【2025年4月公開 Arxiv論文ランキング】2504.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4月頃に公開されたcsカテゴリの論文 (ID: 2504.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15日更新)
   
| 被引用数　　|タイトル|動画|
|------:|:--------|:------------|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.10479-black.svg)](https://arxiv.org/abs/2504.10479)113|<I>InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.13837-black.svg)](https://arxiv.org/abs/2504.13837)90|<I>Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/c9d26279-8daa-4ee9-9de6-7775dacef6a7.png">](https://youtu.be/oORBZ-eWgfM)|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.07615-black.svg)](https://arxiv.org/abs/2504.07615)86|<I>VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.05118-black.svg)](https://arxiv.org/abs/2504.05118)47|<I>VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.07491-black.svg)](https://arxiv.org/abs/2504.07491)46|<I>Kimi-VL Technical Report</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.09858-black.svg)](https://arxiv.org/abs/2504.09858)43|<I>Reasoning Models Can Be Effective Without Thinking</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/1303e84c-6968-4c51-9c21-863e5cfb4403.png">](https://youtu.be/7QNCPkQA-Tw)|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.16084-black.svg)](https://arxiv.org/abs/2504.16084)39|<I>TTRL: Test-Time Reinforcement Learning</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/33322086-36da-4164-88a6-04907431402c.png">](https://youtu.be/6poXk6OB1ZI)|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.16054-black.svg)](https://arxiv.org/abs/2504.16054)39|<I>$\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.01296-black.svg)](https://arxiv.org/abs/2504.01296)39|<I>ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.02495-black.svg)](https://arxiv.org/abs/2504.02495)38|<I>Inference-Time Scaling for Generalist Reward Modeling</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.20571-black.svg)](https://arxiv.org/abs/2504.20571)36|<I>Reinforcement Learning for Reasoning in Large Language Models with One Training Example</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/1f1d7fd8-bfe9-480e-beae-9ba17bda9c5e.png">](https://youtu.be/VgFlwI25MkA)|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.08837-black.svg)](https://arxiv.org/abs/2504.08837)34|<I>VL-Rethinker: Incentivizing Self-Reflection of Vision-Language Models with Reinforcement Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.01990-black.svg)](https://arxiv.org/abs/2504.01990)33|<I>Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.20073-black.svg)](https://arxiv.org/abs/2504.20073)32|<I>RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.03160-black.svg)](https://arxiv.org/abs/2504.03160)32|<I>DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.10458-black.svg)](https://arxiv.org/abs/2504.10458)27|<I>GUI-R1 : A Generalist R1-Style Vision-Language Action Model For GUI Agents</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.06958-black.svg)](https://arxiv.org/abs/2504.06958)27|<I>VideoChat-R1: Enhancing Spatio-Temporal Perception via Reinforcement Fine-Tuning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.21801-black.svg)](https://arxiv.org/abs/2504.21801)26|<I>DeepSeek-Prover-V2: Advancing Formal Mathematical Reasoning via Reinforcement Learning for Subgoal Decomposition</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/b249737d-fcde-4591-9c74-1e84e4b0e23f.png">](https://youtu.be/pTnbIXYHAV0)|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.13958-black.svg)](https://arxiv.org/abs/2504.13958)26|<I>ToolRL: Reward is All Tool Learning Needs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.11536-black.svg)](https://arxiv.org/abs/2504.11536)26|<I>ReTool: Reinforcement Learning for Strategic Tool Use in LLMs</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/699dcd0b-226e-4bd9-be0e-b2e58d863d64.png">](https://youtu.be/j2xWGnY3xjg)|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.02782-black.svg)](https://arxiv.org/abs/2504.02782)25|<I>GPT-ImgEval: A Comprehensive Benchmark for Diagnosing GPT4o in Image Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.21318-black.svg)](https://arxiv.org/abs/2504.21318)24|<I>Phi-4-reasoning Technical Report</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/3fe6b8a8-f10c-4231-a196-f581e7089f4c.png">](https://youtu.be/Z-DOvoDfk6c)|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.13914-black.svg)](https://arxiv.org/abs/2504.13914)23|<I>Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.15585-black.svg)](https://arxiv.org/abs/2504.15585)22|<I>A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.07128-black.svg)](https://arxiv.org/abs/2504.07128)22|<I>DeepSeek-R1 Thoughtology: Let&#39;s think about LLM Reasoning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.05185-black.svg)](https://arxiv.org/abs/2504.05185)21|<I>Concise Reasoning via Reinforcement Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.11343-black.svg)](https://arxiv.org/abs/2504.11343)20|<I>A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.00698-black.svg)](https://arxiv.org/abs/2504.00698)20|<I>Command A: An Enterprise-Ready Large Language Model</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/0d69bf85-7e79-4b8d-8989-cb9c32a2fb65.png">](https://youtu.be/CkWJ4oQopK8)|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.17761-black.svg)](https://arxiv.org/abs/2504.17761)19|<I>Step1X-Edit: A Practical Framework for General Image Editing</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.15895-black.svg)](https://arxiv.org/abs/2504.15895)19|<I>Dynamic Early Exit in Reasoning Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.11468-black.svg)](https://arxiv.org/abs/2504.11468)19|<I>SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.09037-black.svg)](https://arxiv.org/abs/2504.09037)19|<I>A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.08066-black.svg)](https://arxiv.org/abs/2504.08066)18|<I>The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/32394b91-b964-4d03-bf5e-9f593a652a3f.png">](https://youtu.be/5r1k7vMkias)|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.05599-black.svg)](https://arxiv.org/abs/2504.05599)18|<I>Skywork R1V: Pioneering Multimodal Reasoning with Chain-of-Thought</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.05002-black.svg)](https://arxiv.org/abs/2504.05002)18|<I>SmartBugBert: BERT-Enhanced Vulnerability Detection for Smart Contract Bytecode</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.10903-black.svg)](https://arxiv.org/abs/2504.10903)17|<I>Efficient Reasoning Models: A Survey</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.06256-black.svg)](https://arxiv.org/abs/2504.06256)17|<I>Transfer between Modalities with MetaQueries</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.05006-black.svg)](https://arxiv.org/abs/2504.05006)17|<I>Enhancing Smart Contract Vulnerability Detection in DApps Leveraging Fine-Tuned LLM</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.01943-black.svg)](https://arxiv.org/abs/2504.01943)17|<I>OpenCodeReasoning: Advancing Data Distillation for Competitive Coding</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.18425-black.svg)](https://arxiv.org/abs/2504.18425)16|<I>Kimi-Audio Technical Report</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.11455-black.svg)](https://arxiv.org/abs/2504.11455)16|<I>SimpleAR: Pushing the Frontier of Autoregressive Visual Generation through Pretraining, SFT, and RL</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.07086-black.svg)](https://arxiv.org/abs/2504.07086)16|<I>A Sober Look at Progress in Language Model Reasoning: Pitfalls and Paths to Reproducibility</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.02546-black.svg)](https://arxiv.org/abs/2504.02546)16|<I>GPG: A Simple and Strong Reinforcement Learning Baseline for Model Reasoning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.01848-black.svg)](https://arxiv.org/abs/2504.01848)16|<I>PaperBench: Evaluating AI&#39;s Ability to Replicate AI Research</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.00906-black.svg)](https://arxiv.org/abs/2504.00906)16|<I>Agent S2: A Compositional Generalist-Specialist Framework for Computer Use Agents</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.14870-black.svg)](https://arxiv.org/abs/2504.14870)15|<I>Acting Less is Reasoning More! Teaching Model to Act Efficiently</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/de9369d8-4740-4a1a-bcb5-8a2fe44f165f.png">](https://youtu.be/vuXyXuD8qNA)|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.14239-black.svg)](https://arxiv.org/abs/2504.14239)15|<I>InfiGUI-R1: Advancing Multimodal GUI Agents from Reactive Actors to Deliberative Reasoners</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.07954-black.svg)](https://arxiv.org/abs/2504.07954)15|<I>Perception-R1: Pioneering Perception Policy with Reinforcement Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.04022-black.svg)](https://arxiv.org/abs/2504.04022)15|<I>Rethinking Reflection in Pre-Training</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.00810-black.svg)](https://arxiv.org/abs/2504.00810)15|<I>Z1: Efficient Test-time Scaling with Code</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.11354-black.svg)](https://arxiv.org/abs/2504.11354)14|<I>Kimina-Prover Preview: Towards Large Formal Reasoning Models with Reinforcement Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.07934-black.svg)](https://arxiv.org/abs/2504.07934)14|<I>SoTA with Less: MCTS-Guided Sample Selection for Data-Efficient Visual Reasoning Self-Improvement</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.05419-black.svg)](https://arxiv.org/abs/2504.05419)14|<I>Reasoning Models Know When They&#39;re Right: Probing Hidden States for Self-Verification</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.02234-black.svg)](https://arxiv.org/abs/2504.02234)14|<I>LLM Social Simulations Are a Promising Research Method</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.00993-black.svg)](https://arxiv.org/abs/2504.00993)14|<I>MedReason: Eliciting Factual Medical Reasoning Steps in LLMs via Knowledge Graphs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.14286-black.svg)](https://arxiv.org/abs/2504.14286)13|<I>SRPO: A Cross-Domain Implementation of Large-Scale Reinforcement Learning on LLM</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.09696-black.svg)](https://arxiv.org/abs/2504.09696)13|<I>GRPO-LEAD: A Difficulty-Aware Reinforcement Learning Approach for Concise Mathematical Reasoning in Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.08685-black.svg)](https://arxiv.org/abs/2504.08685)13|<I>Seaweed-7B: Cost-Effective Training of Video Generation Foundation Model</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.07981-black.svg)](https://arxiv.org/abs/2504.07981)13|<I>ScreenSpot-Pro: GUI Grounding for Professional High-Resolution Computer Use</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.06514-black.svg)](https://arxiv.org/abs/2504.06514)13|<I>Missing Premise exacerbates Overthinking: Are Reasoning Models losing Critical Thinking Skill?</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.05812-black.svg)](https://arxiv.org/abs/2504.05812)13|<I>Right Question is Already Half the Answer: Fully Unsupervised LLM Reasoning Incentivization</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.05520-black.svg)](https://arxiv.org/abs/2504.05520)13|<I>Efficient Reinforcement Finetuning via Adaptive Curriculum Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.04736-black.svg)](https://arxiv.org/abs/2504.04736)13|<I>Synthetic Data Generation &amp; Multi-Step RL for Reasoning &amp; Tool Use</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.00891-black.svg)](https://arxiv.org/abs/2504.00891)13|<I>GenPRM: Scaling Test-Time Compute of Process Reward Models via Generative Reasoning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.00869-black.svg)](https://arxiv.org/abs/2504.00869)13|<I>m1: Unleash the Potential of Test-Time Scaling for Medical Reasoning with Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.21776-black.svg)](https://arxiv.org/abs/2504.21776)12|<I>WebThinker: Empowering Large Reasoning Models with Deep Research Capability</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.16902-black.svg)](https://arxiv.org/abs/2504.16902)12|<I>Building A Secure Agentic AI Application Leveraging A2A Protocol</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/bea443a4-c386-42de-a23c-ef53a6e64ca5.png">](https://youtu.be/idavZvDXu6M)|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.16891-black.svg)](https://arxiv.org/abs/2504.16891)12|<I>AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models with OpenMathReasoning dataset</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.16656-black.svg)](https://arxiv.org/abs/2504.16656)12|<I>Skywork R1V2: Multimodal Hybrid Reinforcement Learning for Reasoning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.13055-black.svg)](https://arxiv.org/abs/2504.13055)12|<I>NoisyRollout: Reinforcing Visual Reasoning with Data Augmentation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.12720-black.svg)](https://arxiv.org/abs/2504.12720)12|<I>Malicious Code Detection in Smart Contracts via Opcode Vectorization</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.12516-black.svg)](https://arxiv.org/abs/2504.12516)12|<I>BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.09641-black.svg)](https://arxiv.org/abs/2504.09641)12|<I>TinyLLaVA-Video-R1: Towards Smaller LMMs for Video Reasoning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.08600-black.svg)](https://arxiv.org/abs/2504.08600)12|<I>SQL-R1: Training Natural Language to SQL Reasoning Model By Reinforcement Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.07891-black.svg)](https://arxiv.org/abs/2504.07891)12|<I>SpecReason: Fast and Accurate Inference-Time Compute via Speculative Reasoning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.05299-black.svg)](https://arxiv.org/abs/2504.05299)12|<I>SmolVLM: Redefining small and efficient multimodal models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.05298-black.svg)](https://arxiv.org/abs/2504.05298)12|<I>One-Minute Video Generation with Test-Time Training</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/f4774734-7e11-48a7-b45c-8d51836fb6b4.png">](https://youtu.be/TVb4PTEG3tc)|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.03624-black.svg)](https://arxiv.org/abs/2504.03624)12|<I>Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.00050-black.svg)](https://arxiv.org/abs/2504.00050)12|<I>JudgeLRM: Large Reasoning Models as a Judge</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.12680-black.svg)](https://arxiv.org/abs/2504.12680)11|<I>Embodied-R: Collaborative Framework for Activating Embodied Spatial Reasoning in Foundation Models via Reinforcement Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.11456-black.svg)](https://arxiv.org/abs/2504.11456)11|<I>DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.11346-black.svg)](https://arxiv.org/abs/2504.11346)11|<I>Seedream 3.0 Technical Report</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.10160-black.svg)](https://arxiv.org/abs/2504.10160)11|<I>MT-R1-Zero: Advancing LLM-based Machine Translation via R1-Zero-like Reinforcement Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.08623-black.svg)](https://arxiv.org/abs/2504.08623)11|<I>Enterprise-Grade Security for the Model Context Protocol (MCP): Frameworks and Mitigation Strategies</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.08528-black.svg)](https://arxiv.org/abs/2504.08528)11|<I>On The Landscape of Spoken Language Models: A Comprehensive Survey</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.07986-black.svg)](https://arxiv.org/abs/2504.07986)11|<I>SEAL: Steerable Reasoning Calibration of Large Language Models for Free</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.04423-black.svg)](https://arxiv.org/abs/2504.04423)11|<I>UniToken: Harmonizing Multimodal Understanding and Generation through Unified Visual Encoding</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.02605-black.svg)](https://arxiv.org/abs/2504.02605)11|<I>Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.01849-black.svg)](https://arxiv.org/abs/2504.01849)11|<I>An Approach to Technical AGI Safety and Security</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.18458-black.svg)](https://arxiv.org/abs/2504.18458)10|<I>Fast-Slow Thinking for Large Vision-Language Model Reasoning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.17704-black.svg)](https://arxiv.org/abs/2504.17704)10|<I>Safety in Large Reasoning Models: A Survey</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.16736-black.svg)](https://arxiv.org/abs/2504.16736)10|<I>A Survey of AI Agent Protocols</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.16074-black.svg)](https://arxiv.org/abs/2504.16074)10|<I>PHYBench: Holistic Evaluation of Physical Perception and Reasoning in Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.15279-black.svg)](https://arxiv.org/abs/2504.15279)10|<I>VisuLogic: A Benchmark for Evaluating Visual Reasoning in Multi-modal Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2504.14047-black.svg)](https://arxiv.org/abs/2504.14047)10|<I>Think Deep, Think Fast: Investigating Efficiency of Verifier-free Inference-time-scaling Methods</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/