【2025年1月公開 Arxiv論文ランキング】2501.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1月頃に公開されたcsカテゴリの論文 (ID: 2501.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4月4日更新)

  
| 被引用数　　|タイトル|動画|
|------:|:--------|:------------|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.12948-black.svg)](https://arxiv.org/abs/2501.12948)821|<I>DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.19393-black.svg)](https://arxiv.org/abs/2501.19393)85|<I>s1: Simple test-time scaling</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.12599-black.svg)](https://arxiv.org/abs/2501.12599)78|<I>Kimi k1.5: Scaling Reinforcement Learning with LLMs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.04519-black.svg)](https://arxiv.org/abs/2501.04519)67|<I>rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/e8ef32eb-9723-4539-8d76-3fe4831ac82e.png">](https://youtu.be/ggCeWCNHQDI)|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.03575-black.svg)](https://arxiv.org/abs/2501.03575)44|<I>Cosmos World Foundation Model Platform for Physical AI</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.00656-black.svg)](https://arxiv.org/abs/2501.00656)39|<I>2 OLMo 2 Furious</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.07301-black.svg)](https://arxiv.org/abs/2501.07301)36|<I>The Lessons of Developing Process Reward Models in Mathematical Reasoning</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/384a89e3-49af-4400-9bf0-7cdcac33f21a.png">](https://youtu.be/kupVwkMVSVk)|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.09686-black.svg)](https://arxiv.org/abs/2501.09686)34|<I>Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.14249-black.svg)](https://arxiv.org/abs/2501.14249)32|<I>Humanity&#39;s Last Exam</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/61b15a08-7f32-4596-bf34-a9f56588e636.png">](https://youtu.be/e41QhFgZ9wU)|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.17811-black.svg)](https://arxiv.org/abs/2501.17811)31|<I>Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.00103-black.svg)](https://arxiv.org/abs/2501.00103)27|<I>LTX-Video: Realtime Video Latent Diffusion</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.08313-black.svg)](https://arxiv.org/abs/2501.08313)26|<I>MiniMax-01: Scaling Foundation Models with Lightning Attention</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.04682-black.svg)](https://arxiv.org/abs/2501.04682)25|<I>Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.17161-black.svg)](https://arxiv.org/abs/2501.17161)24|<I>SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/b4bc141c-9e5d-4d83-aa20-4623cb3d6fd5.png">](https://youtu.be/3TyDG5-oFPY)|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.01957-black.svg)](https://arxiv.org/abs/2501.01957)22|<I>VITA-1.5: Towards GPT-4o Level Real-Time Vision and Speech Interaction</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.00663-black.svg)](https://arxiv.org/abs/2501.00663)22|<I>Titans: Learning to Memorize at Test Time</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/8439fdec-56c1-4874-9681-1698d552abe3.png">](https://youtu.be/h7XhbK9cguQ)|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.05366-black.svg)](https://arxiv.org/abs/2501.05366)19|<I>Search-o1: Agentic Search-Enhanced Large Reasoning Models</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/1981edda-c547-47e0-8a72-3a145f3a6715.png">](https://youtu.be/CjEI69r1_SI)|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.09732-black.svg)](https://arxiv.org/abs/2501.09732)17|<I>Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/e37d3723-a546-4c27-9c21-3073ae567557.png">](https://youtu.be/UUtY8oo65V4)|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.06186-black.svg)](https://arxiv.org/abs/2501.06186)17|<I>LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.03262-black.svg)](https://arxiv.org/abs/2501.03262)17|<I>REINFORCE++: An Efficient RLHF Algorithm with Robustness to Both Prompt and Reward Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.04377-black.svg)](https://arxiv.org/abs/2501.04377)16|<I>On Computational Limits and Provably Efficient Criteria of Visual Autoregressive Models: A Fine-Grained Complexity Analysis</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.01005-black.svg)](https://arxiv.org/abs/2501.01005)16|<I>FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.18585-black.svg)](https://arxiv.org/abs/2501.18585)14|<I>Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/741f50c6-6008-4b11-9940-50a25cc20f43.png">](https://youtu.be/I4N7U8Yqm4Q)|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.13958-black.svg)](https://arxiv.org/abs/2501.13958)14|<I>A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.13106-black.svg)](https://arxiv.org/abs/2501.13106)14|<I>VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.09891-black.svg)](https://arxiv.org/abs/2501.09891)14|<I>Evolving Deeper LLM Thinking</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/e162f340-074f-4f9d-a23f-2d4a44364dd3.png">](https://youtu.be/FOTp_XwoyXE)|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.04952-black.svg)](https://arxiv.org/abs/2501.04952)14|<I>Open Problems in Machine Unlearning for AI Safety</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/c4d8e87b-f45c-4d5b-8159-c808e72783a8.png">](https://youtu.be/7rkxL2Dmh6o)|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.04227-black.svg)](https://arxiv.org/abs/2501.04227)14|<I>Agent Laboratory: Using LLM Agents as Research Assistants</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/71f97522-52ef-4718-b8ef-7bc52dca16d6.png">](https://youtu.be/KD3cQGubUCw)|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.06444-black.svg)](https://arxiv.org/abs/2501.06444)13|<I>On the Computational Capability of Graph Neural Networks: A Circuit Complexity Bound Perspective</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.11651-black.svg)](https://arxiv.org/abs/2501.11651)12|<I>Advancing Language Model Reasoning through Reinforcement Learning and Inference Scaling</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.10688-black.svg)](https://arxiv.org/abs/2501.10688)12|<I>Neural Algorithmic Reasoning for Hypergraphs with Looped Transformers</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.09747-black.svg)](https://arxiv.org/abs/2501.09747)12|<I>FAST: Efficient Action Tokenization for Vision-Language-Action Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.09038-black.svg)](https://arxiv.org/abs/2501.09038)12|<I>Do generative video models understand physical principles?</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/d46cbec6-84bd-4bb1-b216-1749f26471fa.png">](https://youtu.be/JuOGU39KY6w)|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.03895-black.svg)](https://arxiv.org/abs/2501.03895)12|<I>LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.01904-black.svg)](https://arxiv.org/abs/2501.01904)12|<I>Virgo: A Preliminary Exploration on Reproducing o1-like MLLM</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.01423-black.svg)](https://arxiv.org/abs/2501.01423)12|<I>Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.12570-black.svg)](https://arxiv.org/abs/2501.12570)11|<I>O1-Pruner: Length-Harmonizing Fine-Tuning for O1-Like Reasoning Pruning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.03124-black.svg)](https://arxiv.org/abs/2501.03124)11|<I>PRMBench: A Fine-grained and Challenging Benchmark for Process-Level Reward Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.01377-black.svg)](https://arxiv.org/abs/2501.01377)11|<I>Training Medical Large Vision-Language Models with Abnormal-Aware Feedback</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.13926-black.svg)](https://arxiv.org/abs/2501.13926)10|<I>Can We Generate Images with CoT? Let&#39;s Verify and Reinforce Image Generation Step by Step</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.12202-black.svg)](https://arxiv.org/abs/2501.12202)10|<I>Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.07542-black.svg)](https://arxiv.org/abs/2501.07542)10|<I>Imagine while Reasoning in Space: Multimodal Visualization-of-Thought</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/1836a357-f7b5-4c64-aa8b-2d59885b9c3e.png">](https://youtu.be/Y2V4ufR9RDE)|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.02189-black.svg)](https://arxiv.org/abs/2501.02189)10|<I>A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challenges</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.02116-black.svg)](https://arxiv.org/abs/2501.02116)10|<I>Humanoid Locomotion and Manipulation: Current Progress and Challenges in Control, Planning, and Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.00574-black.svg)](https://arxiv.org/abs/2501.00574)10|<I>VideoChat-Flash: Hierarchical Compression for Long-Context Video Modeling</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.00309-black.svg)](https://arxiv.org/abs/2501.00309)10|<I>Retrieval-Augmented Generation with Graphs (GraphRAG)</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.17805-black.svg)](https://arxiv.org/abs/2501.17805)9|<I>International AI Safety Report</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.16496-black.svg)](https://arxiv.org/abs/2501.16496)9|<I>Open Problems in Mechanistic Interpretability</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.15383-black.svg)](https://arxiv.org/abs/2501.15383)9|<I>Qwen2.5-1M Technical Report</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.12326-black.svg)](https://arxiv.org/abs/2501.12326)9|<I>UI-TARS: Pioneering Automated GUI Interaction with Native Agents</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/cb3da1f2-6ca6-4d53-bee8-4d9f3f27aa4f.png">](https://youtu.be/Lf8HdH0Cpb4)|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.11284-black.svg)](https://arxiv.org/abs/2501.11284)9|<I>RedStar: Does Scaling Long-CoT Data Unlock Better Slow-Reasoning Systems?</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.07033-black.svg)](https://arxiv.org/abs/2501.07033)9|<I>Detection of AI Deepfake and Fraud in Online Payments Using GAN-Based Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.06425-black.svg)](https://arxiv.org/abs/2501.06425)9|<I>Tensor Product Attention Is All You Need</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.06322-black.svg)](https://arxiv.org/abs/2501.06322)9|<I>Multi-Agent Collaboration Mechanisms: A Survey of LLMs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.05707-black.svg)](https://arxiv.org/abs/2501.05707)9|<I>Multiagent Finetuning: Self Improvement with Diverse Reasoning Chains</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.04299-black.svg)](https://arxiv.org/abs/2501.04299)9|<I>Circuit Complexity Bounds for Visual Autoregressive Model</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.18438-black.svg)](https://arxiv.org/abs/2501.18438)8|<I>o3-mini vs DeepSeek-R1: Which One is Safer?</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.18099-black.svg)](https://arxiv.org/abs/2501.18099)8|<I>Learning to Plan &amp; Reason for Evaluation with Thinking-LLM-as-a-Judge</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.12386-black.svg)](https://arxiv.org/abs/2501.12386)8|<I>InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modeling</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.12368-black.svg)](https://arxiv.org/abs/2501.12368)8|<I>InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Model</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.10753-black.svg)](https://arxiv.org/abs/2501.10753)8|<I>Pinching Antennas: Principles, Applications and Challenges</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.06848-black.svg)](https://arxiv.org/abs/2501.06848)8|<I>A General Framework for Inference-time Scaling and Steering of Diffusion Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.06282-black.svg)](https://arxiv.org/abs/2501.06282)8|<I>MinMo: A Multimodal Large Language Model for Seamless Voice Interaction</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.04686-black.svg)](https://arxiv.org/abs/2501.04686)8|<I>URSA: Understanding and Verifying Chain-of-thought Reasoning in Multimodal Mathematics</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.02595-black.svg)](https://arxiv.org/abs/2501.02595)8|<I>Rotatable Antenna Enabled Wireless Communication: Modeling and Optimization</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.02497-black.svg)](https://arxiv.org/abs/2501.02497)8|<I>Test-Time Compute: from System-1 Thinking to System-2 Thinking</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.18427-black.svg)](https://arxiv.org/abs/2501.18427)7|<I>SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.16652-black.svg)](https://arxiv.org/abs/2501.16652)7|<I>Molecular-driven Foundation Model for Oncologic Pathology</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.15368-black.svg)](https://arxiv.org/abs/2501.15368)7|<I>Baichuan-Omni-1.5 Technical Report</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.15167-black.svg)](https://arxiv.org/abs/2501.15167)7|<I>Enhancing Intent Understanding for Ambiguous prompt: A Human-Machine Co-Adaption Strategy</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.11223-black.svg)](https://arxiv.org/abs/2501.11223)7|<I>Reasoning Language Models: A Blueprint</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.09781-black.svg)](https://arxiv.org/abs/2501.09781)7|<I>VideoWorld: Exploring Knowledge Learning from Unlabeled Videos</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.06458-black.svg)](https://arxiv.org/abs/2501.06458)7|<I>O1 Replication Journey -- Part 3: Inference-time Scaling for Medical Reasoning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.04606-black.svg)](https://arxiv.org/abs/2501.04606)7|<I>Enhancing Low-Cost Video Editing with Lightweight Adaptors and Temporal-Aware Inversion</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.04561-black.svg)](https://arxiv.org/abs/2501.04561)7|<I>OpenOmni: Advancing Open-Source Omnimodal Large Language Models with Progressive Multimodal Alignment and Real-Time Self-Aware Emotional Speech Synthesis</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.01257-black.svg)](https://arxiv.org/abs/2501.01257)7|<I>CodeElo: Benchmarking Competition-level Code Generation of LLMs with Human-comparable Elo Ratings</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.19306-black.svg)](https://arxiv.org/abs/2501.19306)6|<I>SETS: Leveraging Self-Verification and Self-Correction for Improved Test-Time Scaling</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.18648-black.svg)](https://arxiv.org/abs/2501.18648)6|<I>Multimodal Large Language Models for Image, Text, and Speech Data Augmentation: A Survey</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.17749-black.svg)](https://arxiv.org/abs/2501.17749)6|<I>Early External Safety Testing of OpenAI&#39;s o3-mini: Insights from the Pre-Deployment Evaluation</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/2f894811-15eb-4958-9e6b-29dbaed5fe72.png">](https://youtu.be/gbqq0C6K9Bo)|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.17703-black.svg)](https://arxiv.org/abs/2501.17703)6|<I>Critique Fine-Tuning: Learning to Critique is More Effective than Learning to Imitate</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.15830-black.svg)](https://arxiv.org/abs/2501.15830)6|<I>SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.13918-black.svg)](https://arxiv.org/abs/2501.13918)6|<I>Improving Video Generation with Human Feedback</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.11733-black.svg)](https://arxiv.org/abs/2501.11733)6|<I>Mobile-Agent-E: Self-Evolving Mobile Assistant for Complex Tasks</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.11425-black.svg)](https://arxiv.org/abs/2501.11425)6|<I>Agent-R: Training Language Model Agents to Reflect via Iterative Self-Training</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.09982-black.svg)](https://arxiv.org/abs/2501.09982)6|<I>RichSpace: Enriching Text-to-Video Prompt Space via Text Embedding Interpolation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.09685-black.svg)](https://arxiv.org/abs/2501.09685)6|<I>Inference-Time Alignment in Diffusion Models with Reward-Guided Generation: Tutorial and Review</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.08316-black.svg)](https://arxiv.org/abs/2501.08316)6|<I>Diffusion Adversarial Post-Training for One-Step Video Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.06994-black.svg)](https://arxiv.org/abs/2501.06994)6|<I>Motion Tracks: A Unified Representation for Human-Robot Transfer in Few-Shot Imitation Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.04575-black.svg)](https://arxiv.org/abs/2501.04575)6|<I>InfiGUIAgent: A Multimodal Generalist GUI Agent with Native Reasoning and Reflection</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2501.02487-black.svg)](https://arxiv.org/abs/2501.02487)6|<I>ACE++: Instruction-Based Image Creation and Editing via Context-Aware Content Filling</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/