【2025年2月公開 Arxiv論文ランキング】2502.xxxxx

# AI論文解説 Youtubeチャンネル: AI時代の羅針盤 

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2025年2月頃に公開されたcsカテゴリの論文 (ID: 2502.xxxxx)を被引用数のデータを元にランキングしています。ランキングは随時更新します。
(2025年6月15日更新)
   
| 被引用数　　|タイトル|動画|
|------:|:--------|:------------|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.13923-black.svg)](https://arxiv.org/abs/2502.13923)720|<I>Qwen2.5-VL Technical Report</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.03387-black.svg)](https://arxiv.org/abs/2502.03387)159|<I>LIMO: Less is More for Reasoning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.17419-black.svg)](https://arxiv.org/abs/2502.17419)119|<I>From System 1 to System 2: A Survey of Reasoning Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.03373-black.svg)](https://arxiv.org/abs/2502.03373)113|<I>Demystifying Long Chain-of-Thought Reasoning in LLMs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.01456-black.svg)](https://arxiv.org/abs/2502.01456)107|<I>Process Reinforcement through Implicit Rewards</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.14768-black.svg)](https://arxiv.org/abs/2502.14768)97|<I>Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.18600-black.svg)](https://arxiv.org/abs/2502.18600)80|<I>Chain of Draft: Thinking Faster by Writing Less</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/f6820ad3-b1b5-4a21-b33b-0869021f9e70.png">](https://youtu.be/AqCTfIbAF38)|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.14786-black.svg)](https://arxiv.org/abs/2502.14786)69|<I>SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.12067-black.svg)](https://arxiv.org/abs/2502.12067)67|<I>TokenSkip: Controllable Chain-of-Thought Compression in LLMs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.02737-black.svg)](https://arxiv.org/abs/2502.02737)67|<I>SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.04463-black.svg)](https://arxiv.org/abs/2502.04463)64|<I>Training Language Models to Reason Efficiently</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.11089-black.svg)](https://arxiv.org/abs/2502.11089)59|<I>Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/b2fda5cc-46ea-4549-8b30-7dc15c27947f.png">](https://youtu.be/C2wYoOjFi2o)|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.18864-black.svg)](https://arxiv.org/abs/2502.18864)57|<I>Towards an AI co-scientist</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.21321-black.svg)](https://arxiv.org/abs/2502.21321)52|<I>LLM Post-Training: A Deep Dive into Reasoning Large Language Models</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/c43ef7ed-6600-4364-b700-348f14bd71a0.png">](https://youtu.be/7UMAVevmYCg)|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.07374-black.svg)](https://arxiv.org/abs/2502.07374)52|<I>LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/77a968cc-94a7-4f6a-afd7-75e3e5f29acc.png">](https://youtu.be/XwaE1ohstGw)|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.07266-black.svg)](https://arxiv.org/abs/2502.07266)52|<I>When More is Less: Understanding Chain-of-Thought Length in LLMs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.09992-black.svg)](https://arxiv.org/abs/2502.09992)50|<I>Large Language Diffusion Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.18449-black.svg)](https://arxiv.org/abs/2502.18449)47|<I>SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/2bf5731b-ac2a-4b4c-b54e-fe4f5fa45c9d.png">](https://youtu.be/fXtClBeAp38)|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.06807-black.svg)](https://arxiv.org/abs/2502.06807)47|<I>Competitive Programming with Large Reasoning Models</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/1124e5cd-9bdb-447d-8e2e-fb6ce321b276.png">](https://youtu.be/n9NPh_3GoHg)|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.05171-black.svg)](https://arxiv.org/abs/2502.05171)46|<I>Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.18080-black.svg)](https://arxiv.org/abs/2502.18080)45|<I>Towards Thinking-Optimal Scaling of Test-Time Compute for LLM Reasoning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.12524-black.svg)](https://arxiv.org/abs/2502.12524)45|<I>YOLOv12: Attention-Centric Real-Time Object Detectors</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.06703-black.svg)](https://arxiv.org/abs/2502.06703)44|<I>Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.19634-black.svg)](https://arxiv.org/abs/2502.19634)43|<I>MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language Models (VLMs) via Reinforcement Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.09601-black.svg)](https://arxiv.org/abs/2502.09601)43|<I>CoT-Valve: Length-Compressible Chain-of-Thought Tuning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.08235-black.svg)](https://arxiv.org/abs/2502.08235)42|<I>The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.20122-black.svg)](https://arxiv.org/abs/2502.20122)40|<I>Self-Training Elicits Concise Reasoning in Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.11886-black.svg)](https://arxiv.org/abs/2502.11886)39|<I>LIMR: Less is More for RL Scaling</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.10248-black.svg)](https://arxiv.org/abs/2502.10248)35|<I>Step-Video-T2V Technical Report: The Practice, Challenges, and Future of Video Foundation Model</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.09621-black.svg)](https://arxiv.org/abs/2502.09621)34|<I>MME-CoT: Benchmarking Chain-of-Thought in Large Multimodal Models for Reasoning Quality, Robustness, and Efficiency</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.13189-black.svg)](https://arxiv.org/abs/2502.13189)33|<I>MoBA: Mixture of Block Attention for Long-Context LLMs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.12025-black.svg)](https://arxiv.org/abs/2502.12025)33|<I>SafeChain: Safety of Language Models with Long Chain-of-Thought Reasoning Capabilities</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.19645-black.svg)](https://arxiv.org/abs/2502.19645)31|<I>Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.15589-black.svg)](https://arxiv.org/abs/2502.15589)31|<I>LightThinker: Thinking Step-by-Step Compression</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.14739-black.svg)](https://arxiv.org/abs/2502.14739)31|<I>SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.12659-black.svg)](https://arxiv.org/abs/2502.12659)31|<I>The Hidden Risks of Large Reasoning Models: A Safety Assessment of R1</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.12143-black.svg)](https://arxiv.org/abs/2502.12143)30|<I>Small Models Struggle to Learn from Strong Reasoners</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.14296-black.svg)](https://arxiv.org/abs/2502.14296)28|<I>On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.14143-black.svg)](https://arxiv.org/abs/2502.14143)28|<I>Multi-Agent Risks from Advanced AI</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.07640-black.svg)](https://arxiv.org/abs/2502.07640)28|<I>Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.21074-black.svg)](https://arxiv.org/abs/2502.21074)27|<I>CODI: Compressing Chain-of-Thought into Continuous Space via Self-Distillation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.01534-black.svg)](https://arxiv.org/abs/2502.01534)27|<I>Preference Leakage: A Contamination Problem in LLM-as-a-judge</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.16982-black.svg)](https://arxiv.org/abs/2502.16982)26|<I>Muon is Scalable for LLM Training</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.01776-black.svg)](https://arxiv.org/abs/2502.01776)26|<I>Sparse VideoGen: Accelerating Video Diffusion Transformers with Spatial-Temporal Sparsity</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.12118-black.svg)](https://arxiv.org/abs/2502.12118)25|<I>Scaling Test-Time Compute Without Verification or RL is Suboptimal</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.04644-black.svg)](https://arxiv.org/abs/2502.04644)25|<I>Agentic Reasoning: Reasoning LLMs with Tools for the Deep Research</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/9bcb1c1e-fbcf-403a-b95f-a29b09d395df.png">](https://youtu.be/yVJHZv-21-s)|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.12893-black.svg)](https://arxiv.org/abs/2502.12893)24|<I>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</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.05855-black.svg)](https://arxiv.org/abs/2502.05855)24|<I>DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General Robot Control</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.17424-black.svg)](https://arxiv.org/abs/2502.17424)23|<I>Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.12215-black.svg)](https://arxiv.org/abs/2502.12215)23|<I>Revisiting the Test-Time Scaling of o1-like Models: Do they Truly Possess Test-Time Scaling Capabilities?</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.12134-black.svg)](https://arxiv.org/abs/2502.12134)23|<I>SoftCoT: Soft Chain-of-Thought for Efficient Reasoning with LLMs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.12110-black.svg)](https://arxiv.org/abs/2502.12110)23|<I>A-MEM: Agentic Memory for LLM Agents</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.09560-black.svg)](https://arxiv.org/abs/2502.09560)23|<I>EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.08691-black.svg)](https://arxiv.org/abs/2502.08691)23|<I>AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.04347-black.svg)](https://arxiv.org/abs/2502.04347)23|<I>SCALM: Detecting Bad Practices in Smart Contracts Through LLMs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.01143-black.svg)](https://arxiv.org/abs/2502.01143)23|<I>ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.01061-black.svg)](https://arxiv.org/abs/2502.01061)22|<I>OmniHuman-1: Rethinking the Scaling-Up of One-Stage Conditioned Human Animation Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.13260-black.svg)](https://arxiv.org/abs/2502.13260)21|<I>Stepwise Perplexity-Guided Refinement for Efficient Chain-of-Thought Reasoning in Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.02871-black.svg)](https://arxiv.org/abs/2502.02871)21|<I>Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.02542-black.svg)](https://arxiv.org/abs/2502.02542)21|<I>OverThink: Slowdown Attacks on Reasoning LLMs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.01100-black.svg)](https://arxiv.org/abs/2502.01100)21|<I>ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.13595-black.svg)](https://arxiv.org/abs/2502.13595)20|<I>MMTEB: Massive Multilingual Text Embedding Benchmark</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.01718-black.svg)](https://arxiv.org/abs/2502.01718)20|<I>ACECODER: Acing Coder RL via Automated Test-Case Synthesis</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.19417-black.svg)](https://arxiv.org/abs/2502.19417)19|<I>Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.18036-black.svg)](https://arxiv.org/abs/2502.18036)19|<I>Harnessing Multiple Large Language Models: A Survey on LLM Ensemble</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.15657-black.svg)](https://arxiv.org/abs/2502.15657)19|<I>Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path?</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.06764-black.svg)](https://arxiv.org/abs/2502.06764)19|<I>History-Guided Video Diffusion</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.05917-black.svg)](https://arxiv.org/abs/2502.05917)19|<I>Modeling and Beamforming Optimization for Pinching-Antenna Systems</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.03671-black.svg)](https://arxiv.org/abs/2502.03671)19|<I>Advancing Reasoning in Large Language Models: Promising Methods and Approaches</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.02013-black.svg)](https://arxiv.org/abs/2502.02013)19|<I>Layer by Layer: Uncovering Hidden Representations in Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.17387-black.svg)](https://arxiv.org/abs/2502.17387)18|<I>Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.14499-black.svg)](https://arxiv.org/abs/2502.14499)18|<I>MLGym: A New Framework and Benchmark for Advancing AI Research Agents</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.14382-black.svg)](https://arxiv.org/abs/2502.14382)18|<I>S*: Test Time Scaling for Code Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.13130-black.svg)](https://arxiv.org/abs/2502.13130)18|<I>Magma: A Foundation Model for Multimodal AI Agents</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/ac89c21d-a19f-48d9-9a77-16e12ce6d7b4.png">](https://youtu.be/L62o0j4o6M0)|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.12115-black.svg)](https://arxiv.org/abs/2502.12115)18|<I>SWE-Lancer: Can Frontier LLMs Earn $1 Million from Real-World Freelance Software Engineering?</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/f6673fda-fd74-43a5-b48f-80fd33533552.png">](https://youtu.be/e4GwOHTPZVY)|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.12018-black.svg)](https://arxiv.org/abs/2502.12018)18|<I>Atom of Thoughts for Markov LLM Test-Time Scaling</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.10391-black.svg)](https://arxiv.org/abs/2502.10391)18|<I>MM-RLHF: The Next Step Forward in Multimodal LLM Alignment</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.09533-black.svg)](https://arxiv.org/abs/2502.09533)18|<I>Long-Term TalkingFace Generation via Motion-Prior Conditional Diffusion Model</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.06772-black.svg)](https://arxiv.org/abs/2502.06772)18|<I>ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.06608-black.svg)](https://arxiv.org/abs/2502.06608)18|<I>TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.04180-black.svg)](https://arxiv.org/abs/2502.04180)18|<I>Multi-agent Architecture Search via Agentic Supernet</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.03544-black.svg)](https://arxiv.org/abs/2502.03544)18|<I>Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.03275-black.svg)](https://arxiv.org/abs/2502.03275)18|<I>Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.19187-black.svg)](https://arxiv.org/abs/2502.19187)17|<I>BIG-Bench Extra Hard</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.17416-black.svg)](https://arxiv.org/abs/2502.17416)17|<I>Reasoning with Latent Thoughts: On the Power of Looped Transformers</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.13124-black.svg)](https://arxiv.org/abs/2502.13124)17|<I>NaturalReasoning: Reasoning in the Wild with 2.8M Challenging Questions</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.12147-black.svg)](https://arxiv.org/abs/2502.12147)17|<I>Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.02508-black.svg)](https://arxiv.org/abs/2502.02508)17|<I>Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.11946-black.svg)](https://arxiv.org/abs/2502.11946)16|<I>Step-Audio: Unified Understanding and Generation in Intelligent Speech Interaction</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.06453-black.svg)](https://arxiv.org/abs/2502.06453)16|<I>MATH-Perturb: Benchmarking LLMs&#39; Math Reasoning Abilities against Hard Perturbations</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.05139-black.svg)](https://arxiv.org/abs/2502.05139)16|<I>Meta Audiobox Aesthetics: Unified Automatic Quality Assessment for Speech, Music, and Sound</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.04896-black.svg)](https://arxiv.org/abs/2502.04896)16|<I>Goku: Flow Based Video Generative Foundation Models</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/61886b2f-bf59-4532-a6c5-cf0031db4897.png">](https://youtu.be/ZeVRiELcC34)|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.01142-black.svg)](https://arxiv.org/abs/2502.01142)16|<I>DeepRAG: Thinking to Retrieve Step by Step for Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.00212-black.svg)](https://arxiv.org/abs/2502.00212)16|<I>STP: Self-play LLM Theorem Provers with Iterative Conjecturing and Proving</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2502.19613-black.svg)](https://arxiv.org/abs/2502.19613)15|<I>Self-rewarding correction for mathematical reasoning</I>|not yet|
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※ 被引用数は更新日における NASA ADSのデータを参照しています
https://ui.adsabs.harvard.edu/