{"total":430,"rows":[{"id":"2412.05271","title":"Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling","created":"2024-12-06","cited_by_count":1599,"area":"lr"},{"id":"2508.18265","title":"InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency","created":"2025-08-25","cited_by_count":1510,"area":"lr"},{"id":"2501.12599","title":"Kimi k1.5: Scaling Reinforcement Learning with LLMs","created":"2025-01-22","cited_by_count":1210,"area":"lr"},{"id":"2503.20215","title":"Qwen2.5-Omni Technical Report","created":"2025-03-26","cited_by_count":736,"area":"lr"},{"id":"2501.17161","title":"SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training","created":"2025-01-28","cited_by_count":672,"area":"lr"},{"id":"2412.14171","title":"Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces","created":"2024-12-18","cited_by_count":611,"area":"lr"},{"id":"2507.01006","title":"GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning","created":"2025-07-01","cited_by_count":466,"area":"lr"},{"id":"2504.07491","title":"Kimi-VL Technical Report","created":"2025-04-10","cited_by_count":388,"area":"lr"},{"id":"2504.08837","title":"VL-Rethinker: Incentivizing Self-Reflection of Vision-Language Models with Reinforcement Learning","created":"2025-04-10","cited_by_count":323,"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":"2410.19168","title":"MMAU: A Massive Multi-Task Audio Understanding and Reasoning Benchmark","created":"2024-10-24","cited_by_count":288,"area":"lr"},{"id":"2407.01284","title":"We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?","created":"2024-07-01","cited_by_count":286,"area":"lr"},{"id":"2505.15966","title":"Pixel Reasoner: Incentivizing Pixel-Space Reasoning with Curiosity-Driven Reinforcement Learning","created":"2025-05-21","cited_by_count":278,"area":"lr"},{"id":"2411.10442","title":"Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization","created":"2024-11-15","cited_by_count":239,"area":"lr"},{"id":"2506.07044","title":"Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning","created":"2025-06-08","cited_by_count":230,"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":"2503.12605","title":"Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey","created":"2025-03-16","cited_by_count":182,"area":"lr"},{"id":"2411.00836","title":"DynaMath: A Dynamic Visual Benchmark for Evaluating Mathematical Reasoning Robustness of Vision Language Models","created":"2024-10-29","cited_by_count":146,"area":"lr"},{"id":"2405.03548","title":"MAmmoTH2: Scaling Instructions from the Web","created":"2024-05-06","cited_by_count":145,"area":"lr"},{"id":"2504.07934","title":"SoTA with Less: MCTS-Guided Sample Selection for Data-Efficient Visual Reasoning Self-Improvement","created":"2025-04-10","cited_by_count":145,"area":"lr"},{"id":"2503.10291","title":"VisualPRM: An Effective Process Reward Model for Multimodal Reasoning","created":"2025-03-13","cited_by_count":139,"area":"lr"},{"id":"2410.16198","title":"Improve Vision Language Model Chain-of-thought Reasoning","created":"2024-10-21","cited_by_count":136,"area":"lr"},{"id":"2502.21321","title":"LLM Post-Training: A Deep Dive into Reasoning Large Language Models","created":"2025-02-28","cited_by_count":135,"area":"lr"},{"id":"2406.17294","title":"Math-LLaVA: Bootstrapping Mathematical Reasoning for Multimodal Large Language Models","created":"2024-06-25","cited_by_count":133,"area":"lr"},{"id":"2406.09961","title":"ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation","created":"2024-06-14","cited_by_count":129,"area":"lr"},{"id":"2412.05237","title":"MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale","created":"2024-12-06","cited_by_count":127,"area":"lr"},{"id":"2405.16473","title":"M$^3$CoT: A Novel Benchmark for Multi-Domain Multi-step Multi-modal Chain-of-Thought","created":"2024-05-26","cited_by_count":121,"area":"lr"},{"id":"2411.14432","title":"Insight-V: Exploring Long-Chain Visual Reasoning with Multimodal Large Language Models","created":"2024-11-21","cited_by_count":116,"area":"lr"},{"id":"2506.03569","title":"MiMo-VL Technical Report","created":"2025-06-04","cited_by_count":115,"area":"lr"},{"id":"2506.03135","title":"OmniSpatial: Towards Comprehensive Spatial Reasoning Benchmark for Vision Language Models","created":"2025-06-03","cited_by_count":114,"area":"lr"},{"id":"2407.08739","title":"MAVIS: Mathematical Visual Instruction Tuning with an Automatic Data Engine","created":"2024-07-11","cited_by_count":113,"area":"lr"},{"id":"2505.17862","title":"Daily-Omni: Towards Audio-Visual Reasoning with Temporal Alignment across Modalities","created":"2025-05-23","cited_by_count":110,"area":"lr"},{"id":"2508.11737","title":"Ovis2.5 Technical Report","created":"2025-08-15","cited_by_count":108,"area":"lr"},{"id":"2406.11776","title":"Improving Multi-Agent Debate with Sparse Communication Topology","created":"2024-06-17","cited_by_count":107,"area":"lr"},{"id":"2504.15279","title":"VisuLogic: A Benchmark for Evaluating Visual Reasoning in Multi-modal Large Language Models","created":"2025-04-21","cited_by_count":107,"area":"lr"},{"id":"2401.10529","title":"Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences","created":"2024-01-19","cited_by_count":94,"area":"lr"},{"id":"2505.21523","title":"More Thinking, Less Seeing? Assessing Amplified Hallucination in Multimodal Reasoning Models","created":"2025-05-23","cited_by_count":86,"area":"lr"},{"id":"2505.11409","title":"Visual Planning: Let's Think Only with Images","created":"2025-05-16","cited_by_count":85,"area":"lr"},{"id":"2505.16839","title":"LaViDa: A Large Diffusion Language Model for Multimodal Understanding","created":"2025-05-22","cited_by_count":84,"area":"lr"},{"id":"2501.12368","title":"InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Model","created":"2025-01-21","cited_by_count":74,"area":"lr"},{"id":"2507.17520","title":"InstructVLA: Vision-Language-Action Instruction Tuning from Understanding to Manipulation","created":"2025-07-23","cited_by_count":72,"area":"lr"},{"id":"2406.12753","title":"OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI","created":"2024-06-18","cited_by_count":71,"area":"lr"},{"id":"2412.10400","title":"Reinforcement Learning Enhanced LLMs: A Survey","created":"2024-12-05","cited_by_count":71,"area":"lr"},{"id":"2402.11451","title":"SciAgent: Tool-augmented Language Models for Scientific Reasoning","created":"2024-02-18","cited_by_count":70,"area":"lr"},{"id":"2312.06722","title":"EgoPlan-Bench: Benchmarking Multimodal Large Language Models for Human-Level Planning","created":"2023-12-11","cited_by_count":69,"area":"lr"},{"id":"2411.00855","title":"Vision-Language Models Can Self-Improve Reasoning via Reflection","created":"2024-10-30","cited_by_count":69,"area":"lr"},{"id":"2504.05599","title":"Skywork R1V: Pioneering Multimodal Reasoning with Chain-of-Thought","created":"2025-04-08","cited_by_count":69,"area":"lr"},{"id":"2506.08343","title":"Wait, We Don't Need to \"Wait\"! Removing Thinking Tokens Improves Reasoning Efficiency","created":"2025-06-10","cited_by_count":66,"area":"lr"},{"id":"2503.23077","title":"Efficient Inference for Large Reasoning Models: A Survey","created":"2025-03-29","cited_by_count":61,"area":"lr"},{"id":"2506.10910","title":"Magistral","created":"2025-06-12","cited_by_count":60,"area":"lr"},{"id":"2510.10689","title":"OmniVideoBench: Towards Audio-Visual Understanding Evaluation for Omni MLLMs","created":"2025-10-12","cited_by_count":59,"area":"lr"},{"id":"2403.13315","title":"PuzzleVQA: Diagnosing Multimodal Reasoning Challenges of Language Models with Abstract Visual Patterns","created":"2024-03-20","cited_by_count":57,"area":"lr"},{"id":"2508.05606","title":"Uni-cot: Towards Unified Chain-of-Thought Reasoning Across Text and Vision","created":"2025-08-07","cited_by_count":56,"area":"lr"},{"id":"2512.03043","title":"OneThinker: All-in-one Reasoning Model for Image and Video","created":"2025-12-02","cited_by_count":56,"area":"lr"},{"id":"2412.11936","title":"A Survey of Mathematical Reasoning in the Era of Multimodal Large Language Model: Benchmark, Method & Challenges","created":"2024-12-16","cited_by_count":55,"area":"lr"},{"id":"2506.04207","title":"Advancing Multimodal Reasoning: From Optimized Cold Start to Staged Reinforcement Learning","created":"2025-06-04","cited_by_count":53,"area":"lr"},{"id":"2505.16673","title":"R1-ShareVL: Incentivizing Reasoning Capability of Multimodal Large Language Models via Share-GRPO","created":"2025-05-22","cited_by_count":52,"area":"lr"},{"id":"2505.12081","title":"VisionReasoner: Unified Reasoning-Integrated Visual Perception via Reinforcement Learning","created":"2025-05-17","cited_by_count":51,"area":"lr"},{"id":"2503.19990","title":"LEGO-Puzzles: How Good Are MLLMs at Multi-Step Spatial Reasoning?","created":"2025-03-25","cited_by_count":49,"area":"lr"},{"id":"2504.16656","title":"Skywork R1V2: Multimodal Hybrid Reinforcement Learning for Reasoning","created":"2025-04-23","cited_by_count":49,"area":"lr"},{"id":"2511.15661","title":"VisPlay: Self-Evolving Vision-Language Models from Images","created":"2025-11-19","cited_by_count":47,"area":"lr"},{"id":"2408.15769","title":"A Survey on Evaluation of Multimodal Large Language Models","created":"2024-08-28","cited_by_count":46,"area":"lr"},{"id":"2508.02095","title":"VLM4D: Towards Spatiotemporal Awareness in Vision Language Models","created":"2025-08-04","cited_by_count":46,"area":"lr"},{"id":"2506.07218","title":"Perception-R1: Advancing Multimodal Reasoning Capabilities of MLLMs via Visual Perception Reward","created":"2025-06-08","cited_by_count":45,"area":"lr"},{"id":"2504.21277","title":"Reinforced MLLM: A Survey on RL-Based Reasoning in Multimodal Large Language Models","created":"2025-04-30","cited_by_count":44,"area":"lr"},{"id":"2606.13392","title":"MiniMax Sparse Attention","created":"2026-06-11","cited_by_count":44,"area":"lr"},{"id":"2507.13348","title":"VisionThink: Smart and Efficient Vision Language Model via Reinforcement Learning","created":"2025-07-17","cited_by_count":43,"area":"lr"},{"id":"2510.26802","title":"Are Video Models Ready as Zero-Shot Reasoners? An Empirical Study with the MME-CoF Benchmark","created":"2025-10-30","cited_by_count":43,"area":"lr"},{"id":"2511.04570","title":"Thinking with Video: Video Generation as a Promising Multimodal Reasoning Paradigm","created":"2025-11-06","cited_by_count":42,"area":"lr"},{"id":"2412.04447","title":"EgoPlan-Bench2: A Benchmark for Multimodal Large Language Model Planning in Real-World Scenarios","created":"2024-12-05","cited_by_count":41,"area":"lr"},{"id":"2506.00555","title":"MMedAgent-RL: Optimizing Multi-Agent Collaboration for Multimodal Medical Reasoning","created":"2025-05-31","cited_by_count":40,"area":"lr"},{"id":"2412.01694","title":"Enhancing Video-LLM Reasoning via Agent-of-Thoughts Distillation","created":"2024-12-02","cited_by_count":37,"area":"lr"},{"id":"2511.16334","title":"OpenMMReasoner: Pushing the Frontiers for Multimodal Reasoning with an Open and General Recipe","created":"2025-11-20","cited_by_count":37,"area":"lr"},{"id":"2511.01833","title":"TIR-Bench: A Comprehensive Benchmark for Agentic Thinking-with-Images Reasoning","created":"2025-11-03","cited_by_count":36,"area":"lr"},{"id":"2404.05091","title":"MM-MATH: Advancing Multimodal Math Evaluation with Process Evaluation and Fine-grained Classification","created":"2024-04-07","cited_by_count":35,"area":"lr"},{"id":"2510.14958","title":"MathCanvas: Intrinsic Visual Chain-of-Thought for Multimodal Mathematical Reasoning","created":"2025-10-16","cited_by_count":35,"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":"2507.05255","title":"Open Vision Reasoner: Transferring Linguistic Cognitive Behavior for Visual Reasoning","created":"2025-07-07","cited_by_count":34,"area":"lr"},{"id":"2510.23473","title":"Video-Thinker: Sparking \"Thinking with Videos\" via Reinforcement Learning","created":"2025-10-27","cited_by_count":34,"area":"lr"},{"id":"2502.02871","title":"Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning","created":"2025-02-05","cited_by_count":33,"area":"lr"},{"id":"2506.00123","title":"Visual Embodied Brain: Let Multimodal Large Language Models See, Think, and Control in Spaces","created":"2025-05-30","cited_by_count":33,"area":"lr"},{"id":"2502.20808","title":"MV-MATH: Evaluating Multimodal Math Reasoning in Multi-Visual Contexts","created":"2025-02-28","cited_by_count":32,"area":"lr"},{"id":"2506.21656","title":"Fine-Grained Preference Optimization Improves Spatial Reasoning in VLMs","created":"2025-06-26","cited_by_count":32,"area":"lr"},{"id":"2508.12587","title":"Multimodal Chain of Continuous Thought for Latent-Space Reasoning in Vision-Language Models","created":"2025-08-18","cited_by_count":32,"area":"lr"},{"id":"2511.22018","title":"MedEyes: Learning Dynamic Visual Focus for Medical Progressive Diagnosis","created":"2025-11-27","cited_by_count":32,"area":"lr"},{"id":"2401.14011","title":"CMMU: A Benchmark for Chinese Multi-modal Multi-type Question Understanding and Reasoning","created":"2024-01-25","cited_by_count":31,"area":"lr"},{"id":"2511.19900","title":"Agent0-VL: Exploring Self-Evolving Agent for Tool-Integrated Vision-Language Reasoning","created":"2025-11-25","cited_by_count":31,"area":"lr"},{"id":"2406.09175","title":"ReMI: A Dataset for Reasoning with Multiple Images","created":"2024-06-13","cited_by_count":30,"area":"lr"},{"id":"2412.12932","title":"CoMT: A Novel Benchmark for Chain of Multi-modal Thought on Large Vision-Language Models","created":"2024-12-17","cited_by_count":30,"area":"lr"},{"id":"2505.13031","title":"MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO","created":"2025-05-19","cited_by_count":30,"area":"lr"},{"id":"2505.18129","title":"One RL to See Them All: Visual Triple Unified Reinforcement Learning","created":"2025-05-23","cited_by_count":30,"area":"lr"},{"id":"2403.04732","title":"How Far Are We from Intelligent Visual Deductive Reasoning?","created":"2024-03-07","cited_by_count":29,"area":"lr"},{"id":"2411.13112","title":"SURDS: Benchmarking Spatial Understanding and Reasoning in Driving Scenarios with Vision Language Models","created":"2024-11-20","cited_by_count":29,"area":"lr"},{"id":"2406.19237","title":"FlowVQA: Mapping Multimodal Logic in Visual Question Answering with Flowcharts","created":"2024-06-27","cited_by_count":28,"area":"lr"},{"id":"2505.15692","title":"TemplateRL: Structured Template-Guided Reinforcement Learning for LLM Reasoning","created":"2025-05-21","cited_by_count":28,"area":"lr"},{"id":"2508.17298","title":"Explain Before You Answer: A Survey on Compositional Visual Reasoning","created":"2025-08-24","cited_by_count":28,"area":"lr"},{"id":"2509.25848","title":"More Thought, Less Accuracy? On the Dual Nature of Reasoning in Vision-Language Models","created":"2025-09-30","cited_by_count":27,"area":"lr"},{"id":"2502.02339","title":"AStar: Boosting Multimodal Reasoning with Automated Structured Thinking","created":"2025-02-04","cited_by_count":25,"area":"lr"},{"id":"2504.12597","title":"GeoSense: Evaluating Identification and Application of Geometric Principles in Multimodal Reasoning","created":"2025-04-17","cited_by_count":25,"area":"lr"},{"id":"2505.17163","title":"OCR-Reasoning Benchmark: Unveiling the True Capabilities of MLLMs in Complex Text-Rich Image Reasoning","created":"2025-05-22","cited_by_count":25,"area":"lr"}],"next_cursor":"100"}