{"total":20,"rows":[{"id":"2506.08745","title":"Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM Reasoning","created":"2025-06-10","cited_by_count":46,"area":"lr"},{"id":"2506.08292","title":"From Debate to Equilibrium: Belief-Driven Multi-Agent LLM Reasoning via Bayesian Nash Equilibrium","created":"2025-06-09","cited_by_count":26,"area":"lr"},{"id":"2604.05655","title":"LLM Reasoning as Trajectories: Step-Specific Representation Geometry and Correctness Signals","created":"2026-04-07","cited_by_count":24,"area":"lr"},{"id":"2506.08295","title":"From Passive to Active Reasoning: Can Large Language Models Ask the Right Questions under Incomplete Information?","created":"2025-06-09","cited_by_count":23,"area":"lr"},{"id":"2508.00410","title":"Co-rewarding: Stable Self-supervised RL for Eliciting Reasoning in Large Language Models","created":"2025-08-01","cited_by_count":23,"area":"lr"},{"id":"2603.09980","title":"Explainable LLM Unlearning Through Reasoning","created":"2026-02-08","cited_by_count":7,"area":"lr"},{"id":"2603.05900","title":"Reference-guided Policy Optimization for Molecular Optimization via LLM Reasoning","created":"2026-03-06","cited_by_count":6,"area":"lr"},{"id":"2506.11077","title":"CyclicReflex: Improving Reasoning Models via Cyclical Reflection Token Scheduling","created":"2025-06-04","cited_by_count":5,"area":"lr"},{"id":"2512.00831","title":"ReJump: A Tree-Jump Representation for Analyzing and Improving LLM Reasoning","created":"2025-11-30","cited_by_count":5,"area":"lr"},{"id":"2506.00783","title":"KG-TRACES: Enhancing Large Language Models with Knowledge Graph-constrained Trajectory Reasoning and Attribution Supervision","created":"2025-06-01","cited_by_count":4,"area":"lr"},{"id":"2511.11187","title":"ReTrace: Interactive Visualizations for Reasoning Traces of Large Reasoning Models","created":"2025-11-14","cited_by_count":3,"area":"lr"},{"id":"2506.04205","title":"EPiC: Towards Lossless Speedup for Reasoning Training through Edge-Preserving CoT Condensation","created":"2025-06-04","cited_by_count":2,"area":"lr"},{"id":"2605.14619","title":"SliceGraph: Mapping Process Isomers in Multi-Run Chain-of-Thought Reasoning","created":"2026-05-14","cited_by_count":2,"area":"lr"},{"id":"2606.07950","title":"The Easy, the Hard, and the Learnable: Confidence and Difficulty-Adaptive Policy Optimization for LLM Reasoning","created":"2026-06-06","cited_by_count":2,"area":"lr"},{"id":"2510.21623","title":"The Universal Landscape of Human Reasoning","created":"2025-10-24","cited_by_count":1,"area":"lr"},{"id":"2512.19135","title":"Understanding Chain-of-Thought in Large Language Models via Topological Data Analysis","created":"2025-12-22","cited_by_count":1,"area":"lr"},{"id":"2603.21286","title":"When the Chain Breaks: Interactive Diagnosis of LLM Chain-of-Thought Reasoning Errors","created":"2026-03-22","cited_by_count":1,"area":"lr"},{"id":"2606.11470","title":"The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes","created":"2026-06-09","cited_by_count":0,"area":"lr"},{"id":"2607.20427","title":"Is MoE Routing a Huffman Code? Discovering the Frequency-Diversity Law in Chain-of-Thought","created":"2026-05-08","cited_by_count":0,"area":"lr"},{"id":"2609.06403","title":"From Concentration to Differentiation and Back: Routing Effective Rank in MoE Reasoning Cohorts","created":"2026-09-06","cited_by_count":0,"area":"lr"}],"next_cursor":null}