{"total":17,"rows":[{"id":"2503.09567","title":"Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models","created":"2025-03-12","cited_by_count":384,"area":"lr"},{"id":"2502.17419","title":"From System 1 to System 2: A Survey of Reasoning Large Language Models","created":"2025-02-24","cited_by_count":320,"area":"lr"},{"id":"2503.24235","title":"A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?","created":"2025-03-31","cited_by_count":223,"area":"lr"},{"id":"2505.19099","title":"SeePhys: Does Seeing Help Thinking? -- Benchmarking Vision-Based Physics Reasoning","created":"2025-05-25","cited_by_count":34,"area":"lr"},{"id":"2504.06260","title":"FEABench: Evaluating Language Models on Multiphysics Reasoning Ability","created":"2025-04-08","cited_by_count":22,"area":"lr"},{"id":"2509.26574","title":"Probing the Critical Point (CritPt) of AI Reasoning: a Frontier Physics Research Benchmark","created":"2025-09-30","cited_by_count":21,"area":"lr"},{"id":"2602.06176","title":"Large Language Model Reasoning Failures","created":"2026-02-05","cited_by_count":21,"area":"lr"},{"id":"2507.04766","title":"ABench-Physics: Benchmarking Physical Reasoning in LLMs via High-Difficulty and Dynamic Physics Problems","created":"2025-07-07","cited_by_count":18,"area":"lr"},{"id":"2506.20729","title":"Test-time Scaling Techniques in Theoretical Physics -- A Comparison of Methods on the TPBench Dataset","created":"2025-06-25","cited_by_count":8,"area":"lr"},{"id":"2508.00079","title":"PhysicsEval: Inference-Time Techniques to Improve the Reasoning Proficiency of Large Language Models on Physics Problems","created":"2025-07-31","cited_by_count":7,"area":"lr"},{"id":"2602.10092","title":"Quantum-Audit: Evaluating the Reasoning Limits of LLMs on Quantum Computing","created":"2026-02-10","cited_by_count":5,"area":"lr"},{"id":"2505.24823","title":"PhySense: Principle-Based Physics Reasoning Benchmarking for Large Language Models","created":"2025-05-30","cited_by_count":4,"area":"lr"},{"id":"2512.19799","title":"PhysMaster: Building an Autonomous AI Physicist for Theoretical and Computational Physics Research","created":"2025-12-22","cited_by_count":2,"area":"lr"},{"id":"2511.10515","title":"Mastering Olympiad-Level Physics with Artificial Intelligence","created":"2025-11-13","cited_by_count":1,"area":"lr"},{"id":"2604.14188","title":"Grading the Unspoken: Evaluating Tacit Reasoning in Quantum Field Theory and String Theory with LLMs","created":"2026-04-01","cited_by_count":1,"area":"lr"},{"id":"2604.18936","title":"Fine-Tuning Small Reasoning Models for Quantum Field Theory","created":"2026-04-21","cited_by_count":1,"area":"lr"},{"id":"2605.06772","title":"When Does Critique Improve AI-Assisted Theoretical Physics? SCALAR: Structured Critic--Actor Loop for Agentic Reasoning","created":"2026-05-07","cited_by_count":0,"area":"lr"}],"next_cursor":null}