{"total":77,"rows":[{"id":"2501.19393","title":"s1: Simple test-time scaling","created":"2025-01-31","cited_by_count":1341,"area":"lr"},{"id":"2307.03109","title":"A Survey on Evaluation of Large Language Models","created":"2023-07-06","cited_by_count":1242,"area":null},{"id":"2410.07985","title":"Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models","created":"2024-10-10","cited_by_count":284,"area":"lr"},{"id":"2504.11456","title":"DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning","created":"2025-04-15","cited_by_count":280,"area":"lr"},{"id":"2312.11970","title":"Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives","created":"2023-12-19","cited_by_count":257,"area":"agents"},{"id":"2307.10635","title":"SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models","created":"2023-07-20","cited_by_count":256,"area":"lr"},{"id":"2310.19736","title":"Evaluating Large Language Models: A Comprehensive Survey","created":"2023-10-30","cited_by_count":202,"area":null},{"id":"2402.19450","title":"Functional Benchmarks for Robust Evaluation of Reasoning Performance, and the Reasoning Gap","created":"2024-02-29","cited_by_count":79,"area":"lr"},{"id":"2406.12753","title":"OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI","created":"2024-06-18","cited_by_count":71,"area":"lr"},{"id":"2502.12054","title":"PhysReason: A Comprehensive Benchmark towards Physics-Based Reasoning","created":"2025-02-17","cited_by_count":59,"area":"lr"},{"id":"2502.00334","title":"UGPhysics: A Comprehensive Benchmark for Undergraduate Physics Reasoning with Large Language Models","created":"2025-02-01","cited_by_count":48,"area":"lr"},{"id":"2508.21148","title":"A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers","created":"2025-08-28","cited_by_count":37,"area":"agents"},{"id":"2306.10512","title":"Position: AI Evaluation Should Learn from How We Test Humans","created":"2023-06-18","cited_by_count":36,"area":null},{"id":"2602.02710","title":"Maximum Likelihood Reinforcement Learning","created":"2026-02-02","cited_by_count":36,"area":"deeprl"},{"id":"2404.01475","title":"Are large language models superhuman chemists?","created":"2024-04-01","cited_by_count":34,"area":null},{"id":"2502.15815","title":"Theoretical Physics Benchmark (TPBench) -- a Dataset and Study of AI Reasoning Capabilities in Theoretical Physics","created":"2025-02-19","cited_by_count":34,"area":"lr"},{"id":"2502.02871","title":"Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning","created":"2025-02-05","cited_by_count":33,"area":"lr"},{"id":"2410.09988","title":"HARDMath: A Benchmark Dataset for Challenging Problems in Applied Mathematics","created":"2024-10-13","cited_by_count":32,"area":null},{"id":"2309.07418","title":"A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Time","created":"2023-09-14","cited_by_count":30,"area":null},{"id":"2407.08348","title":"Skywork-Math: Data Scaling Laws for Mathematical Reasoning in Large Language Models -- The Story Goes On","created":"2024-07-11","cited_by_count":24,"area":"lr"},{"id":"2412.00821","title":"Improving Physics Reasoning in Large Language Models Using Mixture of Refinement Agents","created":"2024-12-01","cited_by_count":24,"area":"lr"},{"id":"2404.08704","title":"MM-PhyQA: Multimodal Physics Question-Answering With Multi-Image CoT Prompting","created":"2024-04-11","cited_by_count":15,"area":null},{"id":"2503.13503","title":"SciHorizon: Benchmarking AI-for-Science Readiness from Scientific Data to Large Language Models","created":"2025-03-12","cited_by_count":15,"area":null},{"id":"2401.15127","title":"Evaluation of LLM Chatbots for OSINT-based Cyber Threat Awareness","created":"2024-01-26","cited_by_count":14,"area":null},{"id":"2501.05382","title":"Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Models","created":"2025-01-09","cited_by_count":14,"area":null},{"id":"2305.18342","title":"Neural Task Synthesis for Visual Programming","created":"2023-05-26","cited_by_count":13,"area":null},{"id":"2405.19740","title":"PertEval: Unveiling Real Knowledge Capacity of LLMs with Knowledge-Invariant Perturbations","created":"2024-05-30","cited_by_count":13,"area":null},{"id":"2406.10834","title":"Exposing the Achilles' Heel: Evaluating LLMs Ability to Handle Mistakes in Mathematical Reasoning","created":"2024-06-16","cited_by_count":13,"area":"lr"},{"id":"2509.17677","title":"EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving","created":"2025-09-22","cited_by_count":12,"area":"lr"},{"id":"2308.09115","title":"MaScQA: A Question Answering Dataset for Investigating Materials Science Knowledge of Large Language Models","created":"2023-08-17","cited_by_count":11,"area":null},{"id":"2408.11841","title":"Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants","created":"2024-08-07","cited_by_count":10,"area":null},{"id":"2507.00711","title":"Large Reasoning Models are not thinking straight: on the unreliability of thinking trajectories","created":"2025-07-01","cited_by_count":10,"area":"lr"},{"id":"2601.18091","title":"From LLMs to LRMs: Rethinking Pruning for Reasoning-Centric Models","created":"2026-01-26","cited_by_count":10,"area":"lr"},{"id":"2407.10380","title":"NTSEBENCH: Cognitive Reasoning Benchmark for Vision Language Models","created":"2024-07-15","cited_by_count":9,"area":"lr"},{"id":"2508.01670","title":"QCBench: Evaluating Large Language Models on Domain-Specific Quantitative Chemistry","created":"2025-08-03","cited_by_count":8,"area":null},{"id":"2604.06802","title":"Riemann-Bench: A Benchmark for Moonshot Mathematics","created":"2026-04-08","cited_by_count":8,"area":null},{"id":"2404.02255","title":"$\\texttt{LM}^\\texttt{2}$: A Simple Society of Language Models Solves Complex Reasoning","created":"2024-04-02","cited_by_count":7,"area":"lr"},{"id":"2307.03762","title":"Brain in a Vat: On Missing Pieces Towards Artificial General Intelligence in Large Language Models","created":"2023-07-07","cited_by_count":6,"area":null},{"id":"2509.21155","title":"Learning the Wrong Lessons: Syntactic-Domain Spurious Correlations in Language Models","created":"2025-09-25","cited_by_count":6,"area":null},{"id":"2604.18584","title":"MathNet: a Global Multimodal Benchmark for Mathematical Reasoning and Retrieval","created":"2026-04-20","cited_by_count":6,"area":"lr"},{"id":"2510.19208","title":"DiSRouter: Distributed Self-Routing for LLM Selections","created":"2025-10-22","cited_by_count":5,"area":null},{"id":"2404.17513","title":"A Comprehensive Evaluation on Event Reasoning of Large Language Models","created":"2024-04-26","cited_by_count":4,"area":"lr"},{"id":"2411.05442","title":"IntellBot: Retrieval Augmented LLM Chatbot for Cyber Threat Knowledge Delivery","created":"2024-11-08","cited_by_count":4,"area":"rag"},{"id":"2412.00102","title":"ElectroVizQA: How well do Multi-modal LLMs perform in Electronics Visual Question Answering?","created":"2024-11-27","cited_by_count":4,"area":"mllm"},{"id":"2508.13214","title":"Too Easily Fooled? Prompt Injection Breaks LLMs on Frustratingly Simple Multiple-Choice Questions","created":"2025-08-16","cited_by_count":4,"area":"safety"},{"id":"2512.23565","title":"RxnBench: A Multimodal Benchmark for Evaluating Large Language Models on Chemical Reaction Understanding from Scientific Literature","created":"2025-12-29","cited_by_count":4,"area":"mllm"},{"id":"2604.11805","title":"Solving Physics Olympiad via Reinforcement Learning on Physics Simulators","created":"2026-04-13","cited_by_count":4,"area":"lr"},{"id":"2406.09891","title":"Benchmarking Generative Models on Computational Thinking Tests in Elementary Visual Programming","created":"2024-06-14","cited_by_count":3,"area":null},{"id":"2409.13753","title":"Synergistic Simulations: Multi-Agent Problem Solving with Large Language Models","created":"2024-09-14","cited_by_count":3,"area":"agents"},{"id":"2502.06279","title":"DebateBench: A Challenging Long Context Reasoning Benchmark For Large Language Models","created":"2025-02-10","cited_by_count":3,"area":"lr"},{"id":"2509.07711","title":"RIMO: An Easy-to-Evaluate, Hard-to-Solve Olympiad Benchmark for Advanced Mathematical Reasoning","created":"2025-09-09","cited_by_count":3,"area":"lr"},{"id":"2511.22888","title":"Adversarial Training for Process Reward Models","created":"2025-11-28","cited_by_count":3,"area":"lr"},{"id":"2408.06598","title":"A Perspective on Large Language Models, Intelligent Machines, and Knowledge Acquisition","created":"2024-08-13","cited_by_count":2,"area":null},{"id":"2506.04255","title":"HASHIRU: Hierarchical Agent System for Hybrid Intelligent Resource Utilization","created":"2025-06-01","cited_by_count":2,"area":"agents"},{"id":"2602.02192","title":"ECHO-2: A Large-Scale Distributed Rollout Framework for Cost-Efficient Reinforcement Learning","created":"2026-02-02","cited_by_count":2,"area":"lr"},{"id":"2605.26132","title":"Self-Verified Distillation: Your Language Model Is Secretly Its Own Synthetic Data Pipeline","created":"2026-05-20","cited_by_count":2,"area":"synth"},{"id":"2608.26070","title":"Prefix Sliding for efficient test-time scaling","created":"2026-08-26","cited_by_count":2,"area":"lr"},{"id":"2609.20612","title":"What Does Privileged Information Add to On-Policy Self-Distillation?","created":"2026-09-17","cited_by_count":2,"area":"distill"},{"id":"2309.08881","title":"ChatGPT-4 with Code Interpreter can be used to solve introductory college-level vector calculus and electromagnetism problems","created":"2023-09-16","cited_by_count":1,"area":null},{"id":"2508.03737","title":"GanitBench: A bi-lingual benchmark for evaluating mathematical reasoning in Vision Language Models","created":"2025-07-31","cited_by_count":1,"area":"lr"},{"id":"2511.04108","title":"Batch Prompting Suppresses Overthinking Reasoning Under Constraint: How Batch Prompting Suppresses Overthinking in Reasoning Models","created":"2025-11-06","cited_by_count":1,"area":"lr"},{"id":"2512.05954","title":"SymPyBench: A Dynamic Benchmark for Scientific Reasoning with Executable Python Code","created":"2025-12-05","cited_by_count":1,"area":"lr"},{"id":"2604.02934","title":"PolyReal: A Benchmark for Real-World Polymer Science Workflows","created":"2026-04-03","cited_by_count":1,"area":null},{"id":"2505.20707","title":"Dissecting Physics Reasoning in Small Language Models: A Multi-Dimensional Analysis from an Educational Perspective","created":"2025-05-27","cited_by_count":0,"area":"lr"},{"id":"2508.21143","title":"The Percept-V Challenge: Can Multimodal LLMs Crack Simple Perception Problems?","created":"2025-08-28","cited_by_count":0,"area":"mllm"},{"id":"2510.12997","title":"Max It or Miss It: Benchmarking LLM On Solving Extremal Problems","created":"2025-10-14","cited_by_count":0,"area":"lr"},{"id":"2512.05930","title":"PRiSM: An Agentic Multimodal Benchmark for Scientific Reasoning via Python-Grounded Evaluation","created":"2025-12-05","cited_by_count":0,"area":"lr"},{"id":"2601.14506","title":"Compounding Disadvantage: Auditing Intersectional Bias in LLM-Generated Explanations Across Indian and American STEM Education","created":"2026-01-20","cited_by_count":0,"area":null},{"id":"2602.10518","title":"MapVerse: A Benchmark for Geospatial Question Answering on Diverse Real-World Maps","created":"2026-02-11","cited_by_count":0,"area":"lr"},{"id":"2603.18073","title":"Continually self-improving AI","created":"2026-03-18","cited_by_count":0,"area":"synth"},{"id":"2603.23800","title":"Object Search in Partially-Known Environments via LLM-informed Model-based Planning and Prompt Selection","created":"2026-03-25","cited_by_count":0,"area":null},{"id":"2606.26108","title":"Where Larger Models Excel: The Primacy of Constraint-Guided Reasoning","created":"2026-05-09","cited_by_count":0,"area":"lr"},{"id":"2607.05199","title":"Reason, Reward, Refine: Step-Level Errors Corrections with Structured Feedback for Physics Reasoning in Small Language Models","created":"2026-07-06","cited_by_count":0,"area":"lr"},{"id":"2607.20520","title":"Representation Robustness Under Executable Reasoning Constraints in Large Language Models for Mathematical Problem Solving","created":"2026-07-08","cited_by_count":0,"area":"lr"},{"id":"2608.14226","title":"RankT2I: A Submodular Framework for Discovering Interpretable and Diverse Semantics in Text-to-Image Models","created":"2026-08-14","cited_by_count":0,"area":null},{"id":"2609.32235","title":"Solving Every Step Is Not Enough: Milestone Oracles Reveal a Composition Gap in LLM Math Reasoning","created":"2026-09-26","cited_by_count":0,"area":"lr"},{"id":"2610.20401","title":"Training Language Models to Fail Gracefully","created":"2026-10-14","cited_by_count":0,"area":null}],"next_cursor":null}