{"total":63,"rows":[{"id":"2503.16419","title":"Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models","created":"2025-03-20","cited_by_count":464,"area":"lr"},{"id":"2501.04519","title":"rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking","created":"2025-01-08","cited_by_count":313,"area":"lr"},{"id":"2504.15895","title":"Dynamic Early Exit in Reasoning Models","created":"2025-04-22","cited_by_count":219,"area":"lr"},{"id":"2406.12624","title":"Judging the Judges: Evaluating Alignment and Vulnerabilities in LLMs-as-Judges","created":"2024-06-18","cited_by_count":205,"area":null},{"id":"2504.09858","title":"Reasoning Models Can Be Effective Without Thinking","created":"2025-04-14","cited_by_count":187,"area":"lr"},{"id":"2402.04614","title":"Faithfulness vs. Plausibility: On the (Un)Reliability of Explanations from Large Language Models","created":"2024-02-07","cited_by_count":105,"area":null},{"id":"2502.05589","title":"On Memory Construction and Retrieval for Personalized Conversational Agents","created":"2025-02-08","cited_by_count":105,"area":"rag"},{"id":"2502.12893","title":"H-CoT: Hijacking the Chain-of-Thought Safety Reasoning Mechanism to Jailbreak Large Reasoning Models, Including OpenAI o1/o3, DeepSeek-R1, and Gemini 2.0 Flash Thinking","created":"2025-02-18","cited_by_count":99,"area":"lr"},{"id":"2503.01141","title":"How Well do LLMs Compress Their Own Chain-of-Thought? A Token Complexity Approach","created":"2025-03-03","cited_by_count":97,"area":"lr"},{"id":"2504.10903","title":"Efficient Reasoning Models: A Survey","created":"2025-04-15","cited_by_count":95,"area":"lr"},{"id":"2505.07686","title":"S-GRPO: Early Exit via Reinforcement Learning in Reasoning Models","created":"2025-05-12","cited_by_count":87,"area":"lr"},{"id":"2505.11896","title":"AdaCoT: Pareto-Optimal Adaptive Chain-of-Thought Triggering via Reinforcement Learning","created":"2025-05-17","cited_by_count":87,"area":"lr"},{"id":"2504.05185","title":"Concise Reasoning via Reinforcement Learning","created":"2025-04-07","cited_by_count":79,"area":"lr"},{"id":"2502.08966","title":"RTBAS: Defending LLM Agents Against Prompt Injection and Privacy Leakage","created":"2025-02-13","cited_by_count":69,"area":"agents"},{"id":"2505.10832","title":"Learning When to Think: Shaping Adaptive Reasoning in R1-Style Models via Multi-Stage RL","created":"2025-05-16","cited_by_count":62,"area":"lr"},{"id":"2505.11274","title":"SelfBudgeter: Adaptive Token Allocation for Efficient LLM Reasoning","created":"2025-05-16","cited_by_count":48,"area":"lr"},{"id":"2405.18634","title":"A Theoretical Understanding of Self-Correction through In-context Alignment","created":"2024-05-28","cited_by_count":44,"area":"lr"},{"id":"2502.16707","title":"Reflective Planning: Vision-Language Models for Multi-Stage Long-Horizon Robotic Manipulation","created":"2025-02-23","cited_by_count":42,"area":"mllm"},{"id":"2407.06813","title":"Richelieu: Self-Evolving LLM-Based Agents for AI Diplomacy","created":"2024-07-09","cited_by_count":36,"area":"agents"},{"id":"2502.14815","title":"Optimizing Model Selection for Compound AI Systems","created":"2025-02-20","cited_by_count":36,"area":null},{"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":"2505.14604","title":"Let LRMs Break Free from Overthinking via Self-Braking Tuning","created":"2025-05-20","cited_by_count":27,"area":"lr"},{"id":"2505.12284","title":"Shorten After You're Right: Lazy Length Penalties for Reasoning RL","created":"2025-05-18","cited_by_count":25,"area":"lr"},{"id":"2510.10035","title":"Failure-Driven Workflow Refinement","created":"2025-10-11","cited_by_count":20,"area":null},{"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":"2506.02397","title":"OThink-R1: Intrinsic Fast/Slow Thinking Mode Switching for Over-Reasoning Mitigation","created":"2025-06-03","cited_by_count":17,"area":"lr"},{"id":"2410.15048","title":"MorphAgent: Empowering Agents through Self-Evolving Profiles and Decentralized Collaboration","created":"2024-10-19","cited_by_count":16,"area":"agents"},{"id":"2502.04322","title":"Speak Easy: Eliciting Harmful Jailbreaks from LLMs with Simple Interactions","created":"2025-02-06","cited_by_count":15,"area":"alignment"},{"id":"2510.24941","title":"Can Aha Moments Be Fake? Towards Quantifying Decorative and True Thinking in Chain-of-Thought","created":"2025-10-28","cited_by_count":15,"area":"lr"},{"id":"2506.11102","title":"Evolutionary Perspectives on the Evaluation of LLM-Based AI Agents: A Comprehensive Survey","created":"2025-06-06","cited_by_count":13,"area":"agents"},{"id":"2509.13990","title":"Slim-SC: Thought Pruning for Efficient Scaling with Self-Consistency","created":"2025-09-17","cited_by_count":13,"area":"lr"},{"id":"2409.07355","title":"Think Together and Work Better: Combining Humans' and LLMs' Think-Aloud Outcomes for Effective Text Evaluation","created":"2024-09-11","cited_by_count":12,"area":"lr"},{"id":"2506.18810","title":"ConciseHint: Boosting Efficient Reasoning via Continuous Concise Hints during Generation","created":"2025-06-23","cited_by_count":10,"area":"lr"},{"id":"2509.24156","title":"Reasoning or Retrieval? A Study of Answer Attribution on Large Reasoning Models","created":"2025-09-29","cited_by_count":10,"area":"lr"},{"id":"2505.24550","title":"A*-Thought: Efficient Reasoning via Bidirectional Compression for Low-Resource Settings","created":"2025-05-30","cited_by_count":9,"area":"lr"},{"id":"2506.04182","title":"Long or short CoT? Investigating Instance-level Switch of Large Reasoning Models","created":"2025-06-04","cited_by_count":8,"area":"lr"},{"id":"2501.09368","title":"Aligning Instruction Tuning with Pre-training","created":"2025-01-16","cited_by_count":7,"area":null},{"id":"2505.07861","title":"Scalable LLM Reasoning Acceleration with Low-rank Distillation","created":"2025-05-08","cited_by_count":7,"area":"lr"},{"id":"2505.10846","title":"AutoRAN: Automated Hijacking of Safety Reasoning in Large Reasoning Models","created":"2025-05-16","cited_by_count":7,"area":"lr"},{"id":"2506.00309","title":"Evaluation of LLMs for mathematical problem solving","created":"2025-05-30","cited_by_count":7,"area":null},{"id":"2509.18713","title":"MemOrb: A Plug-and-Play Verbal-Reinforcement Memory Layer for E-Commerce Customer Service","created":"2025-09-23","cited_by_count":7,"area":"agents"},{"id":"2402.16406","title":"From RAGs to riches: Utilizing large language models to write documents for clinical trials","created":"2024-02-26","cited_by_count":6,"area":null},{"id":"2507.02663","title":"Think How to Think: Mitigating Overthinking with Autonomous Difficulty Cognition in Large Reasoning Models","created":"2025-07-03","cited_by_count":5,"area":"lr"},{"id":"2502.10760","title":"Why is prompting hard? Understanding prompts on binary sequence predictors","created":"2025-02-15","cited_by_count":4,"area":null},{"id":"2510.06052","title":"MixReasoning: Switching Modes to Think","created":"2025-10-07","cited_by_count":3,"area":"lr"},{"id":"2601.03542","title":"Layer-Order Inversion: Rethinking Latent Multi-Hop Reasoning in Large Language Models","created":"2026-01-07","cited_by_count":3,"area":"lr"},{"id":"2503.01385","title":"Q-NL Verifier: Leveraging Synthetic Data for Robust Knowledge Graph Question Answering","created":"2025-03-03","cited_by_count":2,"area":"synth"},{"id":"2505.10844","title":"Creativity or Brute Force? Using Brainteasers as a Window into the Problem-Solving Abilities of Large Language Models","created":"2025-05-16","cited_by_count":2,"area":null},{"id":"2509.05660","title":"Cross-Question Method Reuse in Large Language Models: From Word-Level Prediction to Rational Logical-Layer Reasoning","created":"2025-09-06","cited_by_count":2,"area":"lr"},{"id":"2604.07922","title":"SAT: Balancing Reasoning Accuracy and Efficiency with Stepwise Adaptive Thinking","created":"2026-04-09","cited_by_count":2,"area":"lr"},{"id":"2607.15736","title":"Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning","created":"2026-07-17","cited_by_count":2,"area":"lr"},{"id":"2608.01631","title":"Does Accuracy Equal Evidence? Reasoning Faithfulness under KV Cache Compression","created":"2026-08-03","cited_by_count":2,"area":"inference"},{"id":"2608.04771","title":"Fewer Tokens, Smaller Cache: Reward-Coordinated Efficient Reasoning","created":"2026-08-05","cited_by_count":2,"area":"lr"},{"id":"2603.08462","title":"Reasoning as Compression: Unifying Budget Forcing via the Conditional Information Bottleneck","created":"2026-03-09","cited_by_count":1,"area":"lr"},{"id":"2604.09750","title":"Conflicts Make Large Reasoning Models Vulnerable to Attacks","created":"2026-04-10","cited_by_count":1,"area":"lr"},{"id":"2604.17803","title":"Adversarial Arena: Crowdsourcing Data Generation through Interactive Competition","created":"2026-04-20","cited_by_count":1,"area":null},{"id":"2608.03401","title":"Shorter Reasoning, Earlier Answers? An Evaluation of Reasoning Interfaces","created":"2026-08-04","cited_by_count":1,"area":"lr"},{"id":"2608.27963","title":"SABER: Stability-Aware Early Exit for LLM Reasoning via Adversarial Branch Probing","created":"2026-08-28","cited_by_count":1,"area":"lr"},{"id":"2507.23776","title":"Cascaded Information Disclosure for Generalized Evaluation of Problem Solving Capabilities","created":"2025-07-31","cited_by_count":0,"area":null},{"id":"2603.16867","title":"Efficient Reasoning on the Edge","created":"2026-03-17","cited_by_count":0,"area":"lr"},{"id":"2609.16014","title":"ViCo: Visual-oriented Coding with Self-Reflection for Chart Replication","created":"2026-08-31","cited_by_count":0,"area":null},{"id":"2609.37304","title":"MetaCtrl: Your Large Language Models Can Reason Better and More Concisely with a Metacognitive Controller","created":"2026-09-29","cited_by_count":0,"area":"lr"},{"id":"2610.19796","title":"Learning Perception to Action Rules through Reinforcement Learning for Theory-of-Mind Reasoning","created":"2026-10-14","cited_by_count":0,"area":"lr"}],"next_cursor":null}