{"total":57,"rows":[{"id":"2506.04210","title":"Does Thinking More always Help? Mirage of Test-Time Scaling in Reasoning Models","created":"2025-06-04","cited_by_count":53,"area":"lr"},{"id":"2508.02120","title":"Don't Overthink It: A Survey of Efficient R1-style Large Reasoning Models","created":"2025-08-04","cited_by_count":37,"area":"lr"},{"id":"2507.02076","title":"Reasoning on a Budget: A Survey of Adaptive and Controllable Test-Time Compute in LLMs","created":"2025-07-02","cited_by_count":31,"area":"lr"},{"id":"2508.15827","title":"Mini-Omni-Reasoner: Token-Level Thinking-in-Speaking in Large Speech Models","created":"2025-08-18","cited_by_count":21,"area":"lr"},{"id":"2505.22017","title":"The Price of a Second Thought: On the Evaluation of Reasoning Efficiency in Large Language Models","created":"2025-05-28","cited_by_count":18,"area":"lr"},{"id":"2507.09662","title":"Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey","created":"2025-07-13","cited_by_count":18,"area":"lr"},{"id":"2507.14958","title":"MUR: Momentum Uncertainty guided Reasoning for Large Language Models","created":"2025-07-20","cited_by_count":17,"area":"lr"},{"id":"2601.21919","title":"Self-Compression of Chain-of-Thought via Multi-Agent Reinforcement Learning","created":"2026-01-29","cited_by_count":13,"area":"lr"},{"id":"2507.08297","title":"KAT-V1: Kwai-AutoThink Technical Report","created":"2025-07-11","cited_by_count":12,"area":"lr"},{"id":"2507.20879","title":"DriveAgent-R1: Advancing VLM-based Autonomous Driving with Active Perception and Hybrid Thinking","created":"2025-07-28","cited_by_count":12,"area":"lr"},{"id":"2508.10026","title":"SABER: Switchable and Balanced Training for Efficient LLM Reasoning","created":"2025-08-08","cited_by_count":12,"area":"lr"},{"id":"2512.09616","title":"Rethinking Chain-of-Thought Reasoning for Videos","created":"2025-12-10","cited_by_count":9,"area":"lr"},{"id":"2605.02913","title":"Generate, Filter, Control, Replay: A Comprehensive Survey of Rollout Strategies for LLM Reinforcement Learning","created":"2026-04-08","cited_by_count":9,"area":"lr"},{"id":"2506.07104","title":"How Far Are We from Optimal Reasoning Efficiency?","created":"2025-06-08","cited_by_count":8,"area":"lr"},{"id":"2507.15844","title":"Hierarchical Budget Policy Optimization for Adaptive Reasoning","created":"2025-07-21","cited_by_count":8,"area":"lr"},{"id":"2510.05040","title":"Test-Time Scaling in Diffusion LLMs via Hidden Semi-Autoregressive Experts","created":"2025-10-06","cited_by_count":8,"area":"lr"},{"id":"2601.11340","title":"Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models","created":"2026-01-16","cited_by_count":8,"area":"lr"},{"id":"2602.10042","title":"Fake-HR1: Rethinking Reasoning of Vision Language Model for Synthetic Image Detection","created":"2026-02-10","cited_by_count":8,"area":"lr"},{"id":"2602.11683","title":"ThinkRouter: Efficient Reasoning via Routing Thinking between Latent and Discrete Spaces","created":"2026-02-12","cited_by_count":8,"area":"lr"},{"id":"2507.17307","title":"R-Stitch: Dynamic Trajectory Stitching for Efficient Reasoning","created":"2025-07-23","cited_by_count":7,"area":"lr"},{"id":"2601.07036","title":"Mid-Think: Training-Free Intermediate-Budget Reasoning via Token-Level Triggers","created":"2026-01-11","cited_by_count":6,"area":"lr"},{"id":"2603.11504","title":"LongFlow: Efficient KV Cache Compression for Reasoning Models","created":"2026-03-12","cited_by_count":6,"area":"lr"},{"id":"2603.14251","title":"Mitigating Overthinking in Large Reasoning Language Models via Reasoning Path Deviation Monitoring","created":"2026-03-15","cited_by_count":6,"area":"lr"},{"id":"2604.06787","title":"When Is Thinking Enough? Early Exit via Sufficiency Assessment for Efficient Reasoning","created":"2026-04-08","cited_by_count":6,"area":"lr"},{"id":"2509.26226","title":"Thinking-Free Policy Initialization Makes Distilled Reasoning Models More Effective and Efficient Reasoners","created":"2025-09-30","cited_by_count":5,"area":"lr"},{"id":"2510.08026","title":"PEAR: Phase Entropy Aware Reward for Efficient Reasoning","created":"2025-10-09","cited_by_count":5,"area":"lr"},{"id":"2601.03703","title":"TreeAdv: Tree-Structured Advantage Redistribution for Group-Based RL","created":"2026-01-07","cited_by_count":5,"area":"lr"},{"id":"2605.03314","title":"When to Think, When to Speak: Learning Disclosure Policies for LLM Reasoning","created":"2026-05-05","cited_by_count":5,"area":"lr"},{"id":"2508.04460","title":"From \"Aha Moments\" to Controllable Thinking: Toward Meta-Cognitive Reasoning in Large Reasoning Models via Decoupled Reasoning and Control","created":"2025-08-06","cited_by_count":4,"area":"lr"},{"id":"2510.13170","title":"Putting on the Thinking Hats: A Survey on Chain of Thought Fine-tuning from the Perspective of Human Reasoning Mechanism","created":"2025-10-15","cited_by_count":4,"area":"lr"},{"id":"2602.01472","title":"ConPress: Learning Efficient Reasoning from Multi-Question Contextual Pressure","created":"2026-02-01","cited_by_count":4,"area":"lr"},{"id":"2602.22556","title":"Stable Adaptive Thinking via Advantage Shaping and Length-Aware Gradient Regulation","created":"2026-02-26","cited_by_count":4,"area":"lr"},{"id":"2602.10004","title":"ESTAR: Early-Stopping Token-Aware Reasoning For Efficient Inference","created":"2026-02-10","cited_by_count":3,"area":"lr"},{"id":"2508.12265","title":"Fast, Slow, and Tool-augmented Thinking for LLMs: A Review","created":"2025-08-17","cited_by_count":2,"area":"lr"},{"id":"2509.24560","title":"AdaThink-Med: Optimizing Inference-Time Compute for Medical Reasoning via Uncertainty Quantification","created":"2025-09-29","cited_by_count":2,"area":"lr"},{"id":"2510.10207","title":"Adaptive Dual Reasoner: Large Reasoning Models Can Think Efficiently by Hybrid Reasoning","created":"2025-10-11","cited_by_count":2,"area":"lr"},{"id":"2510.20519","title":"Metis-HOME: Hybrid Optimized Mixture-of-Experts for Multimodal Reasoning","created":"2025-10-23","cited_by_count":2,"area":"lr"},{"id":"2510.21007","title":"Can Confidence Estimates Decide When Chain-of-Thought Is Necessary for LLMs?","created":"2025-10-23","cited_by_count":2,"area":"lr"},{"id":"2601.01195","title":"Reinforcement Learning Enhanced Multi-hop Reasoning for Temporal Knowledge Question Answering","created":"2026-01-03","cited_by_count":2,"area":"lr"},{"id":"2605.22873","title":"When Do LLMs Reason? A Dynamical Systems View via Entropy Phase Transitions","created":"2026-05-20","cited_by_count":2,"area":"lr"},{"id":"2507.15586","title":"Learning to Extract Rational Evidence via Reinforcement Learning for Retrieval-Augmented Generation","created":"2025-07-21","cited_by_count":1,"area":"lr"},{"id":"2601.04805","title":"Thinking-Based Non-Thinking: Solving the Reward Hacking Problem in Training Hybrid Reasoning Models via Reinforcement Learning","created":"2026-01-08","cited_by_count":1,"area":"lr"},{"id":"2603.00207","title":"VisRef: Visual Refocusing while Thinking Improves Test-Time Scaling in Multi-Modal Large Reasoning Models","created":"2026-02-27","cited_by_count":1,"area":"lr"},{"id":"2601.08679","title":"PersonaDual: Balancing Personalization and Objectivity via Adaptive Reasoning","created":"2026-01-13","cited_by_count":0,"area":"lr"},{"id":"2602.21435","title":"Synergizing Understanding and Generation with Interleaved Analyzing-Drafting Thinking","created":"2026-02-24","cited_by_count":0,"area":"lr"},{"id":"2603.16867","title":"Efficient Reasoning on the Edge","created":"2026-03-17","cited_by_count":0,"area":"lr"},{"id":"2604.08232","title":"HiRO-Nav: Hybrid ReasOning Enables Efficient Embodied Navigation","created":"2026-04-09","cited_by_count":0,"area":"lr"},{"id":"2605.06165","title":"Post-Reasoning: Improving the Performance of Non-Thinking Models at No Cost","created":"2026-05-07","cited_by_count":0,"area":"lr"},{"id":"2605.28398","title":"HRBench: Benchmarking and Understanding Thinking-Mode Switch Strategies in Hybrid-Reasoning LLMs","created":"2026-05-27","cited_by_count":0,"area":"lr"},{"id":"2608.10928","title":"ThinkRetrieve: Retrieval-Augmented Reasoning Traces for Test-Time Scaling","created":"2026-08-11","cited_by_count":0,"area":"lr"},{"id":"2608.26141","title":"AdaThinking-E: One-Token Entropy Regulation for Adaptive Thinking","created":"2026-06-26","cited_by_count":0,"area":"lr"},{"id":"2609.24760","title":"Construting Reverse Thinking: Developing Large Language Models' Reverse Thingking Ability","created":"2026-09-21","cited_by_count":0,"area":"lr"},{"id":"2609.29109","title":"CounterRoute: Self-Routed Reasoning via Hierarchical Counterfactual Credit Assignment","created":"2026-09-24","cited_by_count":0,"area":"lr"},{"id":"2609.34181","title":"Efficient Reasoning via Constrained Optimization in Latent Space","created":"2026-09-28","cited_by_count":0,"area":"lr"},{"id":"2610.11765","title":"Thinking Inertia: LLMs Keep Thinking When Told Not To","created":"2026-10-08","cited_by_count":0,"area":"lr"},{"id":"2610.14270","title":"The Thinking Leakage in Reinforcement Learning for LLMs","created":"2026-10-09","cited_by_count":0,"area":"lr"},{"id":"2610.15412","title":"From Candidate Tokens to Final Outcomes: Decoding Relative Long-Horizon Candidate Value from Reasoning Model Representations","created":"2026-10-11","cited_by_count":0,"area":"lr"}],"next_cursor":null}