{"total":94,"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":"2501.09686","title":"Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models","created":"2025-01-16","cited_by_count":293,"area":"wm"},{"id":"2412.18925","title":"HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs","created":"2024-12-25","cited_by_count":265,"area":"lr"},{"id":"2506.10947","title":"Spurious Rewards: Rethinking Training Signals in RLVR","created":"2025-06-12","cited_by_count":209,"area":"lr"},{"id":"2506.01347","title":"The Surprising Effectiveness of Negative Reinforcement in LLM Reasoning","created":"2025-06-02","cited_by_count":162,"area":"lr"},{"id":"2502.06703","title":"Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling","created":"2025-02-10","cited_by_count":136,"area":"lr"},{"id":"2504.00993","title":"MedReason: Eliciting Factual Medical Reasoning Steps in LLMs via Knowledge Graphs","created":"2025-04-01","cited_by_count":110,"area":"lr"},{"id":"2504.00891","title":"GenPRM: Scaling Test-Time Compute of Process Reward Models via Generative Reasoning","created":"2025-04-01","cited_by_count":97,"area":"lr"},{"id":"2503.09501","title":"ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement Learning","created":"2025-03-12","cited_by_count":79,"area":"lr"},{"id":"2412.00154","title":"o1-Coder: an o1 Replication for Coding","created":"2024-11-29","cited_by_count":78,"area":null},{"id":"2504.16074","title":"PHYBench: Holistic Evaluation of Physical Perception and Reasoning in Large Language Models","created":"2025-04-22","cited_by_count":61,"area":"lr"},{"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":"2504.16129","title":"MARFT: Multi-Agent Reinforcement Fine-Tuning","created":"2025-04-21","cited_by_count":52,"area":"agents"},{"id":"2412.14135","title":"Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective","created":"2024-12-18","cited_by_count":50,"area":"lr"},{"id":"2504.00294","title":"Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies Ahead","created":"2025-03-31","cited_by_count":44,"area":"lr"},{"id":"2501.11223","title":"Reasoning Language Models: A Blueprint","created":"2025-01-20","cited_by_count":42,"area":"lr"},{"id":"2502.06737","title":"VersaPRM: Multi-Domain Process Reward Model via Synthetic Reasoning Data","created":"2025-02-10","cited_by_count":40,"area":"lr"},{"id":"2510.08049","title":"A Survey of Process Reward Models: From Outcome Signals to Process Supervisions for Large Language Models","created":"2025-10-09","cited_by_count":38,"area":"lr"},{"id":"2503.12854","title":"Enhancing LLM Reasoning with Iterative DPO: A Comprehensive Empirical Investigation","created":"2025-03-17","cited_by_count":37,"area":"lr"},{"id":"2505.17952","title":"Beyond Distillation: Pushing the Limits of Medical LLM Reasoning with Minimalist Rule-Based RL","created":"2025-05-23","cited_by_count":36,"area":"lr"},{"id":"2411.06018","title":"A Picture is Worth A Thousand Numbers: Enabling LLMs Reason about Time Series via Visualization","created":"2024-11-09","cited_by_count":33,"area":"lr"},{"id":"2505.13427","title":"MM-PRM: Enhancing Multimodal Mathematical Reasoning with Scalable Step-Level Supervision","created":"2025-05-19","cited_by_count":31,"area":"lr"},{"id":"2412.21154","title":"Aviary: training language agents on challenging scientific tasks","created":"2024-12-30","cited_by_count":26,"area":"agents"},{"id":"2509.03871","title":"A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Models","created":"2025-09-04","cited_by_count":25,"area":"lr"},{"id":"2502.14361","title":"Retrieval-Augmented Process Reward Model for Generalizable Mathematical Reasoning","created":"2025-02-20","cited_by_count":23,"area":"lr"},{"id":"2411.14033","title":"LLM-based Multi-Agent Systems: Techniques and Business Perspectives","created":"2024-11-21","cited_by_count":22,"area":"agents"},{"id":"2502.06773","title":"On the Emergence of Thinking in LLMs I: Searching for the Right Intuition","created":"2025-02-10","cited_by_count":22,"area":"lr"},{"id":"2412.15118","title":"Reasoning Through Execution: Unifying Process and Outcome Rewards for Code Generation","created":"2024-12-19","cited_by_count":21,"area":"lr"},{"id":"2503.15952","title":"Adaptive Group Policy Optimization: Towards Stable Training and Token-Efficient Reasoning","created":"2025-03-20","cited_by_count":21,"area":"lr"},{"id":"2504.11741","title":"Climbing the Ladder of Reasoning: What LLMs Can-and Still Can't-Solve after SFT?","created":"2025-04-16","cited_by_count":21,"area":"lr"},{"id":"2502.09673","title":"Are Smarter LLMs Safer? Exploring Safety-Reasoning Trade-offs in Prompting and Fine-Tuning","created":"2025-02-13","cited_by_count":20,"area":"lr"},{"id":"2505.02665","title":"A Survey of Slow Thinking-based Reasoning LLMs using Reinforced Learning and Inference-time Scaling Law","created":"2025-05-05","cited_by_count":19,"area":"lr"},{"id":"2411.11930","title":"AtomThink: Multimodal Slow Thinking with Atomic Step Reasoning","created":"2024-11-18","cited_by_count":17,"area":"lr"},{"id":"2502.16268","title":"ThinkBench: Dynamic Out-of-Distribution Evaluation for Robust LLM Reasoning","created":"2025-02-22","cited_by_count":17,"area":"lr"},{"id":"2503.20271","title":"ViLBench: A Suite for Vision-Language Process Reward Modeling","created":"2025-03-26","cited_by_count":16,"area":"lr"},{"id":"2505.02865","title":"Accelerating Large Language Model Reasoning via Speculative Search","created":"2025-05-03","cited_by_count":16,"area":"lr"},{"id":"2509.06733","title":"Reinforcement Learning Foundations for Deep Research Systems: A Survey","created":"2025-09-08","cited_by_count":14,"area":"lr"},{"id":"2412.16849","title":"OpenRFT: Adapting Reasoning Foundation Model for Domain-specific Tasks with Reinforcement Fine-Tuning","created":"2024-12-22","cited_by_count":13,"area":"lr"},{"id":"2503.13447","title":"MetaScale: Test-Time Scaling with Evolving Meta-Thoughts","created":"2025-03-17","cited_by_count":13,"area":"lr"},{"id":"2509.26628","title":"Attention as a Compass: Efficient Exploration for Process-Supervised RL in Reasoning Models","created":"2025-09-30","cited_by_count":13,"area":"lr"},{"id":"2502.06217","title":"Examining False Positives under Inference Scaling for Mathematical Reasoning","created":"2025-02-10","cited_by_count":11,"area":"lr"},{"id":"2502.11514","title":"Investigating Inference-time Scaling for Chain of Multi-modal Thought: A Preliminary Study","created":"2025-02-17","cited_by_count":11,"area":"lr"},{"id":"2507.23317","title":"Good Learners Think Their Thinking: Generative PRM Makes Large Reasoning Model More Efficient Math Learner","created":"2025-07-31","cited_by_count":11,"area":"lr"},{"id":"2503.06252","title":"Can Atomic Step Decomposition Enhance the Self-structured Reasoning of Multimodal Large Models?","created":"2025-03-08","cited_by_count":10,"area":"lr"},{"id":"2504.19981","title":"Accurate and Diverse LLM Mathematical Reasoning via Automated PRM-Guided GFlowNets","created":"2025-04-28","cited_by_count":10,"area":"lr"},{"id":"2507.15512","title":"Step-level Verifier-guided Hybrid Test-Time Scaling for Large Language Models","created":"2025-07-21","cited_by_count":10,"area":"lr"},{"id":"2508.13023","title":"G$^2$RPO-A: Guided Group Relative Policy Optimization with Adaptive Guidance","created":"2025-08-18","cited_by_count":10,"area":"lr"},{"id":"2510.14942","title":"GroundedPRM: Tree-Guided and Fidelity-Aware Process Reward Modeling for Step-Level Reasoning","created":"2025-10-16","cited_by_count":10,"area":"lr"},{"id":"2505.19634","title":"Faster and Better LLMs via Latency-Aware Test-Time Scaling","created":"2025-05-26","cited_by_count":9,"area":"lr"},{"id":"2503.01642","title":"Graph-Augmented Reasoning: Evolving Step-by-Step Knowledge Graph Retrieval for LLM Reasoning","created":"2025-03-03","cited_by_count":8,"area":"lr"},{"id":"2505.19706","title":"Error Typing for Smarter Rewards: Improving Process Reward Models with Error-Aware Hierarchical Supervision","created":"2025-05-26","cited_by_count":8,"area":"lr"},{"id":"2505.20325","title":"Guided by Gut: Efficient Test-Time Scaling with Reinforced Intrinsic Confidence","created":"2025-05-23","cited_by_count":8,"area":"lr"},{"id":"2510.01925","title":"Enhancing Large Language Model Reasoning with Reward Models: An Analytical Survey","created":"2025-10-02","cited_by_count":8,"area":"lr"},{"id":"2502.11525","title":"Beyond Single-Task: Robust Multi-Task Length Generalization for LLMs","created":"2025-02-17","cited_by_count":7,"area":null},{"id":"2509.16548","title":"SCAN: Self-Denoising Monte Carlo Annotation for Robust Process Reward Learning","created":"2025-09-20","cited_by_count":6,"area":"lr"},{"id":"2512.22716","title":"Memento 2: Learning by Stateful Reflective Memory","created":"2025-12-27","cited_by_count":6,"area":"agents"},{"id":"2502.16040","title":"Inference Computation Scaling for Feature Augmentation in Recommendation Systems","created":"2025-02-22","cited_by_count":5,"area":null},{"id":"2503.15944","title":"From Chaos to Order: The Atomic Reasoner Framework for Fine-grained Reasoning in Large Language Models","created":"2025-03-20","cited_by_count":5,"area":"lr"},{"id":"2505.11730","title":"Rethinking Optimal Verification Granularity for Compute-Efficient Test-Time Scaling","created":"2025-05-16","cited_by_count":5,"area":"lr"},{"id":"2505.11875","title":"J1: Exploring Simple Test-Time Scaling for LLM-as-a-Judge","created":"2025-05-17","cited_by_count":5,"area":"lr"},{"id":"2507.15698","title":"CoLD: Counterfactually-Guided Length Debiasing for Process Reward Models in Mathematical Reasoning","created":"2025-07-21","cited_by_count":5,"area":"lr"},{"id":"2510.06217","title":"TaTToo: Tool-Grounded Thinking PRM for Test-Time Scaling in Tabular Reasoning","created":"2025-10-07","cited_by_count":5,"area":"lr"},{"id":"2603.00142","title":"Evaluating Theory of Mind and Internal Beliefs in LLM-Based Multi-Agent Systems","created":"2026-02-24","cited_by_count":5,"area":"agents"},{"id":"2503.12167","title":"PLM: Efficient Peripheral Language Models Hardware-Co-Designed for Ubiquitous Computing","created":"2025-03-15","cited_by_count":4,"area":null},{"id":"2505.00472","title":"UserCentrix: An Agentic Memory-augmented AI Framework for Smart Spaces","created":"2025-05-01","cited_by_count":4,"area":"agents"},{"id":"2512.01210","title":"Knowledge Graph Augmented Large Language Models for Disease Prediction","created":"2025-12-01","cited_by_count":4,"area":null},{"id":"2502.09042","title":"Typhoon T1: An Open Thai Reasoning Model","created":"2025-02-13","cited_by_count":3,"area":"lr"},{"id":"2509.17380","title":"Correlation or Causation: Analyzing the Causal Structures of LLM and LRM Reasoning Process","created":"2025-09-22","cited_by_count":3,"area":"lr"},{"id":"2510.27469","title":"Diffuse Thinking: Exploring Diffusion Language Models as Efficient Thought Proposers for Reasoning","created":"2025-10-31","cited_by_count":3,"area":"lr"},{"id":"2604.07851","title":"ReRec: Reasoning-Augmented LLM-based Recommendation Assistant via Reinforcement Fine-tuning","created":"2026-04-09","cited_by_count":3,"area":"lr"},{"id":"2605.01345","title":"The Perceptual Bandwidth Bottleneck in Vision-Language Models: Active Visual Reasoning via Sequential Experimental Design","created":"2026-05-02","cited_by_count":3,"area":"lr"},{"id":"2411.17075","title":"Don't Command, Cultivate: An Exploratory Study of System-2 Alignment","created":"2024-11-26","cited_by_count":2,"area":"safety"},{"id":"2504.18942","title":"LawFlow: Collecting and Simulating Lawyers' Thought Processes on Business Formation Case Studies","created":"2025-04-26","cited_by_count":2,"area":"lr"},{"id":"2511.00521","title":"Reasoning Planning for Language Models","created":"2025-11-01","cited_by_count":2,"area":"lr"},{"id":"2602.01709","title":"ARTIS: Agentic Risk-Aware Test-Time Scaling via Iterative Simulation","created":"2026-02-02","cited_by_count":2,"area":"lr"},{"id":"2602.20132","title":"LAD: Learning Advantage Distribution for Reasoning","created":"2026-02-23","cited_by_count":2,"area":"lr"},{"id":"2412.12163","title":"Towards LLM-based optimization compilers. Can LLMs learn how to apply a single peephole optimization? Reasoning is all LLMs need!","created":"2024-12-11","cited_by_count":1,"area":"lr"},{"id":"2602.04145","title":"Training Data Efficiency in Multimodal Process Reward Models","created":"2026-02-04","cited_by_count":1,"area":"lr"},{"id":"2604.17957","title":"Process Reward Models Meet Planning: Generating Precise and Scalable Datasets for Step-Level Rewards","created":"2026-04-20","cited_by_count":1,"area":"lr"},{"id":"2605.15721","title":"Contexting as Recommendation: Evolutionary Collaborative Filtering for Context Engineering","created":"2026-05-15","cited_by_count":1,"area":null},{"id":"2605.30898","title":"UniScale: Adaptive Unified Inference Scaling via Online Joint Optimization of Model Routing and Test-Time Scaling","created":"2026-05-29","cited_by_count":1,"area":"lr"},{"id":"2606.31484","title":"Fork-Think with Confidence","created":"2026-06-30","cited_by_count":1,"area":"lr"},{"id":"2505.17829","title":"Stepwise Reasoning Checkpoint Analysis: A Test Time Scaling Method to Enhance LLMs' Reasoning","created":"2025-05-23","cited_by_count":0,"area":"lr"},{"id":"2602.06527","title":"HyPER: Bridging Exploration and Exploitation for Scalable LLM Reasoning with Hypothesis Path Expansion and Reduction","created":"2026-02-06","cited_by_count":0,"area":"lr"},{"id":"2602.14054","title":"LogitsCoder: Towards Efficient Chain-of-Thought Path Search via Logits Preference Decoding for Code Generation","created":"2026-02-15","cited_by_count":0,"area":"lr"},{"id":"2604.00438","title":"TR-ICRL: Test-Time Rethinking for In-Context Reinforcement Learning","created":"2026-04-01","cited_by_count":0,"area":"lr"},{"id":"2605.10195","title":"Breaking the Reward Barrier: Accelerating Tree-of-Thought Reasoning via Speculative Exploration","created":"2026-05-11","cited_by_count":0,"area":"lr"},{"id":"2605.15529","title":"Process Rewards with Learned Reliability","created":"2026-05-15","cited_by_count":0,"area":"lr"},{"id":"2606.06915","title":"ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning","created":"2026-06-05","cited_by_count":0,"area":"lr"},{"id":"2606.27739","title":"The Weakest Link Tells It All: Outcome-Supervised Process Reward Modeling via Learnable Credit Assignment","created":"2026-06-26","cited_by_count":0,"area":"lr"},{"id":"2607.23771","title":"Training Language Models to Cooperate with Inference-Time Controllers","created":"2026-07-26","cited_by_count":0,"area":null},{"id":"2608.10928","title":"ThinkRetrieve: Retrieval-Augmented Reasoning Traces for Test-Time Scaling","created":"2026-08-11","cited_by_count":0,"area":"lr"},{"id":"2608.15669","title":"Large Discovery Models: Empirically-grounded Model-Based Open-Ended Search","created":"2026-08-16","cited_by_count":0,"area":null},{"id":"2609.39334","title":"Taming Speculative Search for Test-Time Scaling in LLM Serving","created":"2026-09-30","cited_by_count":0,"area":"lr"}],"next_cursor":null}