20260102_111412/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,article-26896,Solving Math Word Problems concerning Systems of Equations with GPT-3,Mingyu Zong; Bhaskar Krishnamachari,2023,AAAI 2023,eaai symposium ai for education,Technical,,,0,27.052,0.000,,https://ojs.aaai.org/index.php/AAAI/article/view/26896,https://ojs.aaai.org/index.php/AAAI/article/view/26896/26668,offline_aaai,,"Researchers have been interested in developing AI tools to help students learn various mathematical subjects. One challenging set of tasks for school students is learning to solve math word problems. We explore how recent advances in natural language processing, specifically the rise of powerful tra"
2,2024.acl-long.852,DocMath-Eval: Evaluating Math Reasoning Capabilities of LLMs in Understanding Long and Specialized Documents,Yilun Zhao; Yitao Long; Hongjun Liu; Ryo Kamoi; Linyong Nan,2024,ACL 2024,main,Long,,,0,26.334,0.000,,https://aclanthology.org/2024.acl-long.852/,https://aclanthology.org/2024.acl-long.852.pdf,offline_acl,,"Recent LLMs have demonstrated remarkable performance in solving exam-like math word problems. However, the degree to which these numerical reasoning skills are effective in real-world scenarios, particularly in expert domains, is still largely unexplored. This paper introduces DocMath-Eval, a compre"
3,8wIgDG87jn,MorphAgent: Empowering Agents through Self-Evolving Profiles and Decentralized Collaboration,Siyuan Lu; Jiaqi Shao; Bing Luo; Tao Lin,2025,ICLR 2025,main,Reject,"other topics in machine learning (i.e., none of the above)",self-evolving LLM agent;multi-agent collaboration,0,26.121,0.000,,https://openreview.net/forum?id=8wIgDG87jn,,offline_iclr,,"Large Language Model (LLM) based multi-agent systems (MAS) have shown promise in tackling complex tasks, but often rely on predefined roles and centralized coordination, limiting their adaptability to evolving challenges. This paper introduces $MorphAgent$, a novel framework for $\textit{decentraliz"
4,E4hK8t7Fts,Improving Large Language Model Fine-tuning for Solving Math Problems,Yixin Liu; Avi Singh; C. Daniel Freeman; John D Co-Reyes; Peter J Liu,2024,ICLR 2024,main,Reject,"representation learning for computer vision, audio, language, and other modalities",Math Problem Solving;Large Language Models,0,25.507,0.000,,https://openreview.net/forum?id=E4hK8t7Fts,,offline_iclr,,"Despite their success in many natural language tasks, solving math problems remains a significant challenge for large language models (LLMs). A large gap exists between LLMs' pass-at-one and pass-at-N performance in solving math problems, suggesting LLMs might be close to finding correct solutions, "
5,2024.findings-emnlp.980,Solving for X and Beyond: Can Large Language Models Solve Complex Math Problems with More-Than-Two Unknowns?,Kuei-Chun Kao; Ruochen Wang; Cho-Jui Hsieh,2024,EMNLP 2024,main,finding,,,0,25.488,0.000,,https://aclanthology.org/2024.findings-emnlp.980/,https://aclanthology.org/2024.findings-emnlp.980.pdf,offline_emnlp,,"Large Language Models have demonstrates remarkable performance in solving math problems, a hallmark of human intelligence.Despite high success rates on current benchmarks, however, these often feature simple problems with only one or two unknowns, which do not sufficiently challenge their reasoning "
6,article-29949,MathAttack: Attacking Large Language Models towards Math Solving Ability,Zihao Zhou; Qiufeng Wang; Mingyu Jin; Jie Yao; Jianan Ye,2024,AAAI 2024,main,Technical,natural language processing ii,,0,24.898,0.000,,https://ojs.aaai.org/index.php/AAAI/article/view/29949,https://ojs.aaai.org/index.php/AAAI/article/view/29949/31658,offline_aaai,,"With the boom of Large Language Models (LLMs), the research of solving Math Word Problem (MWP) has recently made great progress. However, there are few studies to examine the robustness of LLMs in math solving ability. Instead of attacking prompts in the use of LLMs, we propose a MathAttack model to"
7,2025.coling-industry.40,BackMATH: Towards Backward Reasoning for Solving Math Problems Step by Step,Shaowei Zhang; Deyi Xiong,2025,COLING 2025,main,Industry,,,0,24.896,0.000,,https://aclanthology.org/2025.coling-industry.40/,https://aclanthology.org/2025.coling-industry.40.pdf,offline_coling,,"Large language models (LLMs) have achieved impressive results in reasoning, particularly in multi-step reasoning tasks. However, when faced with more complex mathematical problems, the performance of LLMs drops significantly. To address this issue, in this paper, we propose a backward reasoning data"
8,4R71pdPBZp,Self-Evolving Multi-Agent Collaboration Networks for Software Development,Yue Hu; Yuzhu Cai; Yaxin Du; Xinyu Zhu; Xiangrui Liu,2025,ICLR 2025,main,Poster,"foundation or frontier models, including LLMs",Software development;LLM;Multi-agent collaboration,0,24.774,0.000,,https://iclr.cc/virtual/2025/poster/31011,https://openreview.net/pdf?id=4R71pdPBZp,offline_iclr,,"LLM-driven multi-agent collaboration (MAC) systems have demonstrated impressive capabilities in automatic software development at the function level. However, their heavy reliance on human design limits their adaptability to the diverse demands of real-world software development.
To address this lim"
9,VR2RdSxtzs,MACM: Utilizing a Multi-Agent System for Condition Mining in Solving Complex Mathematical Problems,Bin Lei; Yi Zhang; Shan Zuo; Ali Payani; Caiwen Ding,2024,NIPS 2024,main,Poster,natural_language_processing,Multi-Agent;Prompting;LLM;Math problem,0,24.678,0.000,,https://neurips.cc/virtual/2024/poster/94899,https://openreview.net/pdf?id=VR2RdSxtzs,offline_nips,,"Recent advancements in large language models, such as GPT-4, have demonstrated remarkable capabilities in processing standard queries. Despite these advancements, their performance substantially declines in advanced mathematical problems requiring complex, multi-step logical reasoning. To enhance th"
10,cZi1njoRT6,Leveraging Online Olympiad-Level Math Problems for LLMs Training and Contamination-Resistant Evaluation,Sadegh Mahdavi; Muchen Li; Kaiwen Liu; Christos Thrampoulidis; Leonid Sigal,2025,ICML 2025,main,Poster,deep_learning->large_language_models,Mathematical Reasoning;Large Language Models;Evaluation,0,24.291,0.000,,https://icml.cc/virtual/2025/poster/44681,https://openreview.net/pdf?id=cZi1njoRT6,offline_icml,,"Advances in Large Language Models (LLMs) have sparked interest in their ability to solve Olympiad-level math problems. 
However, the training and evaluation of these models are constrained by the limited size and quality of available datasets, as creating large-scale data for such advanced problems "
11,xToDNYv0GZ,EvoCurr: Self-evolving Curriculum with Behavior Code Generation for Complex Decision-making,,2026,ICLR 2026,main,Active,"applications to computer vision, audio, language, and other modalities",LLM Agents;Complex Task;Behavior Code;Self-evolve,0,24.033,0.000,,https://openreview.net/forum?id=xToDNYv0GZ,,offline_iclr,,"While large language models (LLMs) demonstrate remarkable capabilities across diverse domains, they fail catastrophically on high-complexity tasks requiring long-horizon reasoning and multi-step coordination. To address this problem, we present EvoCurr, a self-evolving curriculum learning framework "
12,Bgz3okeZ7H,AoPS Dataset: Leveraging Online Olympiad-Level Math Problems for LLMs Training and Contamination-Resistant Evaluation,Sadegh Mahdavi; Muchen Li; Kaiwen Liu; Christos Thrampoulidis; Leonid Sigal,2025,ICLR 2025,main,Reject,"foundation or frontier models, including LLMs",Mathematical Reasoning;Large Language Models,0,23.997,0.000,,https://openreview.net/forum?id=Bgz3okeZ7H,,offline_iclr,,"Advances in Large Language Models (LLMs) have sparked interest in their ability to solve Olympiad-level math problems. 
However, the training and evaluation of these models are constrained by the limited size and quality of available datasets, as creating large-scale data for such advanced problems "
13,2024.acl-long.693,FinanceMATH: Knowledge-Intensive Math Reasoning in Finance Domains,Yilun Zhao; Hongjun Liu; Yitao Long; Rui Zhang; Chen Zhao,2024,ACL 2024,main,Long,,,0,23.906,0.000,,https://aclanthology.org/2024.acl-long.693/,https://aclanthology.org/2024.acl-long.693.pdf,offline_acl,,"We introduce FinanceMath, a novel benchmark designed to evaluate LLMs' capabilities in solving knowledge-intensive math reasoning problems. Compared to prior works, this study features three core advancements. First, FinanceMath includes 1,200 problems with a hybrid of textual and tabular content. T"
14,article-34640,Augmenting Math Word Problems via Iterative Question Composing,Haoxiong Liu; Yifan Zhang; Yifan Luo; Andrew C Yao,2025,AAAI 2025,main,Technical,natural language processing ii,,0,23.844,0.000,,https://ojs.aaai.org/index.php/AAAI/article/view/34640,https://ojs.aaai.org/index.php/AAAI/article/view/34640/36795,offline_aaai,,"Despite the advancements in large language models (LLMs) for mathematical reasoning, solving competition-level math problems remains a significant challenge, especially for open-source LLMs without external tools. We introduce the MMIQC dataset, comprising a mixture of processed web data and synthet"
15,paper485,Solving Math Word Problems with Teacher Supervision,Zhenwen Liang; Xiangliang Zhang,2021,IJCAI 2021,main,Poster,Machine Learning Applications,Machine Learning Applications: Applications of Supervised Learning; Natural Language Processing: Question Answering,0,23.829,0.000,,https://www.ijcai.org/proceedings/2021/485,https://www.ijcai.org/proceedings/2021/0485.pdf,offline_ijcai,,"Math word problems (MWPs) have been recently addressed with Seq2Seq models by `translating' math problems described in natural language to a mathematical expression, following a typical encoder-decoder structure. Although effective in solving classical math problems, these models fail when a subtle "
16,z8TW0ttBPp,MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning,Ke Wang; Houxing Ren; Aojun Zhou; Zimu Lu; Sichun Luo,2024,ICLR 2024,main,Poster,generative models,mathematical reasoning;large language models;code generation,0,23.699,0.000,,https://iclr.cc/virtual/2024/poster/17389,https://openreview.net/pdf?id=z8TW0ttBPp,offline_iclr,,"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,"
17,YDmkmdoC3M,Enumerate-Conjecture-Prove: Formally Solving Answer-Construction Problems in Math Competitions,Jialiang Sun; Yuzhi Tang; Ao Li; Chris J. Maddison; Kuldeep S. Meel,2026,ICLR 2026,main,Withdraw,"neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)",Automated Reasoning;Theorem Proving;Autoformalization;AI for Math;LLM,0,23.560,0.000,,https://openreview.net/forum?id=YDmkmdoC3M,,offline_iclr,,"Mathematical reasoning is central to artificial intelligence, with applications in education, code generation, and research-level mathematical discovery. Mathematical competitions highlight two problem types: theorem-proving, requiring rigorous proofs, and answer-construction, requiring creative gen"
18,Vcw3vzjHDb,Lean Workbook: A large-scale Lean problem set formalized from natural language math problems,Huaiyuan Ying; Zijian Wu; Yihan Geng; JIayu Wang; Dahua Lin,2024,NIPS 2024,Datasets & Benchmarks,Poster,,Formal language proving;Translation;Lean4;expert iteration;dataset,0,23.488,0.000,,https://neurips.cc/virtual/2024/poster/97664,https://openreview.net/pdf?id=Vcw3vzjHDb,offline_nips,,"Large language models have demonstrated impressive capabilities across various natural language processing tasks, especially in solving mathematical problems. However, large language models are not good at math theorem proving using formal languages like Lean. A significant challenge in this area is"
19,2023.findings-acl.579,World Models for Math Story Problems,Andreas Opedal; Niklas Stoehr; Abulhair Saparov; Mrinmaya Sachan,2023,ACL 2023,main,Findings,,,0,23.417,0.000,,https://aclanthology.org/2023.findings-acl.579/,https://aclanthology.org/2023.findings-acl.579.pdf,offline_acl,,"Solving math story problems is a complex task for students and NLP models alike, requiring them to understand the world as described in the story and reason over it to compute an answer. Recent years have seen impressive performance on automatically solving these problems with large pre-trained lang"
20,V0Fb4pwhS4,HexMachina: Self-Evolving Multi-Agent System for Continual Learning of Catan,,2026,ICLR 2026,main,Active,"transfer learning, meta learning, and lifelong learning",Multi-Agent System;LLM;Strategic Planning;Lifelong Learning,0,23.353,0.000,,https://openreview.net/forum?id=V0Fb4pwhS4,,offline_iclr,,"We aim to improve on the long-horizon gaps in large language model (LLM) agents by enabling them to sustain coherent strategies in adversarial, stochastic environments. Settlers of Catan provides a challenging benchmark: strategic success depends on balancing short- and long-term goals in the face o"
21,ptUxbqOGrC,Diversity for The Win: Towards Building Multi-Agent Systems with Heterogeneous LLMs,,2026,ICLR 2026,main,Active,datasets and benchmarks,LLM;Multi-Agent Systems,0,23.245,0.000,,https://openreview.net/forum?id=ptUxbqOGrC,,offline_iclr,,"LLM-based multi-agent systems (MAS) extend the capabilities of single LLMs by enabling cooperation among multiple specialized agents. However, most existing MAS frameworks rely on a single LLM to drive all agents, constraining the system's intelligence to the limitations of that model. This paper ex"
22,BRG43Nc7ec,SafeEvalAgent: Toward Agentic and Self-Evolving Safety Evaluation of LLMs,Yixu Wang; Xin Wang; Yang Yao; Xinyuan Li; Yan Teng,2026,ICLR 2026,main,Withdraw,"alignment, fairness, safety, privacy, and societal considerations",Large Language Models;Agentic Safety Evaluation;AI Compliance and Safety,0,23.142,0.000,,https://openreview.net/forum?id=BRG43Nc7ec,,offline_iclr,,"The rapid integration of Large Language Models (LLMs) into high-stakes domains necessitates reliable safety and compliance evaluation. 
However, existing static benchmarks are ill-equipped to address the dynamic nature of AI risks and evolving regulations, creating a critical safety gap. 
This paper"
23,paper38,Learning Optimal Temperature Region for Solving Mixed Integer Functional DCOPs,Saaduddin Mahmud; Md. Mosaddek Khan; Moumita Choudhury; Long Tran-Thanh; Nicholas R. Jennings,2020,IJCAI 2020,main,Poster,Agent-based and Multi-agent Systems,Agent-based and Multi-agent Systems: Coordination and Cooperation; Constraints and SAT: Constraint Optimization; Constraints and SAT: Distributed Constraints; Agent-based and Multi-agent Systems: Multi-agent Learning,0,23.066,0.000,,https://www.ijcai.org/proceedings/2020/38,https://www.ijcai.org/proceedings/2020/0038.pdf,offline_ijcai,,"Distributed Constraint Optimization Problems (DCOPs) are an important framework for modeling coordinated decision-making problems in multi-agent systems with a set of discrete variables. Later works have extended DCOPs to model problems with a set of continuous variables, named Functional DCOPs (F-D"
24,owR9ofvkFQ,MathOdyssey: Benchmarking Mathematical Problem-Solving Skills in Large Language Models Using Odyssey Math Data,Meng Fang; Xiangpeng Wan; Fei Lu; Fei Xing; Kai Zou,2025,ICLR 2025,main,Withdraw,datasets and benchmarks,Math;LLMs,0,22.833,0.000,,https://openreview.net/forum?id=owR9ofvkFQ,,offline_iclr,,"Large language models (LLMs) have significantly advanced natural language understanding and demonstrated strong problem-solving abilities. Despite these successes, most LLMs still struggle with solving mathematical problems due to the intricate reasoning required. This paper investigates the mathema"
25,MvlBFwAwK8,A Survey of LLM-based Multi-agent Systems in Medicine,Yanna Lin; Shaojie Xu; Wenshuo Zhang; Yushi Sun; Zixin CHEN,2026,ICLR 2026,main,Withdraw,"foundation or frontier models, including LLMs",Survey;LLM-based Multi-agent Systems;Medical domain,0,22.729,0.000,,https://openreview.net/forum?id=MvlBFwAwK8,,offline_iclr,,"Large Language Model (LLM)-based multi-agent systems have shown great potential in supporting complex tasks in the medical domain, such as improving diagnostic accuracy and facilitating multidisciplinary collaboration. However, despite the advancement, there is a lack of structured frameworks to gui"
26,Kjww7ZN47M,MathScale: Scaling Instruction Tuning for Mathematical Reasoning,Zhengyang Tang; Xingxing Zhang; Benyou Wang; Furu Wei,2024,ICML 2024,main,Poster,,,0,22.605,0.000,,https://icml.cc/virtual/2024/poster/34329,https://openreview.net/pdf?id=Kjww7ZN47M,offline_icml,,"Large language models (LLMs) have demonstrated remarkable capabilities in problem-solving. However, their proficiency in solving mathematical problems remains inadequate. We propose MathScale, a simple and scalable method to create high-quality mathematical reasoning data using frontier LLMs (e.g., "
27,2020.coling-main.262,Solving Math Word Problems with Multi-Encoders and Multi-Decoders,Yibin Shen; Cheqing Jin,2020,COLING 2020,main,Main,,,0,22.522,0.000,,https://aclanthology.org/2020.coling-main.262/,https://aclanthology.org/2020.coling-main.262.pdf,offline_coling,,"Math word problems solving remains a challenging task where potential semantic and mathematical logic need to be mined from natural language. Although previous researches employ the Seq2Seq technique to transform text descriptions into equation expressions, most of them achieve inferior performance "
28,ABIcBDLBVG,Fill in the Blank: Exploring and Enhancing LLM Capabilities for Backward Reasoning in Math Word Problems,Aniruddha Deb; Neeva Hareshbhai Oza; Sarthak Singla; Dinesh Khandelwal; Dinesh Garg,2024,ICLR 2024,main,Reject,generative models,large language models;prompting;mathematical reasoning;natural language processing,0,22.508,0.000,,https://openreview.net/forum?id=ABIcBDLBVG,,offline_iclr,,"While forward reasoning (i.e., find the answer given the question) has been explored extensively in the recent literature, backward reasoning is relatively unexplored. We examine the backward reasoning capabilities of LLMs on Math Word Problems (MWPs): given a mathematical question and its answer, w"
29,2025.findings-acl.656,Disentangling Text and Math in Word Problems: Evidence for the Bidimensional Structure of Large Language Models’ Reasoning,Pedro Calais; Gabriel Franco; Zilu Tang; Themistoklis Nikas; Wagner Meira Jr.,2025,ACL 2025,main,finding,,,0,22.495,0.000,,https://aclanthology.org/2025.findings-acl.656/,https://aclanthology.org/2025.findings-acl.656.pdf,offline_acl,,"Do LLMs process text and mathematics as a unified skill, or do these components rely on distinct underlying mechanisms? We investigate this question by disentangling the textual interpretation and mathematical solving steps in word problems drawn from Brazil’s largest college entrance exam (ENEM) an"
30,2022.findings-emnlp.316,Textual Enhanced Contrastive Learning for Solving Math Word Problems,Yibin Shen; Qianying Liu; Zhuoyuan Mao; Fei Cheng; Sadao Kurohashi,2022,EMNLP 2022,main,finding,,,0,22.202,0.000,,https://aclanthology.org/2022.findings-emnlp.316/,https://aclanthology.org/2022.findings-emnlp.316.pdf,offline_emnlp,,Solving math word problems is the task that analyses the relation of quantities e and requires an accurate understanding of contextual natural language information. Recent studies show that current models rely on shallow heuristics to predict solutions and could be easily misled by small textual per
31,C9ju8QQSCv,Can LLMs Solve Longer Math Word Problems Better?,Xin Xu; Tong Xiao; Zitong Chao; Zhenya Huang; Can Yang,2025,ICLR 2025,main,Poster,"foundation or frontier models, including LLMs",Large Language Models;Math Reasoning;Long Math Word Problems,0,22.164,0.000,,https://iclr.cc/virtual/2025/poster/30529,https://openreview.net/pdf?id=C9ju8QQSCv,offline_iclr,,"Math Word Problems (MWPs) play a vital role in assessing the capabilities of Large Language Models (LLMs), yet current research primarily focuses on questions with concise contexts. The impact of longer contexts on mathematical reasoning remains under-explored. This study pioneers the investigation "
32,EqcLAU6gyU,Agent-Oriented Planning in Multi-Agent Systems,Ao Li; Yuexiang Xie; Songze Li; Fugee Tsung; Bolin Ding,2025,ICLR 2025,main,Poster,"other topics in machine learning (i.e., none of the above)",Multi-Agent System; Planning,0,22.078,0.000,,https://iclr.cc/virtual/2025/poster/30386,https://openreview.net/pdf?id=EqcLAU6gyU,offline_iclr,,"Through the collaboration of multiple LLM-empowered agents possessing diverse expertise and tools, multi-agent systems achieve impressive progress in solving real-world problems. Given the user queries, the meta-agents, serving as the brain within multi-agent systems, are required to decompose the q"
33,2024.findings-naacl.72,What Makes Math Word Problems Challenging for LLMs?,Kv Aditya Srivatsa; Ekaterina Kochmar,2024,NAACL 2024,main,Findings,,,0,22.022,0.000,,https://aclanthology.org/2024.findings-naacl.72/,https://aclanthology.org/2024.findings-naacl.72.pdf,offline_naacl,,"This paper investigates the question of what makes math word problems (MWPs) in English challenging for large language models (LLMs). We conduct an in-depth analysis of the key linguistic and mathematical characteristics of MWPs. In addition, we train feature-based classifiers to better understand t"
34,j9wBgcxa7N,"MAgICoRe: Multi-Agent, Iterative, Coarse-to-Fine Refinement for Reasoning",Justin Chen; Archiki Prasad; Swarnadeep Saha; Elias Stengel-Eskin; Mohit Bansal,2025,ICLR 2025,main,Reject,"foundation or frontier models, including LLMs",LLM Refinement;Reasoning;Multi-Agent,0,21.913,0.000,,https://openreview.net/forum?id=j9wBgcxa7N,,offline_iclr,,"Large Language Models' (LLM) reasoning can be improved using test-time aggregation strategies, i.e., generating multiple samples for each problem and aggregating over them to find a better answer. While these improve performance, they often reach a saturation point beyond which additional samples pr"
35,YH1wtz2pbo,LONG-HORIZON REASONING AGENT FOR OLYMPIAD- LEVEL MATHEMATICAL PROBLEM SOLVING,,2026,ICLR 2026,main,Active,"foundation or frontier models, including LLMs",Large Language Model;Reinforcement Learning;Mathmatical Reasoning,0,21.599,0.000,,https://openreview.net/forum?id=YH1wtz2pbo,,offline_iclr,,"Large Reasoning Models (LRMs) have expanded the mathematical reasoning frontier through Chain-of-Thought (CoT) techniques and Reinforcement Learning with Verifiable Rewards (RLVR), capable of solving AIME-level problems.
However, the performance of LRMs is heavily dependent on the extended reasoning"
36,article-34679,GNS: Solving Plane Geometry Problems by Neural-Symbolic Reasoning with Multi-Modal LLMs,Maizhen Ning; Zihao Zhou; Qiufeng Wang; Xiaowei Huang; Kaizhu Huang,2025,AAAI 2025,main,Technical,natural language processing ii,,0,21.599,0.000,,https://ojs.aaai.org/index.php/AAAI/article/view/34679,https://ojs.aaai.org/index.php/AAAI/article/view/34679/36834,offline_aaai,,"With the outstanding capabilities of Large Language Models (LLMs), 
solving math word problems (MWP) has greatly progressed, achieving higher performance on several benchmark datasets. 
However, it is more challenging to solve plane geometry problems (PGPs) due to the necessity of understanding, rea"
37,2022.findings-acl.195,"Seeking Patterns, Not just Memorizing Procedures: Contrastive Learning for Solving Math Word Problems",Zhongli Li; Wenxuan Zhang; Chao Yan; Qingyu Zhou; Chao Li,2022,ACL 2022,main,Findings,,,0,21.593,0.000,,https://aclanthology.org/2022.findings-acl.195/,https://aclanthology.org/2022.findings-acl.195.pdf,offline_acl,,"Math Word Problem (MWP) solving needs to discover the quantitative relationships over natural language narratives. Recent work shows that existing models memorize procedures from context and rely on shallow heuristics to solve MWPs. In this paper, we look at this issue and argue that the cause is a "
38,gsSIH0mZ0Y,AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMs,,2026,ICLR 2026,main,Active,datasets and benchmarks,multi-agent systems;llms;collaborative reasoning,0,21.568,0.000,,https://openreview.net/forum?id=gsSIH0mZ0Y,,offline_iclr,,"Large-language models (LLMs) have demonstrated powerful problem-solving capabilities, in particular when organized in multi-agent systems. However, the advent of such systems also raises several questions on the ability of a complex network of agents to effectively self-organize and collaborate. Whi"
39,Ge7ZqrKG9t,Modeling Complex Mathematical Reasoning via Large Language Model based MathAgent,Haoran Liao; Qinyi Du; Shaohua Hu; Hao HE; Yanyan Xu,2024,ICLR 2024,main,Withdraw,"general machine learning (i.e., none of the above)",large language model; agent; mathematical reasoning; zero-shot prompting,0,21.555,0.000,,https://openreview.net/forum?id=Ge7ZqrKG9t,,offline_iclr,,"Large language models (LLMs) face challenges in solving complex mathematical problems that require comprehensive capacities to parse the statements, associate domain knowledge, perform compound logical reasoning, and integrate the intermediate rationales. Tackling all these problems once could be ar"
40,2021.acl-long.455,Math Word Problem Solving with Explicit Numerical Values,Qinzhuo Wu; Qi Zhang; Zhongyu Wei; Xuanjing Huang,2021,ACL 2021,main,Long,,,0,21.555,0.000,,https://aclanthology.org/2021.acl-long.455/,https://aclanthology.org/2021.acl-long.455.pdf,offline_acl,,"In recent years, math word problem solving has received considerable attention and achieved promising results, but previous methods rarely take numerical values into consideration. Most methods treat the numerical values in the problems as number symbols, and ignore the prominent role of the numeric"
41,c8McWs4Av0,Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification,Aojun Zhou; Ke Wang; Zimu Lu; Weikang Shi; Sichun Luo,2024,ICLR 2024,main,Poster,generative models,mathematical reasoning;large language models;zero-shot learning;code generation;prompting,0,21.490,0.000,,https://iclr.cc/virtual/2024/poster/18306,https://openreview.net/pdf?id=c8McWs4Av0,offline_iclr,,"Recent progress in large language models (LLMs) like GPT-4 and PaLM-2 has brought significant advancements in addressing math reasoning problems. In particular, OpenAI's latest version of GPT-4, known as GPT-4 Code Interpreter, shows remarkable performance on challenging math datasets. In this paper"
42,2024.findings-acl.848,NUMCoT: Numerals and Units of Measurement in Chain-of-Thought Reasoning using Large Language Models,Ancheng Xu; Minghuan Tan; Lei Wang; Min Yang; Ruifeng Xu,2024,ACL 2024,main,Findings,,,0,21.348,0.000,,https://aclanthology.org/2024.findings-acl.848/,https://aclanthology.org/2024.findings-acl.848.pdf,offline_acl,,"Numeral systems and units of measurement are two conjoined topics in activities of human beings and have mutual effects with the languages expressing them. Currently, the evaluation of Large Language Models (LLMs) often involves mathematical reasoning, yet little attention is given to how minor chan"
43,2022.emnlp-main.643,Analogical Math Word Problems Solving with Enhanced Problem-Solution Association,Zhenwen Liang; Jipeng Zhang; Xiangliang Zhang,2022,EMNLP 2022,main,Main,,,0,21.248,0.000,,https://aclanthology.org/2022.emnlp-main.643/,https://aclanthology.org/2022.emnlp-main.643.pdf,offline_emnlp,,"Math word problem (MWP) solving is an important task in question answering which requires human-like reasoning ability. Analogical reasoning has long been used in mathematical education, as it enables students to apply common relational structures of mathematical situations to solve new problems. In"
44,2022.coling-1.338,Noun-MWP: Math Word Problems Meet Noun Answers,Taehun Cha; Jaeheun Jung; Donghun Lee,2022,COLING 2022,main,Main,,,0,21.226,0.000,,https://aclanthology.org/2022.coling-1.338/,https://aclanthology.org/2022.coling-1.338.pdf,offline_coling,,"We introduce a new type of problems for math word problem (MWP) solvers, named Noun-MWPs, whose answer is a non-numerical string containing a noun from the problem text. We present a novel method to empower existing MWP solvers to handle Noun-MWPs, and apply the method on Expression-Pointer Transfor"
45,IC5WZt23gv,Ladders of Thought: A Self-Evolving Curriculum of Progressively Simplified Reasoning Traces,,2026,ICLR 2026,main,Active,"foundation or frontier models, including LLMs",curriculum learning;reasoning;distillation;language models;progressive rewrites;difficulty estimation,0,21.198,0.000,,https://openreview.net/forum?id=IC5WZt23gv,,offline_iclr,,"Large language models (LLMs) excel at reasoning when scaled to hundreds of billions of parameters, but small- and mid-scale models remain brittle reasoners even with knowledge distillation (KD). We present Ladders-of-Thought (LoT), a framework that improves reasoning by combining progressive questio"
46,2023.acl-industry.4,MathPrompter: Mathematical Reasoning using Large Language Models,Shima Imani; Liang Du; Harsh Shrivastava,2023,ACL 2023,main,Industry,,,0,21.157,0.000,,https://aclanthology.org/2023.acl-industry.4/,https://aclanthology.org/2023.acl-industry.4.pdf,offline_acl,,"Large Language Models (LLMs) have limited performance when solving arithmetic reasoning tasks and often provide incorrect answers. Unlike natural language understanding, math problems typically have a single correct answer, making the task of generating accurate solutions more challenging for LLMs. "
47,fovPyqPcKY,UGMathBench: A Diverse and Dynamic Benchmark for Undergraduate-Level Mathematical Reasoning with Large Language Models,Xin Xu; Jiaxin ZHANG; Tianhao Chen; Zitong Chao; Jishan Hu,2025,ICLR 2025,main,Poster,datasets and benchmarks,Math Reasoning;Undergraduate-Level Math problems;Benchmark,0,21.124,0.000,,https://iclr.cc/virtual/2025/poster/28857,https://openreview.net/pdf?id=fovPyqPcKY,offline_iclr,,"Large Language Models (LLMs) have made significant strides in mathematical reasoning, underscoring the need for a comprehensive and fair evaluation of their capabilities. However, existing benchmarks often fall short, either lacking extensive coverage of undergraduate-level mathematical problems or "
48,FfsxgSZW0c,Large Language Models Miss the Multi-agent Mark,Emanuele La Malfa; Gabriele La Malfa; Samuele Marro; Jie M. Zhang; Elizabeth Black,2025,NIPS 2025,Position,Poster,,multi-agent large language models;language models;multi-agent systems,0,21.036,0.000,,https://openreview.net/forum?id=FfsxgSZW0c,,offline_nips,,"Recent interest in Multi-Agent Systems of Large Language Models (MAS LLMs) has led to an increase in frameworks leveraging multiple LLMs to tackle complex tasks. 
However, much of this literature appropriates the terminology of MAS without engaging with its foundational principles. 
In this position"
49,OB10WTlwmX,Evaluating and Improving Tool-Augmented Computation-Intensive Math Reasoning,Beichen Zhang; Kun Zhou; Xilin Wei; Xin Zhao; Jing Sha,2023,NIPS 2023,Datasets & Benchmarks,Poster,,Math Problem Solving,0,21.022,0.000,,https://nips.cc/virtual/2023/poster/73611,https://openreview.net/pdf?id=OB10WTlwmX,offline_nips,,"Chain-of-thought prompting (CoT) and tool augmentation have been validated in recent work as effective practices for improving large language models (LLMs) to perform step-by-step reasoning on complex math-related tasks.
However, most existing math reasoning datasets may not be able to fully evaluat"
50,2023.acl-long.245,Solving Math Word Problems via Cooperative Reasoning induced Language Models,Xinyu Zhu; Junjie Wang; Lin Zhang; Yuxiang Zhang; Yongfeng Huang,2023,ACL 2023,main,Long,,,0,20.950,0.000,,https://aclanthology.org/2023.acl-long.245/,https://aclanthology.org/2023.acl-long.245.pdf,offline_acl,,"Large-scale pre-trained language models (PLMs) bring new opportunities to challenging problems, especially those that need high-level intelligence, such as the math word problem (MWPs). However, directly applying existing PLMs to MWPs can fail as the generation process lacks sufficient supervision a"
51,xcBV0fK0ZK,Adversarial Robustness of LLM-Based Multi-Agent Systems for Engineering Problems,Lorenz Wiesmeier; Matthias Busch; Marius Tacke; Kevin Linka; Christian J Cyron,2026,ICLR 2026,main,Withdraw,"alignment, fairness, safety, privacy, and societal considerations",LLM;MAS;Adversarial robustness;Engineering;GPT-4o mini;Misalignment,0,20.757,0.000,,https://openreview.net/forum?id=xcBV0fK0ZK,,offline_iclr,,"Large language models (LLMs) are increasingly deployed in multi-agent systems (MAS), often in new domains, including for solving engineering problems. Unlike purely linguistic tasks, engineering workflows demand formal rigor and numerical accuracy, meaning that adversarial perturbations can cause no"
52,sOTbFCUrDj,A Generation-based Deductive Method for Math Word Problems,Yuxuan Hu; Jing Zhang; Haoyang Li; Cuiping Li; Hong Chen,2023,EMNLP 2023,main,Long Main,,math word problem;natural language processing,0,20.248,0.000,,https://openreview.net/forum?id=sOTbFCUrDj,,offline_emnlp,,"Math word problems (MWP) involving advanced operators such as linear equation solver cannot be easily tackled by earlier MWP methods, because the existing generation methods suffer from repeated sub-expression generation and deductive methods are restricted to dealing with binary operations. This pa"
53,UDeDdxZOwl,MathViz-Bench: Evaluating Text-to-Image Models on Visually Solving Math Problems,,2026,ICLR 2026,main,Active,datasets and benchmarks,Text-to-image models;foundation model;benchmark,0,20.195,0.000,,https://openreview.net/forum?id=UDeDdxZOwl,,offline_iclr,,"We present MathViz-Bench, a comprehensive benchmark for evaluating Text-to-Image (T2I) models' capability to visualize step-by-step solutions for high school mathematics problems.
MathViz-Bench comprises 500 carefully curated problems sampled from levels 1-3 of the MATH dataset, spanning seven mathe"
54,UEr2bzsDXn,Beyond Solving Math Quiz: Evaluating the Ability of Large Reasoning Models to Ask for Information,,2026,ICLR 2026,main,Active,datasets and benchmarks,Large Reasoning Models; Evaluation; Asking for Information; Mathematical Reasoning; Benchmarks,0,20.152,0.000,,https://openreview.net/forum?id=UEr2bzsDXn,,offline_iclr,,"The recent development of Large Reasoning Models (LRMs) has demonstrated remarkable problem-solving abilities in mathematics, as evaluated by existing benchmarks exclusively on well-defined problems. However, such evaluation setup constitutes a critical gap, since a genuine intelligent agent should "
55,2023.findings-acl.635,Compositional Mathematical Encoding for Math Word Problems,Zhenwen Liang; Jipeng Zhang; Kehan Guo; Xiaodong Wu; Jie Shao,2023,ACL 2023,main,Findings,,,0,20.129,0.000,,https://aclanthology.org/2023.findings-acl.635/,https://aclanthology.org/2023.findings-acl.635.pdf,offline_acl,,"Solving math word problem (MWP) remains a challenging task, as it requires to understand both the semantic meanings of the text and the mathematical logic among quantities, i.e., for both semantics modal and quantity modal learning. Current MWP encoders work in a uni-modal setting and map the given "
56,96apU6YzSO,R-Zero: Self-Evolving Reasoning LLM from Zero Data,,2026,ICLR 2026,main,Active,"foundation or frontier models, including LLMs",large language model;reinforcement learning;self-evolving;reasoning,0,19.985,0.000,,https://openreview.net/forum?id=96apU6YzSO,,offline_iclr,,"Self-evolving Large Language Models (LLMs) offer a scalable path toward super-intelligence by autonomously generating, refining, and learning from their own experiences. However, existing methods for training such models still rely heavily on vast human-curated tasks and labels, typically via fine-t"
57,uO3gGxzu8k,Toward Self-Evolving Systems of LLM Agents through Exploration and Iterative Feedback,,2026,ICLR 2026,main,Active,"applications to robotics, autonomy, planning",Agent;Language Model;Exploration;Data Generation;Self-Evolving;Iterative Feedback;Imitation Learning;Demonstrations,0,19.945,0.000,,https://openreview.net/forum?id=uO3gGxzu8k,,offline_iclr,,"Training large language model (LLM) agents to acquire necessary skills and perform diverse tasks within an environment is gaining interest as a means to enable open-endedness.
However, creating the training dataset for their skill acquisition faces several challenges. 
Manual trajectory collection r"
58,,Randomization Times under Quantum Chaotic Hamiltonian Evolution,Souradeep Ghosh; Nicholas Hunter-Jones; Joaquin F. Rodriguez-Nieva,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25074v1,https://arxiv.org/pdf/2512.25074v1,arxiv,,"Randomness generation through quantum-chaotic evolution underpins foundational questions in statistical mechanics and applications across quantum information science, including benchmarking, tomography, metrology, and demonstrations of quantum computational advantage. While statistical mechanics suc"
59,,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"
60,,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"
61,,Classification of Interacting Topological Crystalline Superconductors in Three Dimensions and Beyond,Shang-Qiang Ning; Xing-Yu Ren; Qing-Rui Wang; Yang Qi; Zheng-Cheng Gu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25069v1,https://arxiv.org/pdf/2512.25069v1,arxiv,,"Although classification for free-fermion topological superconductors (TSC) is established, systematically understanding the classification of 3D interacting TSCs remains difficult, especially those protected by crystalline symmetries like the 230 space groups. We build up a general framework for sys"
62,,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"
63,,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"
64,,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"
65,,Many Minds from One Model: Bayesian Transformers for Population Intelligence,Diji Yang; Yi Zhang,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25063v1,https://arxiv.org/pdf/2512.25063v1,arxiv,,"Despite their scale and success, modern transformers are almost universally trained as single-minded systems: optimization produces one deterministic set of parameters, representing a single functional hypothesis about the data. Motivated by the idea that intelligence emerge from many minds, we prop"
66,,Local approximations of global Hamiltonian from inclusion of algebras,Yidong Chen; Nima Lashkari; Kwing Lam Leung,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25062v1,https://arxiv.org/pdf/2512.25062v1,arxiv,,"We write down the global Hamiltonian of conformal field theory (CFT) in finite volume in terms of the modular Hamiltonian of the vacuum reduced to a local ball-shaped region, and use it to propose local approximations to the global Minkowski Hamiltonian in quantum field theory (QFT). The proposed Ha"
67,,Melting curve of correlated iron at Earth's core conditions from machine-learned DFT+DMFT,Rishi Rao; Li Zhu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25061v1,https://arxiv.org/pdf/2512.25061v1,arxiv,,"Reliable constraints on iron's melting curve at Earth's inner-core boundary require accurate finite-temperature electronic correlations, yet DFT+DMFT calculations remain too costly for large-scale thermodynamic sampling. Here, we develop a machine-learning accelerator for charge self-consistent DFT+"
68,,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"
69,,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"
70,,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"
71,,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"
72,,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 
73,,Emergence of 3D Superconformal Ising Criticality on the Fuzzy Sphere,Yin Tang; Cristian Voinea; Liangdong Hu; Zlatko Papić; W. Zhu,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25054v1,https://arxiv.org/pdf/2512.25054v1,arxiv,,"Supersymmetric conformal field theories (SCFTs) form a unique subset of quantum field theories which provide powerful insights into strongly coupled critical phenomena. Here, we present a microscopic and non-perturbative realization of the three-dimensional $\mathcal{N}=1$ superconformal Ising criti"
74,,Fluid dynamics as intersection problem,Nikita Nekrasov; Paul Wiegmann,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25053v1,https://arxiv.org/pdf/2512.25053v1,arxiv,,"We formulate the covariant hydrodynamics equations describing the fluid dynamics as the problem of intersection theory on the infinite dimensional symplectic manifold associated with spacetime. This point of view separates the structures related to the equation of state, the geometry of spacetime, a"
75,,AdaGReS:Adaptive Greedy Context Selection via Redundancy-Aware Scoring for Token-Budgeted RAG,Chao Peng; Bin Wang; Zhilei Long; Jinfang Sheng,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25052v1,https://arxiv.org/pdf/2512.25052v1,arxiv,,"Retrieval-augmented generation (RAG) is highly sensitive to the quality of selected context, yet standard top-k retrieval often returns redundant or near-duplicate chunks that waste token budget and degrade downstream generation. We present AdaGReS, a redundancy-aware context selection framework for"
76,,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"
77,,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 "
78,,Amplitude constraints on dark energy,Scott Melville,2025,arXiv,,,,,0,0.000,0.000,10.58027/3q8k-ew90,http://arxiv.org/abs/2512.25047v1,https://arxiv.org/pdf/2512.25047v1,arxiv,,"This talk gives a short introduction to the ``UV/EFT correspondence"", which uses scattering amplitudes to relate the Effective Field Theory (EFT) coefficients probed by low-energy measurements to properties of the underlying high-energy (UV) completion. This includes recent ``positivity bounds"" on E"
79,,Extreme nonlinear optics in optical fibers,Mario Ferraro; Bertrand Kibler; Pierre Béjot; Frédéric Gérome; Benoit Debord,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25046v1,https://arxiv.org/pdf/2512.25046v1,arxiv,,"This paper reviews the field of extreme nonlinear optics in optical fibers, highlighting key phenomena and advancements. It discusses multiple ionization effects caused by femtosecond laser pulses that generate plasma and induce permanent material modifications, as well as plasma luminescence and it"
80,,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"
81,,Thin Tree Verification is coNP-Complete,Alice Moayyedi,2025,arXiv,,,,,0,0.000,0.000,,http://arxiv.org/abs/2512.25043v1,https://arxiv.org/pdf/2512.25043v1,arxiv,,"An $α$-thin tree $T$ of a graph $G$ is a spanning tree such that every cut of $G$ has at most an $α$ proportion of its edges in $T$. The Thin Tree Conjecture proposes that there exists a function $f$ such that for any $α> 0$, every $f(α)$-edge-connected graph has an $α$-thin tree. Aside from its ind"
82,,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"
83,,"Impact of Artificial Intelligence on Education and Research: Pedagogy, Learning Analytics, and Academic Transformation",Sasmita Padhy,2025,,,,,,0,0.000,0.000,10.70593/978-93-7185-525-9,https://openalex.org/W4414201528,https://www.deepscienceresearch.com/dsr/catalog/download/304/1459/2845,openalex,,"This book discusses the impact of artificial intelligence on academic practice and research. This book demonstrates how AI and its applications in teaching, learning, and discovery impact opportunities for educational and scientific innovation. The description raises the good, and the bad, moral con"
84,,AI Reasoning for Wireless Communications and Networking: A Survey and Perspectives,Haoxiang Luo; Ruichen Zhang; Jiacheng Wang; Gang Sun; Dusit Niyato,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2509.09193,https://openalex.org/W4417071390,https://arxiv.org/pdf/2509.09193,openalex,,"Artificial Intelligence (AI) techniques play a pivotal role in optimizing wireless communication networks. However, traditional deep learning approaches often act as closed boxes, lacking the structured reasoning abilities needed to tackle complex, multi-step decision problems. This survey provides "
85,,Building Trustworthy Autonomous AI: Essential Principles beyond Traditional Software Design,Renner Christian; Durgesh Babu P; Hrishitva Patel; Keyur Modi,2025,Applied Cybersecurity & Internet Governance,,,,,0,0.000,0.000,10.60097/acig/208710,https://openalex.org/W4413992056,https://www.acigjournal.com/pdf-208710-128314?filename=Building Trustworthy.pdf,openalex,,"Imagine smart Artificial Intelligence (AI) agents that can act on their own, like digital teammates, needing our complete trust, especially in protecting our digital world. Just as early software was chaotic until ideas like ‘object-oriented programming’ (OOP) brought order, today’s powerful AI agen"
86,,AI Agents for Economic Research,Anton Korinek,2025,,,,,,2,0.000,0.000,10.3386/w34202,https://openalex.org/W4414156829,https://doi.org/10.3386/w34202,openalex,,
87,,Big Loop and Atomization: A Holistic Review on the Expansion Capabilities of Large Language Models,Zefa Hu; Yi Huang; Junlan Feng; Chao Deng,2025,Applied Sciences,,,,,0,0.000,0.000,10.3390/app15179466,https://openalex.org/W4413832627,https://www.mdpi.com/2076-3417/15/17/9466/pdf?version=1756391648,openalex,,"Large language models (LLMs) have demonstrated impressive capabilities, yet they face significant limitations in real-world applications. To overcome these boundaries, research areas such as tool learning, model collaboration, agents, and multi-agent systems have increasingly drawn attention. Howeve"
88,,AI and Financial Fragility: A Framework for Measuring Systemic Risk in Deployment of Generative AI for Stock Price Predictions,Miranda McClellan,2025,Journal of risk and financial management,,,,,1,0.000,0.000,10.3390/jrfm18090475,https://openalex.org/W4413696618,https://www.mdpi.com/1911-8074/18/9/475/pdf?version=1756196971,openalex,,"In a few years, most investment firms will deploy Generative AI (GenAI) and large language models (LLMs) for reduced-cost stock trading decisions. If GenAI-run investment decisions from most firms are heavily coordinated, they could all give a “sell” signal simultaneously, triggering market crashes."
89,,Enhancing Teacher Professional Development Through a Human-AI Co-Intelligent System for Scalable Automated Feedback in Computing Education,Qiong Cheng; Surendra K. Dara,2025,,,,,,0,0.000,0.000,10.36227/techrxiv.175606386.61533946/v1,https://openalex.org/W4413478117,https://www.techrxiv.org/doi/pdf/10.36227/techrxiv.175606386.61533946,openalex,,
90,,Prompt Orchestration Markup Language,Yuge Zhang; Nan Chen; Jiahang Xu; Yuqing Yang,2025,arXiv (Cornell University),,,,,0,0.000,0.000,,https://openalex.org/W4415014182,https://arxiv.org/pdf/2508.13948,openalex,,"Large Language Models (LLMs) require sophisticated prompting, yet current practices face challenges in structure, data integration, format sensitivity, and tooling. Existing methods lack comprehensive solutions for organizing complex prompts involving diverse data types (documents, tables, images) o"
91,,Artificial ignorance: Understanding the role of AI in modern agnotology,Amit Ray; Michael Nolan,2025,First Monday,,,,,0,0.000,0.000,10.5210/fm.v30i8.13890,https://openalex.org/W4413271901,https://firstmonday.org/ojs/index.php/fm/article/download/13890/12068,openalex,,"This paper explores the concept of agnotology, the deliberate production of ignorance, within the context of modern scientific endeavors, particularly in the corporate and technological sectors. It examines how industries use various tactics to manipulate public understanding of scientific issues, o"
92,,Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Frontiers,,2025,,,,,,0,0.000,0.000,10.1145/3736539,https://openalex.org/W4413330000,https://dl.acm.org/doi/pdf/10.1145/3736539,openalex,,
93,,Optimal-Agent-Selection: State-Aware Routing Framework for Efficient Multi-Agent Collaboration,Jingbo Wang; Sendong Zhao; Hao Wang; Yuzhen Fan; Lizhe Zhang,2025,arXiv.org,,,,,0,0.000,0.000,10.48550/arXiv.2511.02200,https://www.semanticscholar.org/paper/1f24e18ba7106b464a7c9081300d4c2f3b661c11,,semantic_scholar,,"The emergence of multi-agent systems powered by large language models (LLMs) has unlocked new frontiers in complex task-solving, enabling diverse agents to integrate unique expertise, collaborate flexibly, and address challenges unattainable for individual models. However, the full potential of such"
94,,MAS-ZERO: Designing Multi-Agent Systems with Zero Supervision,Zixuan Ke; Austin Xu; Yifei Ming; Xuan-Phi Nguyen; Caiming Xiong,2025,arXiv.org,,,,,8,0.000,0.000,10.48550/arXiv.2505.14996,https://www.semanticscholar.org/paper/097e2138daa8d6af8ef1af85ac9cc6e031540aa1,,semantic_scholar,,"Multi-agent systems (MAS) leveraging the impressive capabilities of Large Language Models (LLMs) hold significant potential for tackling complex tasks. However, most current MAS depend on manually designed agent roles and communication protocols. These manual designs often fail to align with the und"
95,,Self-evolving expertise in complex non-verifiable subject domains: dialogue as implicit meta-RL,Richard M. Bailey,2025,arXiv.org,,,,,0,0.000,0.000,10.48550/arXiv.2510.15772,https://www.semanticscholar.org/paper/931ebc2b83e8ef4775001f2a8bd81b6395f77d96,,semantic_scholar,,"So-called `wicked problems', those involving complex multi-dimensional settings, non-verifiable outcomes, heterogeneous impacts and a lack of single objectively correct answers, have plagued humans throughout history. Modern examples include decisions over justice frameworks, solving environmental p"
96,,Agent0: Unleashing Self-Evolving Agents from Zero Data via Tool-Integrated Reasoning,Peng Xia; Peng Xia; Kaide Zeng; Jiaqi Liu; Can Qin,2025,,,,,,2,0.000,0.000,,https://www.semanticscholar.org/paper/a647788b47b1bac9c137ab192316f72de52471d4,,semantic_scholar,,"Large Language Model (LLM) Agents, often trained with Reinforcement Learning (RL), are constrained by a dependency on human-curated data, limiting scalability and tethering AI to human knowledge. Existing self-evolution frameworks offer an alternative but are typically restricted by the model's inhe"
97,,AdvEvo-MARL: Shaping Internalized Safety through Adversarial Co-Evolution in Multi-Agent Reinforcement Learning,Zhenyu Pan; Yiting Zhang; Zhuo Liu; Yolo Yunlong Tang; Zeliang Zhang,2025,arXiv.org,,,,,0,0.000,0.000,10.48550/arXiv.2510.01586,https://www.semanticscholar.org/paper/7a44b3e5b5151aa1c9fd533e2374578a1ddbc4df,,semantic_scholar,,"LLM-based multi-agent systems excel at planning, tool use, and role coordination, but their openness and interaction complexity also expose them to jailbreak, prompt-injection, and adversarial collaboration. Existing defenses fall into two lines: (i) self-verification that asks each agent to pre-fil"
98,,Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team,Md. Tanzib Hosain; Salman Rahman; Md. Kishor Morol; Md. Rizwan Parvez,2025,arXiv.org,,,,,6,0.000,0.000,10.48550/arXiv.2506.14234,https://www.semanticscholar.org/paper/3bd17cf2227ca49163bf74519cc64b5e2d359227,,semantic_scholar,,"Despite impressive progress on complex reasoning, current large language models (LLMs) typically operate in isolation - treating each problem as an independent attempt, without accumulating or integrating experiential knowledge. In contrast, expert problem solvers - such as Olympiad or programming c"
99,,HERMES: Towards Efficient and Verifiable Mathematical Reasoning in LLMs,Azim Ospanov; Zijin Feng; Jiacheng Sun; Haoli Bai; Xin Shen,2025,,,,,,0,0.000,0.000,,https://www.semanticscholar.org/paper/8a3d33aac79b05d94d3634851658155c8f9a6f37,,semantic_scholar,,"Informal mathematics has been central to modern large language model (LLM) reasoning, offering flexibility and enabling efficient construction of arguments. However, purely informal reasoning is prone to logical gaps and subtle errors that are difficult to detect and correct. In contrast, formal the"
100,,LiveTradeBench: Seeking Real-World Alpha with Large Language Models,Haofei Yu; Fenghai Li; Jiaxuan You,2025,,,,,,1,0.000,0.000,,https://www.semanticscholar.org/paper/9b8944c299cd7ce32db8bf187b96b508bede49d1,,semantic_scholar,,"Large language models (LLMs) achieve strong performance across benchmarks--from knowledge quizzes and math reasoning to web-agent tasks--but these tests occur in static settings, lacking real dynamics and uncertainty. Consequently, they evaluate isolated reasoning or problem-solving rather than deci"
101,,Multi Uav Cooperative Surveillance With Spatio Temporal,,2022,,,,,,0,0.000,0.000,,https://www.semanticscholar.org/paper/03244ebd14a553b3f47cf287f744ce73d6a99e1d,,semantic_scholar,,
102,,CollaPipe: Adaptive Segment-Optimized Pipeline Parallelism for Collaborative LLM Training in Heterogeneous Edge Networks,Jiewei Chen; Xiumei Deng; Zehui Xiong; Shaoyong Guo; Xuesong Qiu,2025,arXiv.org,,,,,0,0.000,0.000,10.48550/arXiv.2509.19855,https://www.semanticscholar.org/paper/dea03e01eb6704b2bf8648d5520a6d59161bc0b1,,semantic_scholar,,"The increasing demand for intelligent mobile applications has made multi-agent collaboration with Transformer-based large language models (LLMs) essential in mobile edge computing (MEC) networks. However, training LLMs in such environments remains challenging due to heavy computation, high end-to-en"
103,,Charting AI’s Trajectory: Historical Foundations and Future Directions,Imed Reese Sy,2025,Preprints.org,,,,,0,0.000,0.000,10.20944/preprints202509.1217.v1,https://openalex.org/W4414315743,https://www.preprints.org/frontend/manuscript/80177d030026724d3a816dadd53a77e5/download_pub,openalex,,"This review draws insights into the technical, historical, and socio-economic dimensions of AI’s rapid transformation. It traced AI’s progression from symbolic rule-based systems to data-driven statistical learning and deep neural networks, showing how advances in computational power, optimization m"
104,,"Chasing Progress, Not Perfection: Revisiting Strategies for End-to-End LLM Plan Generation",Sukai Huang; Trevor Cohn; Nir Lipovetzky,2025,Proceedings of the International Conference on Automated Planning and Scheduling,,,,,0,0.000,0.000,10.1609/icaps.v35i1.36119,https://openalex.org/W4414222903,https://ojs.aaai.org/index.php/ICAPS/article/download/36119/38273,openalex,,"The capability of Large Language Models (LLMs) to plan remains a topic of debate. Some critics argue that strategies to boost LLMs' reasoning skills are ineffective in planning tasks, while others report strong outcomes merely from training models on a planning corpus. This paper revisits these clai"
105,,"Lyotard’s ‘Brain’, and/or the Mathematical Universe",Sunil Manghani,2025,Technophany A Journal for Philosophy and Technology,,,,,0,0.000,0.000,10.54195/technophany.19598,https://openalex.org/W4414297538,https://technophany.philosophyandtechnology.network/article/download/19598/25857,openalex,,This article provides a reading of Jean-François Lyotard’s “A Postmodern Fable.” It explores the speculative narrative on the fate of human consciousness as the Sun dies in conjunction with a reading of contemporary artificial intelligence and the hypothesis of a mathematical universe. The analysis 
106,,Gödelian embodied self-referential genomic intelligence: lessons for AI and AGI from the genomic blockchain,Sheri M. Markose,2025,Frontiers in Robotics and AI,,,,,0,0.000,0.000,10.3389/frobt.2025.1624695,https://openalex.org/W4414184733,https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2025.1624695/pdf,openalex,,"The security of code-based digital records is a major concern of the 21st century. AI and artificial general intelligence (AGI) can be hacked to pieces by digital adversaries, and some AI objectives can lead to existential threats. The former arises from sitting duck problems that all software syste"
107,,Biotechnology in materials science: A storied past and a bold future,Shonit N. Sharma; Jacob Witten; Ranjit Kumar Das; R. Rox Anderson; Daniel G. Anderson,2025,MRS Bulletin,,,,,0,0.000,0.000,10.1557/s43577-025-00929-4,https://openalex.org/W4414086134,https://link.springer.com/content/pdf/10.1557/s43577-025-00929-4.pdf,openalex,,"Abstract The intersection of biotechnology and materials science has driven medical and scientific innovation for decades and is poised to make similar transformative impacts over the next 50 years. Advanced drug delivery systems, including nanoparticles and larger delivery material platforms, are e"
108,,Why unequal AI access enhances team productivity: the mediating role of interaction processes and cognitive diversity,Jin‐Hee Han; Ruqin Ren,2025,Frontiers in Psychology,,,,,0,0.000,0.000,10.3389/fpsyg.2025.1636906,https://openalex.org/W4414072495,https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1636906/pdf,openalex,,"Introduction Generative artificial intelligence (GenAI) is widely viewed as valuable for improving the performance of human-agent teams (HATs). However, in reality, not all members have equal access to AI tools, making uneven AI integration an important factor impacting team composition and, thus, t"
109,,Minds in Crisis: How the AI Revolution is Impacting Mental Health,Keith Robert Head,2025,JOURNAL OF MENTAL HEALTH AND CLINICAL PSYCHOLOGY,,,,,0,0.000,0.000,10.29245/2578-2959/2025/3.1352,https://openalex.org/W4414239927,https://doi.org/10.29245/2578-2959/2025/3.1352,openalex,,"The rapid rise of generative AI systems, particularly conversational chatbots such as ChatGPT and Character.AI, has sparked new concerns regarding their psychological impact on users. While these tools offer unprecedented access to information and companionship, a growing body of evidence suggests t"
110,,Agua: A Concept-Based Explainer for Learning-Enabled Systems,Sagar Patel; Dongsu Han; Nina Narodytska; Sangeetha Abdu Jyothi,2025,,,,,,0,0.000,0.000,10.1145/3718958.3754341,https://openalex.org/W4413756881,https://dl.acm.org/doi/pdf/10.1145/3718958.3754341,openalex,,
111,,Transduction is All You Need for Structured Data Workflows,Alfio Gliozzo; Naweed Khan; Constantinos Constantinides; Nandana Mihindukulasooriya; Nahuel Defosse,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2508.15610,https://openalex.org/W4415337318,https://arxiv.org/pdf/2508.15610,openalex,,"This paper introduces Agentics, a functional agentic AI framework for building LLM-based structured data workflow pipelines. Designed for both research and practical applications, Agentics offers a new data-centric paradigm in which agents are embedded within data types, enabling logical transductio"
112,,Probing for consciousness in machines,Mathis Immertreu; Achim Schilling; Andreas Maier; Patrick Krauß,2025,Frontiers in Artificial Intelligence,,,,,0,0.000,0.000,10.3389/frai.2025.1610225,https://openalex.org/W4413363322,https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1610225/pdf,openalex,,"This study explores the potential for artificial agents to develop core consciousness, as proposed by Antonio Damasio's theory of consciousness. According to Damasio, the emergence of core consciousness relies on the integration of a self model, informed by representations of emotions and feelings, "
113,,MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers,Zhenlin Luo; Zhiqi Shen; Wenzhuo Yang; Zirui Zhao; Prathyusha Jwalapuram,2025,arXiv (Cornell University),,,,,0,0.000,0.000,10.48550/arxiv.2508.14704,https://openalex.org/W4415240829,https://arxiv.org/pdf/2508.14704,openalex,,"The Model Context Protocol has emerged as a transformative standard for connecting large language models to external data sources and tools, rapidly gaining adoption across major AI providers and development platforms. However, existing benchmarks are overly simplistic and fail to capture real appli"
114,,Digital twins as self-models for intelligent structures,Xiaoxue Shen; David Wagg; Matthew Tipuric; Matthew S. Bonney,2025,Scientific Reports,,,,,1,0.000,0.000,10.1038/s41598-025-14347-8,https://openalex.org/W4413330590,https://www.nature.com/articles/s41598-025-14347-8.pdf,openalex,,
115,,Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation,Siyuan Wang; Zhuohan Long; Zhihao Fan; Zhongyu Wei; Xuanjing Huang,2024,International Conference on Computational Linguistics,,,,,64,0.000,0.000,10.48550/arXiv.2402.11443,https://www.semanticscholar.org/paper/b93ac10de176c4a7aaa2cc652b90bb25636532cd,,semantic_scholar,,"This paper presents a benchmark self-evolving framework to dynamically evaluate rapidly advancing Large Language Models (LLMs), aiming for a more accurate assessment of their capabilities and limitations. We utilize a multi-agent system to manipulate the context or question of original instances, re"
116,,Deciding the Path: Leveraging Multi-Agent Systems for Solving Complex Tasks,Iman Abbasnejad; Xuefeng Liu; Atanu Roy,2025,2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW),,,,,0,0.000,0.000,10.1109/CVPRW67362.2025.00405,https://www.semanticscholar.org/paper/0dcf56342e4d8b443638fa81f84904d90fc6cc02,,semantic_scholar,,"We present a multi-agent framework that enhances the capabilities of LLMs through intelligent task distribution and resource optimization for solving complex problems. The framework uses a dynamic routing mechanism that automatically delegates queries to specialized agents, complemented by an effici"
117,,CoTGuard: Using Chain-of-Thought Triggering for Copyright Protection in Multi-Agent LLM Systems,Yan Wen; Junfeng Guo; Heng Huang,2025,arXiv.org,,,,,1,0.000,0.000,10.48550/arXiv.2505.19405,https://www.semanticscholar.org/paper/bd7107b697981c50cf683b6b5df762f8a82b81e6,,semantic_scholar,,"As large language models (LLMs) evolve into autonomous agents capable of collaborative reasoning and task execution, multi-agent LLM systems have emerged as a powerful paradigm for solving complex problems. However, these systems pose new challenges for copyright protection, particularly when sensit"
118,,SPIRAL: Self-Play on Zero-Sum Games Incentivizes Reasoning via Multi-Agent Multi-Turn Reinforcement Learning,Bo Liu (Benjamin Liu); Leon Guertler; Simon Yu; Zi-Yan Liu; Penghui Qi,2025,arXiv.org,,,,,21,0.000,0.000,10.48550/arXiv.2506.24119,https://www.semanticscholar.org/paper/6ac8d8bfc7cf6dd6ad6cbc764cedffe673aef346,,semantic_scholar,,"Recent advances in reinforcement learning have shown that language models can develop sophisticated reasoning through training on tasks with verifiable rewards, but these approaches depend on human-curated problem-answer pairs and domain-specific reward engineering. We introduce SPIRAL, a self-play "
119,,SEW: Self-Evolving Agentic Workflows for Automated Code Generation,Siwei Liu; Jinyuan Fang; Han Zhou; Yingxu Wang; Zaiqiao Meng,2025,arXiv.org,,,,,5,0.000,0.000,10.48550/arXiv.2505.18646,https://www.semanticscholar.org/paper/5019a83ed9737ee59616070a26ca7581b1bd566c,,semantic_scholar,,"Large Language Models (LLMs) have demonstrated effectiveness in code generation tasks. To enable LLMs to address more complex coding challenges, existing research has focused on crafting multi-agent systems with agentic workflows, where complex coding tasks are decomposed into sub-tasks, assigned to"
120,,ReSo: A Reward-driven Self-organizing LLM-based Multi-Agent System for Reasoning Tasks,Heng Zhou; Hejia Geng; Xiangyuan Xue; Zhenfei Yin; Lei Bai,2025,Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing,,,,,15,0.000,0.000,10.48550/arXiv.2503.02390,https://www.semanticscholar.org/paper/499979b1347c53cc3cadedf66575623cc0d6a727,,semantic_scholar,,"Multi-agent systems (MAS) have emerged as a promising approach for enhancing the reasoning capabilities of large language models in complex problem-solving; however, current MAS frameworks suffer from poor flexibility and scalability with underdeveloped optimization strategies. To address these chal"
121,,Enhancing Online Learning Through Multi-Agent Debates for CS University Students,Jing Du; Guangtao Xu; Wenhao Liu; Dibin Zhou; Fuchang Liu,2025,Applied Sciences,,,,,1,0.000,0.000,10.3390/app15115877,https://www.semanticscholar.org/paper/9dfedd53ef2e8e8d0fae09371013fd6612eed36a,,semantic_scholar,,"As recent advancements in large language models enhance reasoning across various domains, educators are increasingly exploring their use in conversation-based tutoring systems. However, since LLMs are black-box models to users and lack human-like problem-solving strategies, users are hardly convince"
122,,Knowledge Tagging with Large Language Model based Multi-Agent System,Hang Li; Tianlong Xu; Ethan Chang; Qingsong Wen,2024,AAAI Conference on Artificial Intelligence,,,,,2,0.000,0.000,10.48550/arXiv.2409.08406,https://www.semanticscholar.org/paper/ee0814fb2b065205cf08019314d0e21ce09ad977,,semantic_scholar,,"Knowledge tagging for questions is vital in modern intelligent educational applications, including learning progress diagnosis, practice question recommendations, and course content organization. Traditionally, these annotations have been performed by pedagogical experts, as the task demands not onl"
123,,E3-MAS: A Self-Evolution Multi-Agent System Framework,Ming-Yi Huang; Yao-Zhi Xue; Chai-Yu Lin,2025,2025 IEEE/IEIE International Conference on Consumer Electronics-Asia (ICCE-Asia),,,,,0,0.000,0.000,10.1109/ICCE-Asia67487.2025.11263582,https://www.semanticscholar.org/paper/44f5ae089324cad7a0d9a0d27380c521c78acd30,,semantic_scholar,,"Large Language Model (LLM)-based Multi-Agent Systems (MAS) have emerged as a powerful paradigm for solving complex, real-world tasks with tedious workflows and frequent errors. However, current self-evolution MAS approaches require extensive manual tuning and lack dynamic adaptation, precise issue i"
124,,Polymath: A Self-Optimizing Agent with Dynamic Hierarchical Workflow,Chia-Tung Ho; Jing Gong; Xufeng Yao; Yunsheng Bai; Abhishek B. Akkur,2025,arXiv.org,,,,,1,0.000,0.000,10.48550/arXiv.2508.02959,https://www.semanticscholar.org/paper/9718bb2429dee2cacf5f9316a174e26b815694a2,,semantic_scholar,,"Large language models (LLMs) excel at solving complex tasks by executing agentic workflows composed of detailed instructions and structured operations. Yet, building general-purpose agents by manually embedding foundation models into agentic systems such as Chain-of-Thought, Self-Reflection, and ReA"
125,,ApexCodium: a Multi-Agent System for Code Generation with Enhanced Self-Reflection,Mihir S Arya; Aditya Ranjan; Ananmay A Lohia,2025,2025 International Conference on Artificial intelligence and Emerging Technologies (ICAIET),,,,,0,0.000,0.000,10.1109/ICAIET65052.2025.11211133,https://www.semanticscholar.org/paper/5872513f36f2fa0e35c3fe71abd6b5412e46416b,,semantic_scholar,,"LLMs demonstrate a remarkable performance on natural language tasks, but they are relatively less accurate when it comes to code generation. This gap stems from the inherent complexity of coding, which demands precise logical reasoning, meticulous attention to syntactic and semantic details, and sys"
126,,InfiAgent: Self-Evolving Pyramid Agent Framework for Infinite Scenarios,Chenglin Yu; Yang Yu; Songmiao Wang; Yucheng Wang; Yifan Yang,2025,arXiv.org,,,,,0,0.000,0.000,10.48550/arXiv.2509.22502,https://www.semanticscholar.org/paper/e65bbc15263d37ace731287af9bff60c283294a3,,semantic_scholar,,"Large Language Model (LLM) agents have demonstrated remarkable capabilities in organizing and executing complex tasks, and many such agents are now widely used in various application scenarios. However, developing these agents requires carefully designed workflows, carefully crafted prompts, and ite"
127,,Intrinsic Memory Agents: Heterogeneous Multi-Agent LLM Systems through Structured Contextual Memory,Sizhe Yuen; Francisco Gomez Medina; Ting Su; Yali Du; A. Sobey,2025,arXiv.org,,,,,0,0.000,0.000,10.48550/arXiv.2508.08997,https://www.semanticscholar.org/paper/6dafab866aee42bac0e0df26f9206170e711f0e1,,semantic_scholar,,"Multi-agent systems built on Large Language Models (LLMs) show exceptional promise for complex collaborative problem-solving, yet they face fundamental challenges stemming from context window limitations that impair memory consistency, role adherence, and procedural integrity. This paper introduces "
128,,A Genetic Programming-based Framework for Semi-automated Multi-agent Systems Engineering,Nicola Mc Donnell; J. Duggan; E. Howley,2023,ACM Transactions on Autonomous and Adaptive Systems,,,,,5,0.000,0.000,10.1145/3584731,https://www.semanticscholar.org/paper/7f108e5f67a638da0e56678a516aadd4e868b9cf,,semantic_scholar,,"With the rise of new technologies, such as Edge computing, Internet of Things, Smart Cities, and Smart Grids, there is a growing need for multi-agent systems (MAS) approaches. Designing multi-agent systems is challenging, and doing this in an automated way is even more so. To address this, we propos"
129,,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"