【2025年3月公開 Arxiv論文ランキング】2503.xxxxx

# AI論文解説 Youtubeチャンネル: AI時代の羅針盤 

https://www.youtube.com/@compassinai

2025年3月頃に公開されたcsカテゴリの論文 (ID: 2503.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月6月14日更新)
   
| 被引用数　　|タイトル|動画|
|------:|:--------|:------------|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.14476-black.svg)](https://arxiv.org/abs/2503.14476)225|<I>DAPO: An Open-Source LLM Reinforcement Learning System at Scale</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/6f2ad773-9032-4c29-aedd-ccc6945f85f5.png">](https://youtu.be/bF61qo6LdT8)|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.19786-black.svg)](https://arxiv.org/abs/2503.19786)190|<I>Gemma 3 Technical Report</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/ddca15c2-1764-4e26-bc81-9506ba486e4b.png">](https://youtu.be/PLxox9j8ly8)|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.20783-black.svg)](https://arxiv.org/abs/2503.20783)142|<I>Understanding R1-Zero-Like Training: A Critical Perspective</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/63fa8569-1484-4675-947d-bb28a6f2fbe1.png">](https://youtu.be/Ya8ImBGcvQ0)|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.06749-black.svg)](https://arxiv.org/abs/2503.06749)123|<I>Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.09567-black.svg)](https://arxiv.org/abs/2503.09567)120|<I>Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.01785-black.svg)](https://arxiv.org/abs/2503.01785)117|<I>Visual-RFT: Visual Reinforcement Fine-Tuning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.20314-black.svg)](https://arxiv.org/abs/2503.20314)116|<I>Wan: Open and Advanced Large-Scale Video Generative Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.16419-black.svg)](https://arxiv.org/abs/2503.16419)113|<I>Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.18892-black.svg)](https://arxiv.org/abs/2503.18892)109|<I>SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.09516-black.svg)](https://arxiv.org/abs/2503.09516)107|<I>Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.04697-black.svg)](https://arxiv.org/abs/2503.04697)97|<I>L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.07365-black.svg)](https://arxiv.org/abs/2503.07365)91|<I>MM-Eureka: Exploring the Frontiers of Multimodal Reasoning with Rule-based Reinforcement Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.01307-black.svg)](https://arxiv.org/abs/2503.01307)89|<I>Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/a636fddc-f18c-4eb2-a2be-fd2c0f44c2c0.png">](https://youtu.be/UK2c1v3hYUE)|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.10615-black.svg)](https://arxiv.org/abs/2503.10615)82|<I>R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.24290-black.svg)](https://arxiv.org/abs/2503.24290)77|<I>Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.12937-black.svg)](https://arxiv.org/abs/2503.12937)64|<I>R1-VL: Learning to Reason with Multimodal Large Language Models via Step-wise Group Relative Policy Optimization</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.07536-black.svg)](https://arxiv.org/abs/2503.07536)64|<I>LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RL</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.05592-black.svg)](https://arxiv.org/abs/2503.05592)64|<I>R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.10460-black.svg)](https://arxiv.org/abs/2503.10460)61|<I>Light-R1: Curriculum SFT, DPO and RL for Long COT from Scratch and Beyond</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.20215-black.svg)](https://arxiv.org/abs/2503.20215)59|<I>Qwen2.5-Omni Technical Report</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.05132-black.svg)](https://arxiv.org/abs/2503.05132)58|<I>R1-Zero&#39;s &#34;Aha Moment&#34; in Visual Reasoning on a 2B Non-SFT Model</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.01743-black.svg)](https://arxiv.org/abs/2503.01743)57|<I>Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.14734-black.svg)](https://arxiv.org/abs/2503.14734)54|<I>GR00T N1: An Open Foundation Model for Generalist Humanoid Robots</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.04472-black.svg)](https://arxiv.org/abs/2503.04472)49|<I>DAST: Difficulty-Adaptive Slow-Thinking for Large Reasoning Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.21776-black.svg)](https://arxiv.org/abs/2503.21776)47|<I>Video-R1: Reinforcing Video Reasoning in MLLMs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.21614-black.svg)](https://arxiv.org/abs/2503.21614)45|<I>A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.19470-black.svg)](https://arxiv.org/abs/2503.19470)43|<I>ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.24235-black.svg)](https://arxiv.org/abs/2503.24235)42|<I>A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well?</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.07572-black.svg)](https://arxiv.org/abs/2503.07572)42|<I>Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.17352-black.svg)](https://arxiv.org/abs/2503.17352)38|<I>OpenVLThinker: An Early Exploration to Complex Vision-Language Reasoning via Iterative Self-Improvement</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.06520-black.svg)](https://arxiv.org/abs/2503.06520)38|<I>Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.13657-black.svg)](https://arxiv.org/abs/2503.13657)36|<I>Why Do Multi-Agent LLM Systems Fail?</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/9867138c-0a51-491c-a8f8-7c06bba62a2b.png">](https://youtu.be/Wsw01doEXhI)|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.10291-black.svg)](https://arxiv.org/abs/2503.10291)36|<I>VisualPRM: An Effective Process Reward Model for Multimodal Reasoning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.20020-black.svg)](https://arxiv.org/abs/2503.20020)35|<I>Gemini Robotics: Bringing AI into the Physical World</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.06669-black.svg)](https://arxiv.org/abs/2503.06669)32|<I>AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.05179-black.svg)](https://arxiv.org/abs/2503.05179)32|<I>Sketch-of-Thought: Efficient LLM Reasoning with Adaptive Cognitive-Inspired Sketching</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.04548-black.svg)](https://arxiv.org/abs/2503.04548)32|<I>An Empirical Study on Eliciting and Improving R1-like Reasoning Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.23278-black.svg)](https://arxiv.org/abs/2503.23278)30|<I>Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.20752-black.svg)](https://arxiv.org/abs/2503.20752)30|<I>Reason-RFT: Reinforcement Fine-Tuning for Visual Reasoning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.13939-black.svg)](https://arxiv.org/abs/2503.13939)30|<I>Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.21620-black.svg)](https://arxiv.org/abs/2503.21620)29|<I>UI-R1: Enhancing Efficient Action Prediction of GUI Agents by Reinforcement Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.21460-black.svg)](https://arxiv.org/abs/2503.21460)29|<I>Large Language Model Agent: A Survey on Methodology, Applications and Challenges</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.12605-black.svg)](https://arxiv.org/abs/2503.12605)29|<I>Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.01141-black.svg)](https://arxiv.org/abs/2503.01141)29|<I>How Well do LLMs Compress Their Own Chain-of-Thought? A Token Complexity Approach</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.06072-black.svg)](https://arxiv.org/abs/2503.06072)27|<I>Large Language Models Post-training: Surveying Techniques from Alignment to Reasoning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.11926-black.svg)](https://arxiv.org/abs/2503.11926)26|<I>Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/5e761331-4ef8-46d6-bac0-6cdefee5a04e.png">](https://youtu.be/wJybDdgGlmU)|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.07265-black.svg)](https://arxiv.org/abs/2503.07265)23|<I>WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.23383-black.svg)](https://arxiv.org/abs/2503.23383)22|<I>ToRL: Scaling Tool-Integrated RL</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.01491-black.svg)](https://arxiv.org/abs/2503.01491)22|<I>What&#39;s Behind PPO&#39;s Collapse in Long-CoT? Value Optimization Holds the Secret</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.04761-black.svg)](https://arxiv.org/abs/2503.04761)21|<I>Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.22020-black.svg)](https://arxiv.org/abs/2503.22020)20|<I>CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.21934-black.svg)](https://arxiv.org/abs/2503.21934)20|<I>Proof or Bluff? Evaluating LLMs on 2025 USA Math Olympiad</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/0be4b6f6-2e48-4c98-936f-9c2dfc6e1c39.png">](https://youtu.be/i_bbqU9Sfkc)|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.19983-black.svg)](https://arxiv.org/abs/2503.19983)20|<I>A Linear Collider Vision for the Future of Particle Physics</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.10639-black.svg)](https://arxiv.org/abs/2503.10639)20|<I>GoT: Unleashing Reasoning Capability of Multimodal Large Language Model for Visual Generation and Editing</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.07598-black.svg)](https://arxiv.org/abs/2503.07598)20|<I>VACE: All-in-One Video Creation and Editing</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.07065-black.svg)](https://arxiv.org/abs/2503.07065)20|<I>Boosting the Generalization and Reasoning of Vision Language Models with Curriculum Reinforcement Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.23829-black.svg)](https://arxiv.org/abs/2503.23829)19|<I>Crossing the Reward Bridge: Expanding RL with Verifiable Rewards Across Diverse Domains</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.22342-black.svg)](https://arxiv.org/abs/2503.22342)19|<I>CPPO: Accelerating the Training of Group Relative Policy Optimization-Based Reasoning Models</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/ca8c57cf-e8ec-4f77-9e89-9c8bb172e6fe.png">](https://youtu.be/wQo8H6l6V48)|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.22156-black.svg)](https://arxiv.org/abs/2503.22156)19|<I>SoK: Security Analysis of Blockchain-based Cryptocurrency</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.15478-black.svg)](https://arxiv.org/abs/2503.15478)19|<I>SWEET-RL: Training Multi-Turn LLM Agents on Collaborative Reasoning Tasks</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.14456-black.svg)](https://arxiv.org/abs/2503.14456)19|<I>RWKV-7 &#34;Goose&#34; with Expressive Dynamic State Evolution</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.00031-black.svg)](https://arxiv.org/abs/2503.00031)19|<I>Efficient Test-Time Scaling via Self-Calibration</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.16219-black.svg)](https://arxiv.org/abs/2503.16219)18|<I>Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn&#39;t</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.14499-black.svg)](https://arxiv.org/abs/2503.14499)18|<I>Measuring AI Ability to Complete Long Tasks</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.24377-black.svg)](https://arxiv.org/abs/2503.24377)17|<I>Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.16252-black.svg)](https://arxiv.org/abs/2503.16252)17|<I>Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.11647-black.svg)](https://arxiv.org/abs/2503.11647)17|<I>ReCamMaster: Camera-Controlled Generative Rendering from A Single Video</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.10631-black.svg)](https://arxiv.org/abs/2503.10631)17|<I>HybridVLA: Collaborative Diffusion and Autoregression in a Unified Vision-Language-Action Model</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.01067-black.svg)](https://arxiv.org/abs/2503.01067)17|<I>All Roads Lead to Likelihood: The Value of Reinforcement Learning in Fine-Tuning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.00555-black.svg)](https://arxiv.org/abs/2503.00555)17|<I>Safety Tax: Safety Alignment Makes Your Large Reasoning Models Less Reasonable</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.18013-black.svg)](https://arxiv.org/abs/2503.18013)16|<I>Vision-R1: Evolving Human-Free Alignment in Large Vision-Language Models via Vision-Guided Reinforcement Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.15477-black.svg)](https://arxiv.org/abs/2503.15477)16|<I>What Makes a Reward Model a Good Teacher? An Optimization Perspective</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.11733-black.svg)](https://arxiv.org/abs/2503.11733)16|<I>LLM Agents for Education: Advances and Applications</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.07027-black.svg)](https://arxiv.org/abs/2503.07027)16|<I>EasyControl: Adding Efficient and Flexible Control for Diffusion Transformer</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.02875-black.svg)](https://arxiv.org/abs/2503.02875)16|<I>The First Few Tokens Are All You Need: An Efficient and Effective Unsupervised Prefix Fine-Tuning Method for Reasoning Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.01710-black.svg)](https://arxiv.org/abs/2503.01710)16|<I>Spark-TTS: An Efficient LLM-Based Text-to-Speech Model with Single-Stream Decoupled Speech Tokens</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.23077-black.svg)](https://arxiv.org/abs/2503.23077)15|<I>Efficient Inference for Large Reasoning Models: A Survey</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.10622-black.svg)](https://arxiv.org/abs/2503.10622)15|<I>Transformers without Normalization</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/b4fe0232-119d-4dcf-b994-6e4ce8104b30.png">](https://youtu.be/sFmOMuot-hM)|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.02951-black.svg)](https://arxiv.org/abs/2503.02951)15|<I>KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.00223-black.svg)](https://arxiv.org/abs/2503.00223)15|<I>DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.20201-black.svg)](https://arxiv.org/abs/2503.20201)14|<I>Open Deep Search: Democratizing Search with Open-source Reasoning Agents</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/fd14a40f-97c2-416d-9af7-f84d23afae16.png">](https://youtu.be/hIgUZpSNXlM)|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.17407-black.svg)](https://arxiv.org/abs/2503.17407)14|<I>A Comprehensive Survey on Long Context Language Modeling</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.16081-black.svg)](https://arxiv.org/abs/2503.16081)14|<I>OThink-MR1: Stimulating multimodal generalized reasoning capabilities via dynamic reinforcement learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.15558-black.svg)](https://arxiv.org/abs/2503.15558)14|<I>Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.11651-black.svg)](https://arxiv.org/abs/2503.11651)14|<I>VGGT: Visual Geometry Grounded Transformer</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.09501-black.svg)](https://arxiv.org/abs/2503.09501)14|<I>ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.07703-black.svg)](https://arxiv.org/abs/2503.07703)14|<I>Seedream 2.0: A Native Chinese-English Bilingual Image Generation Foundation Model</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.04625-black.svg)](https://arxiv.org/abs/2503.04625)14|<I>START: Self-taught Reasoner with Tools</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.01840-black.svg)](https://arxiv.org/abs/2503.01840)14|<I>EAGLE-3: Scaling up Inference Acceleration of Large Language Models via Training-Time Test</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.23157-black.svg)](https://arxiv.org/abs/2503.23157)13|<I>Reasoning-SQL: Reinforcement Learning with SQL Tailored Partial Rewards for Reasoning-Enhanced Text-to-SQL</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.19855-black.svg)](https://arxiv.org/abs/2503.19855)13|<I>Think Twice: Enhancing LLM Reasoning by Scaling Multi-round Test-time Thinking</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.19633-black.svg)](https://arxiv.org/abs/2503.19633)13|<I>1.4 Million Open-Source Distilled Reasoning Dataset to Empower Large Language Model Training</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.16428-black.svg)](https://arxiv.org/abs/2503.16428)13|<I>XAttention: Block Sparse Attention with Antidiagonal Scoring</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.09648-black.svg)](https://arxiv.org/abs/2503.09648)13|<I>A Survey on Trustworthy LLM Agents: Threats and Countermeasures</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.09642-black.svg)](https://arxiv.org/abs/2503.09642)13|<I>Open-Sora 2.0: Training a Commercial-Level Video Generation Model in $200k</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2503.08638-black.svg)](https://arxiv.org/abs/2503.08638)13|<I>YuE: Scaling Open Foundation Models for Long-Form Music Generation</I>|not yet|
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|[![arxiv](https://img.shields.io/badge/arXiv-2503.20641-black.svg)](https://arxiv.org/abs/2503.20641)9|<I>Unlocking Efficient Long-to-Short LLM Reasoning with Model Merging</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/