiv.org,,,,,2,0.000,0.000,10.48550/arXiv.2509.23967,https://www.semanticscholar.org/paper/232f1332c85b96a06d59442260d81728a6fabcba,,semantic_scholar,,"Large Language Models (LLMs) increasingly rely on Chain-of-Thought (CoT) reasoning to improve accuracy on complex tasks. However, always generating lengthy reasoning traces is inefficient, leading to excessive token usage and higher inference costs. This paper introduces the Hybrid Policy Optimizati"
171,,Training LLMs for EHR-Based Reasoning Tasks via Reinforcement Learning,Jiacheng Lin; Zhenbang Wu; Jimeng Sun,2025,arXiv.org,,,,,3,0.000,0.000,10.48550/arXiv.2505.24105,https://www.semanticscholar.org/paper/d9d8984f2cf7e8eae12fe1a4b2f21e1bf7160202,,semantic_scholar,,"We present EHRMIND, a practical recipe for adapting large language models (LLMs) to complex clinical reasoning tasks using reinforcement learning with verifiable rewards (RLVR). While RLVR has succeeded in mathematics and coding, its application to healthcare contexts presents unique challenges due "
172,,"SAND-Math: Using LLMs to Generate Novel, Difficult and Useful Mathematics Questions and Answers",Chaitanya Manem; P. Brahma; Prakamya Mishra; Zicheng Liu; E. Barsoum,2025,arXiv.org,,,,,4,0.000,0.000,10.48550/arXiv.2507.20527,https://www.semanticscholar.org/paper/5dfb28ff182f11234ed685471b450b393e180c62,,semantic_scholar,,"The demand for Large Language Models (LLMs) at multiple scales, capable of sophisticated and sound mathematical reasoning, continues to grow. However, the development of performant mathematical LLMs is often bottlenecked by the scarcity of useful training data containing problems with significant co"
173,,Weaker LLMs' Opinions Also Matter: Mixture of Opinions Enhances LLM's Mathematical Reasoning,Yanan Chen; Ali Pesaranghader; Tanmana Sadhu,2025,arXiv.org,,,,,1,0.000,0.000,10.48550/arXiv.2502.19622,https://www.semanticscholar.org/paper/8ed670805abf99b723a3ae4fe9abc58ae1bd8c2e,,semantic_scholar,,"Recent advances in Large Language Models (LLMs) have raised interest in their formal reasoning capabilities, particularly in mathematics. While closed LLMs like GPT-4 perform well on mathematical benchmarks, e.g., GSM8K, it remains unclear whether small to medium-sized open LLMs can achieve similar "
174,,From Reasoning to Generalization: Knowledge-Augmented LLMs for ARC Benchmark,Chao Lei; N. Lipovetzky; Krista A. Ehinger; Yanchuan Chang,2025,arXiv.org,,,,,1,0.000,0.000,10.48550/arXiv.2505.17482,https://www.semanticscholar.org/paper/857b67284f81a87143abc8cf9911b7f14121b24f,,semantic_scholar,,"Recent reasoning-oriented LLMs have demonstrated strong performance on challenging tasks such as mathematics and science examinations. However, core cognitive faculties of human intelligence, such as abstract reasoning and generalization, remain underexplored. To address this, we evaluate recent rea"
175,,Thinking Machines: Mathematical Reasoning in the Age of LLMs,Andrea Asperti; Alberto Naibo; C. S. Coen,2025,arXiv.org,,,,,1,0.000,0.000,10.48550/arXiv.2508.00459,https://www.semanticscholar.org/paper/59d66d2bb1895cf54d2d89036db8e8092852c664,,semantic_scholar,,"Large Language Models (LLMs) have shown remarkable abilities in structured reasoning and symbolic tasks, with coding emerging as a particular area of strength. This success has sparked growing interest in applying LLMs to mathematics, both in informal problem-solving and formal theorem proving. Howe"
176,,SuperCLUE-Math6: Graded Multi-Step Math Reasoning Benchmark for LLMs in Chinese,Liang Xu; Hang Xue; Lei Zhu; Kangkang Zhao,2024,arXiv.org,,,,,10,0.000,0.000,10.48550/arXiv.2401.11819,https://www.semanticscholar.org/paper/b15ef4e49d4757316337dad2c65066227f10cbd0,,semantic_scholar,,"We introduce SuperCLUE-Math6(SC-Math6), a new benchmark dataset to evaluate the mathematical reasoning abilities of Chinese language models. SC-Math6 is designed as an upgraded Chinese version of the GSM8K dataset with enhanced difficulty, diversity, and application scope. It consists of over 2000 m"
177,,Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT,Jiawei Zhang,2023,arXiv.org,,,,,101,0.000,0.000,10.48550/arXiv.2304.11116,https://www.semanticscholar.org/paper/0d502a1e300336ae628f5c8b99ee4d3766c8f60b,http://arxiv.org/pdf/2304.11116,semantic_scholar,,"In this paper, we aim to develop a large language model (LLM) with the reasoning ability on complex graph data. Currently, LLMs have achieved very impressive performance on various natural language learning tasks, extensions of which have also been applied to study the vision tasks with multi-modal "
178,,GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models,Iman Mirzadeh; Keivan Alizadeh-Vahid; Hooman Shahrokhi; Oncel Tuzel; Samy Bengio,2024,International Conference on Learning Representations,,,,,397,0.000,0.000,,https://www.semanticscholar.org/paper/05506581cade1a8ef6372616cec20b81a3d5c366,,semantic_scholar,,"Recent advancements in Large Language Models (LLMs) have sparked interest in their formal reasoning capabilities, particularly in mathematics. The GSM8K benchmark is widely used to assess the mathematical reasoning of models on grade-school-level questions. While the performance of LLMs on GSM8K has"
179,,"Think Silently, Think Fast: Dynamic Latent Compression of LLM Reasoning Chains",Wenhui Tan; Jiaze Li; Jianzhong Ju; Zhenbo Luo; Jian Luan,2025,arXiv.org,,,,,17,0.000,0.000,10.48550/arXiv.2505.16552,https://www.semanticscholar.org/paper/ab4800a924508f49d644ced8ba236ec92f54f566,,semantic_scholar,,"Large Language Models (LLMs) achieve superior performance through Chain-of-Thought (CoT) reasoning, but these token-level reasoning chains are computationally expensive and inefficient. In this paper, we introduce Compressed Latent Reasoning (CoLaR), a novel framework that dynamically compresses rea"
180,,Exposing the Achilles' Heel: Evaluating LLMs Ability to Handle Mistakes in Mathematical Reasoning,Joykirat Singh; A. Nambi; Vibhav Vineet,2024,Annual Meeting of the Association for Computational Linguistics,,,,,9,0.000,0.000,10.48550/arXiv.2406.10834,https://www.semanticscholar.org/paper/a95e16c6bbf912a2452fc974d3f6a50482726877,,semantic_scholar,,"Large Language Models (LLMs) have been applied to Math Word Problems (MWPs) with transformative impacts, revolutionizing how these complex problems are approached and solved in various domains including educational settings. However, the evaluation of these models often prioritizes final accuracy, o"
181,,"Fast, Slow, and Tool-augmented Thinking for LLMs: A Review",Xinda Jia; Jinpeng Li; Zezhong Wang; Jingjing Li; Xingshan Zeng,2025,arXiv.org,,,,,0,0.000,0.000,10.48550/arXiv.2508.12265,https://www.semanticscholar.org/paper/7bdda23a6e293ff03db8be0a19fa51855e7de51c,,semantic_scholar,,"Large Language Models (LLMs) have demonstrated remarkable progress in reasoning across diverse domains. However, effective reasoning in real-world tasks requires adapting the reasoning strategy to the demands of the problem, ranging from fast, intuitive responses to deliberate, step-by-step reasonin"
182,,Integrating LLMs and RAG Techniques Into Mathematics Learning for Engineering Students,Mohamed Malek Gritli; Mohamed Anis Ben Lasmer; Mohamed Hedi Riahi; Lotfi Ncib,2024,Advanced Robotics,,,,,0,0.000,0.000,10.1109/AIR63653.2024.00021,https://www.semanticscholar.org/paper/c98f3d2fcba60f714b46da82ed1e27916046fe52,,semantic_scholar,,"In this study, we present a novel pipeline designed to enhance the capabilities of large language models (LLMs) for solving mathematical problems (exercises, course) using a retrieval-augmented generation (RAG) methodology. Recog-nizing that a straightforward RAG approach is inadequate for significa"
183,,Extracting Reasoning Patterns from Knowledge Graph to Enhance LLMs' Reasoning Capability,Tingkai Yang; Yuanzhao Zhai; Huanxi Liu; Dawei Feng; Huaimin Wang,2025,Fall Joint Computer Conference,,,,,0,0.000,0.000,10.1109/JCC67032.2025.00010,https://www.semanticscholar.org/paper/65ec23977f6bbe06688961f4ab65b663b536c00f,,semantic_scholar,,"Large language models (LLMs) have demonstrated great potential across diverse fields. However, their reasoning capabilities face challenges, especially when dealing with complex tasks. Existing work on enhancing LLMs' reasoning abilities mostly focuses on fields like mathematics and coding. Due to t"
184,,Evaluating LLMs on Kazakhstan's mathematics exam for university admission,Shirali Kadyrov; Bolatbek Abdrasilov; A. Sabyrov; N. Baizhanov; Alfira Makhmutova,2025,Frontiers in Artificial Intelligence,,,,,0,0.000,0.000,10.3389/frai.2025.1642570,https://www.semanticscholar.org/paper/1178192ba92ac054078d83f4b7adfb203cb4dc99,,semantic_scholar,,"Introduction The rapid advancement of large language models (LLMs) has prompted their exploration in educational contexts, particularly in high-stakes standardized tests such as Kazakhstan's Unified National Testing (UNT) mathematics component, which is critical for university admission. While most "
185,,ChemAU: Harness the Reasoning of LLMs in Chemical Research with Adaptive Uncertainty Estimation,Xinyi Liu; Lipeng Ma; Yixuan Li; Weidong Yang; Qingyuan Zhou,2025,arXiv.org,,,,,0,0.000,0.000,10.48550/arXiv.2506.01116,https://www.semanticscholar.org/paper/19b7d83e982518c76e3c19cda532ac13f03592d5,,semantic_scholar,,"Large Language Models (LLMs) are widely used across various scenarios due to their exceptional reasoning capabilities and natural language understanding. While LLMs demonstrate strong performance in tasks involving mathematics and coding, their effectiveness diminishes significantly when applied to "
186,,Thinking Fast and Right: Balancing Accuracy and Reasoning Length with Adaptive Rewards,Jinyan Su; Claire Cardie,2025,arXiv.org,,,,,6,0.000,0.000,10.48550/arXiv.2505.18298,https://www.semanticscholar.org/paper/0ba8cbebdbb3a91f984313d33e76fe5946b96ad4,,semantic_scholar,,"Large language models (LLMs) have demonstrated strong reasoning abilities in mathematical tasks, often enhanced through reinforcement learning (RL). However, RL-trained models frequently produce unnecessarily long reasoning traces -- even for simple queries -- leading to increased inference costs an"
187,,"Fast on the Easy, Deep on the Hard: Efficient Reasoning via Powered Length Penalty",Zehui Ling; Deshu Chen; Hongwei Zhang; Yifeng Jiao; Xin Guo,2025,arXiv.org,,,,,8,0.000,0.000,10.48550/arXiv.2506.10446,https://www.semanticscholar.org/paper/010c508b6e23d253bb47934ea3433251d3aebc33,,semantic_scholar,,"Large language models (LLMs) have demonstrated significant advancements in reasoning capabilities, performing well on various challenging benchmarks. Techniques like Chain-of-Thought prompting have been introduced to further improve reasoning. However, these approaches frequently generate longer out"
188,,Scheherazade: Evaluating Chain-of-Thought Math Reasoning in LLMs with Chain-of-Problems,Stephen Miner; Yoshiki Takashima; Simeng Han; Ferhat Erata; Timos Antonopoulos,2024,arXiv.org,,,,,8,0.000,0.000,10.48550/arXiv.2410.00151,https://www.semanticscholar.org/paper/a3eacbe7f70240a5c8830ec729ad786d018b922e,,semantic_scholar,,"Benchmarks are critical for measuring Large Language Model (LLM) reasoning capabilities. Some benchmarks have even become the de facto indicator of such capabilities. However, as LLM reasoning capabilities improve, existing widely-used benchmarks such as GSM8K marginally encapsulate model reasoning "
189,,"Fast Think-on-Graph: Wider, Deeper and Faster Reasoning of Large Language Model on Knowledge Graph",Xujiang Liang; Zhaoquan Gu,2025,AAAI Conference on Artificial Intelligence,,,,,5,0.000,0.000,10.48550/arXiv.2501.14300,https://www.semanticscholar.org/paper/1dc16188838dfd053a17e77a5154348f62c5db33,,semantic_scholar,,"Graph Retrieval Augmented Generation (GRAG) is a novel paradigm that takes the naive RAG system a step further by integrating graph information, such as knowledge graph (KGs), into large-scale language models (LLMs) to mitigate hallucination. However, existing GRAG still encounter limitations: 1) si"
190,,Enhancing Mathematical Reasoning in LLMs by Stepwise Correction,Zhenyu Wu; Qingkai Zeng; Zhihan Zhang; Zhaoxuan Tan; Chao Shen,2024,Annual Meeting of the Association for Computational Linguistics,,,,,7,0.000,0.000,10.48550/arXiv.2410.12934,https://www.semanticscholar.org/paper/8d402896d7afd49cc1289978347347c8c1c0dfc1,,semantic_scholar,,"Best-of-N decoding methods instruct large language models (LLMs) to generate multiple solutions, score each using a scoring function, and select the highest scored as the final answer to mathematical reasoning problems. However, this repeated independent process often leads to the same mistakes, mak"
191,,Multilingual Mathematical Reasoning: Advancing Open-Source LLMs in Hindi and English,Avinash Anand; Kritarth Prasad; Chhavi Kirtani; Ashwin R Nair; Manvendra Kumar Nema,2024,AAAI Conference on Artificial Intelligence,,,,,4,0.000,0.000,10.48550/arXiv.2412.18415,https://www.semanticscholar.org/paper/8147c5503cb8ea33ed25540c854fdfadaabc1032,,semantic_scholar,,"Large Language Models (LLMs) excel in linguistic tasks but struggle with mathematical reasoning, particularly in non- English languages like Hindi. This research aims to en- hance the mathematical reasoning skills of smaller, resource- efficient open-source LLMs in both Hindi and English. We evaluat"
192,,Federated Prompting and Chain-of-Thought Reasoning for Improving LLMs Answering,Xiangyang Liu; Tianqi Pang; Chenyou Fan,2023,"Knowledge Science, Engineering and Management",,,,,30,0.000,0.000,10.48550/arXiv.2304.13911,https://www.semanticscholar.org/paper/a7c0d9bf44045c9d4c41e329e2a87df0ae7e0af6,http://arxiv.org/pdf/2304.13911,semantic_scholar,,We investigate how to enhance answer precision in frequently asked questions posed by distributed users using cloud-based Large Language Models (LLMs). Our study focuses on a typical situations where users ask similar queries that involve identical mathematical reasoning steps and problem-solving pr
193,,FRoG: Evaluating Fuzzy Reasoning of Generalized Quantifiers in LLMs,Yiyuan Li; Shichao Sun; Pengfei Liu,2024,Conference on Empirical Methods in Natural Language Processing,,,,,0,0.000,0.000,10.18653/v1/2024.emnlp-main.411,https://www.semanticscholar.org/paper/3b0bd8fc6dfe28840e282f20bb195b7d105bd6a6,,semantic_scholar,,"Fuzzy reasoning is vital due to the frequent use of imprecise information in daily contexts. However, the ability of current large language models (LLMs) to handle such reasoning remains largely uncharted. In this paper, we introduce a new benchmark, FRoG, for fuzzy reasoning, featuring real-world m"
194,,"Guiding LLMs The Right Way: Fast, Non-Invasive Constrained Generation",Luca Beurer-Kellner; Marc Fischer; Martin T. Vechev,2024,International Conference on Machine Learning,,,,,72,0.000,0.000,10.48550/arXiv.2403.06988,https://www.semanticscholar.org/paper/b95ca121a606da32180ab8bb0c0c58bf19b1499b,,semantic_scholar,,"To ensure that text generated by large language models (LLMs) is in an expected format, constrained decoding proposes to enforce strict formal language constraints during generation. However, as we show in this work, not only do such methods incur performance overhead during generation, but many of "
195,,Hallucination Detection in LLMs: Fast and Memory-Efficient Finetuned Models,Gabriel Y. Arteaga; Thomas B. Schön; Nicolas Pielawski,2024,NLDL,,,,,20,0.000,0.000,10.48550/arXiv.2409.02976,https://www.semanticscholar.org/paper/e66f9a0fbda43e311d51ab0f0e9d1637c41b3385,,semantic_scholar,,"Uncertainty estimation is a necessary component when implementing AI in high-risk settings, such as autonomous cars, medicine, or insurances. Large Language Models (LLMs) have seen a surge in popularity in recent years, but they are subject to hallucinations, which may cause serious harm in high-ris"
196,,"A Comprehensive Study on NLP Data Augmentation for Hate Speech Detection: Legacy Methods, BERT, and LLMs",Md Saroar Jahan; M. Oussalah; D. Beddiar; Jhuma Kabir Mim; Nabil Arhab,2024,arXiv.org,,,,,16,0.000,0.000,10.48550/arXiv.2404.00303,https://www.semanticscholar.org/paper/8fbeb8fff13e9489d6e9f0cf7459e18b024b3d10,,semantic_scholar,,"The surge of interest in data augmentation within the realm of NLP has been driven by the need to address challenges posed by hate speech domains, the dynamic nature of social media vocabulary, and the demands for large-scale neural networks requiring extensive training data. However, the prevalent "
197,,FASTTRACK: Fast and Accurate Fact Tracing for LLMs,Si Chen; Feiyang Kang; Ning Yu; Ruoxi Jia,2024,arXiv.org,,,,,3,0.000,0.000,10.48550/arXiv.2404.15157,https://www.semanticscholar.org/paper/187eaacc793b283bc9295fbca5238e158631cdc7,,semantic_scholar,,"Fact tracing seeks to identify specific training examples that serve as the knowledge source for a given query. Existing approaches to fact tracing rely on assessing the similarity between each training sample and the query along a certain dimension, such as lexical similarity, gradient, or embeddin"
198,,Small and Fast LLMs on Commodity Hardware: Post-Training Quantization in llama. cpp,Lorenz Sparrenberg; Tobias Deuβer; Armin Berger; R. Sifa,2025,International Conference on Data Science and Advanced Analytics,,,,,0,0.000,0.000,10.1109/DSAA65442.2025.11247985,https://www.semanticscholar.org/paper/972768785c213c433e853d6ca68a02cb5b3af745,,semantic_scholar,,"Large Language Models (LLMs) have demonstrated remarkable capabilities but their significant computational and memory demands hinder widespread deployment, especially on resource-constrained devices. Quantization, the process of reducing the numerical precision of model parameters, has emerged as a "
199,,Efficient Adversarial Training in LLMs with Continuous Attacks,Sophie Xhonneux; Alessandro Sordoni; Stephan Günnemann; G. Gidel; Leo Schwinn,2024,Neural Information Processing Systems,,,,,91,0.000,0.000,10.48550/arXiv.2405.15589,https://www.semanticscholar.org/paper/723ae07c5c8921eb94ac52528c8a53bec837c5a9,,semantic_scholar,,"Large language models (LLMs) are vulnerable to adversarial attacks that can bypass their safety guardrails. In many domains, adversarial training has proven to be one of the most promising methods to reliably improve robustness against such attacks. Yet, in the context of LLMs, current methods for a"
200,,Everybody Prune Now: Structured Pruning of LLMs with only Forward Passes,L. Dery; Steven Kolawole; Jean-Francois Kagey; Virginia Smith; Graham Neubig,2024,arXiv.org,,,,,46,0.000,0.000,10.48550/arXiv.2402.05406,https://www.semanticscholar.org/paper/6f7f4f97890dae5cdee3d5df9ca932cae486e1ba,,semantic_scholar,,"Structured pruning is a promising approach to create smaller, faster LLMs. However, existing methods typically rely on backward passes, which can inflate memory requirements and compute costs. In this work we introduce Bonsai, a gradient-free structured pruning method that eliminates the need for ba"
201,,Integrating LLMs into Mathematics Laboratories Alongside CAS,Nikolaos Matzakos; Maria Moundridou,2025,Int. J. Eng. Pedagog.,,,,,1,0.000,0.000,10.3991/ijep.v15i3.53151,https://www.semanticscholar.org/paper/b3e0df73848d18309b827b98d58550255a6a028d,,semantic_scholar,,"This paper proposes a structured approach for integrating large language models (LLMs) and computer algebra systems (CAS) in mathematics laboratories to enhance learning in higher education. By combining LLMs with CAS, this method provides a balanced environment where students engage actively in sol"
202,,EBFT: Effective and Block-Wise Fine-Tuning for Sparse LLMs,Song Guo; Fan Wu; Lei Zhang; Xiawu Zheng; Shengchuan Zhang,2024,arXiv.org,,,,,7,0.000,0.000,10.48550/arXiv.2402.12419,https://www.semanticscholar.org/paper/0c41cfc99c77c0cca5eff8767ac60128dafa124a,,semantic_scholar,,"Existing methods for fine-tuning sparse LLMs often suffer from resource-intensive requirements and high retraining costs. Additionally, many fine-tuning methods often rely on approximations or heuristic optimization strategies, which may lead to suboptimal solutions. To address these issues, we prop"
203,,Rollout Roulette: A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods,Isha Puri; Shivchander Sudalairaj; Guangxuan Xu; Kai Xu; Akash Srivastava,2025,,,,,,4,0.000,0.000,,https://www.semanticscholar.org/paper/38af83f87c61177aa96eeba8a07a1e0922241532,,semantic_scholar,,"Large language models (LLMs) have achieved significant performance gains via scaling up model sizes and/or data. However, recent evidence suggests diminishing returns from such approaches, motivating scaling the computation spent at inference time. Existing inference-time scaling methods, usually wi"
204,,Have LLMs Advanced Enough? A Challenging Problem Solving Benchmark For Large Language Models,Daman Arora; H. Singh; Mausam,2023,Conference on Empirical Methods in Natural Language Processing,,,,,75,0.000,0.000,10.48550/arXiv.2305.15074,https://www.semanticscholar.org/paper/2cf1f6c723006f258599fd9f000bb616ae83387a,https://arxiv.org/pdf/2305.15074,semantic_scholar,,"The performance of large language models (LLMs) on existing reasoning benchmarks has significantly improved over the past years. In response, we present JEEBench, a considerably more challenging benchmark dataset for evaluating the problem solving abilities of LLMs. We curate 515 challenging pre-eng"
205,,PipeRAG: Fast Retrieval-Augmented Generation via Algorithm-System Co-design,Wenqi Jiang; Shuai Zhang; Boran Han; Jie Wang; Bernie Wang,2024,arXiv.org,,,,,45,0.000,0.000,10.48550/arXiv.2403.05676,https://www.semanticscholar.org/paper/858cbd99d5a3d2658254d055cd26e06f81050927,,semantic_scholar,,"Retrieval-augmented generation (RAG) can enhance the generation quality of large language models (LLMs) by incorporating external token databases. However, retrievals from large databases can constitute a substantial portion of the overall generation time, particularly when retrievals are periodical"
206,,TheoremLlama: Transforming General-Purpose LLMs into Lean4 Experts,Ruida Wang; Jipeng Zhang; Yizhen Jia; Rui Pan; Shizhe Diao,2024,Conference on Empirical Methods in Natural Language Processing,,,,,45,0.000,0.000,10.48550/arXiv.2407.03203,https://www.semanticscholar.org/paper/a9de58b1ad7e052dfc21f0363ec3809b5f9e5b7b,,semantic_scholar,,"Proving mathematical theorems using computer-verifiable formal languages like Lean significantly impacts mathematical reasoning. One approach to formal theorem proving involves generating complete proofs using Large Language Models (LLMs) based on Natural Language (NL) proofs. However, due to the sc"
207,,How to Train Data-Efficient LLMs,Noveen Sachdeva; Benjamin Coleman; Wang-Cheng Kang; Jianmo Ni; Lichan Hong,2024,arXiv.org,,,,,90,0.000,0.000,10.48550/arXiv.2402.09668,https://www.semanticscholar.org/paper/31b2918acc77682b5a499da1aa111f34e76d9603,,semantic_scholar,,"The training of large language models (LLMs) is expensive. In this paper, we study data-efficient approaches for pre-training LLMs, i.e., techniques that aim to optimize the Pareto frontier of model quality and training resource/data consumption. We seek to understand the tradeoffs associated with d"
208,,"Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data",Fahim Tajwar; Anikait Singh; Archit Sharma; Rafael Rafailov; Jeff Schneider,2024,International Conference on Machine Learning,,,,,168,0.000,0.000,10.48550/arXiv.2404.14367,https://www.semanticscholar.org/paper/cd3359ed7119210bfa0b34ba5796d1314a05e212,,semantic_scholar,,"Learning from preference labels plays a crucial role in fine-tuning large language models. There are several distinct approaches for preference fine-tuning, including supervised learning, on-policy reinforcement learning (RL), and contrastive learning. Different methods come with different implement"
209,,BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via Self-Distillation,Dayou Du; Yijia Zhang; Shijie Cao; Jiaqi Guo; Ting Cao,2024,Annual Meeting of the Association for Computational Linguistics,,,,,59,0.000,0.000,10.48550/arXiv.2402.10631,https://www.semanticscholar.org/paper/52258e9110f8e73cca24f6394bfc483341b459a4,,semantic_scholar,,"The upscaling of Large Language Models (LLMs) has yielded impressive advances in natural language processing, yet it also poses significant deployment challenges. Weight quantization has emerged as a widely embraced solution to reduce memory and computational demands. This paper introduces BitDistil"
210,,HOBBIT: A Mixed Precision Expert Offloading System for Fast MoE Inference,Peng Tang; Jiacheng Liu; Xiaofeng Hou; Yifei Pu; Jing Wang,2024,arXiv.org,,,,,26,0.000,0.000,10.48550/arXiv.2411.01433,https://www.semanticscholar.org/paper/300f45605c373826bf7d1800749bc85305276a7f,,semantic_scholar,,"The Mixture-of-Experts (MoE) architecture has demonstrated significant advantages in the era of Large Language Models (LLMs), offering enhanced capabilities with reduced inference costs. However, deploying MoE-based LLMs on memoryconstrained edge devices remains challenging due to their substantial "
211,,GaLore+: Boosting Low-Rank Adaptation for LLMs with Cross-Head Projection,Xutao Liao; Shaohui Li; Yuhui Xu; Zhi Li; Yu Liu,2024,arXiv.org,,,,,7,0.000,0.000,10.48550/arXiv.2412.19820,https://www.semanticscholar.org/paper/aad8d15c32ef86a1ad77cbd3d432f6d9d4183ac6,,semantic_scholar,,"Recent low-rank training methods, such as GaLore, have significantly reduced the memory required to optimize large language models (LLMs). However, these methods often suffer from time-consuming low-rank projection estimations. In particular, the singular value decomposition (SVD) in GaLore can cons"