【2024年12月公開 Arxiv論文ランキング】2412.xxxxx

# AI論文解説 Youtubeチャンネル: AI時代の羅針盤 

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

2024年12月頃に公開されたcsカテゴリの論文 (ID: 2412.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-2412.15115-black.svg)](https://arxiv.org/abs/2412.15115)595|<I>Qwen2.5 Technical Report</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.19437-black.svg)](https://arxiv.org/abs/2412.19437)456|<I>DeepSeek-V3 Technical Report</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.16720-black.svg)](https://arxiv.org/abs/2412.16720)262|<I>OpenAI o1 System Card</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.05271-black.svg)](https://arxiv.org/abs/2412.05271)147|<I>Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.03603-black.svg)](https://arxiv.org/abs/2412.03603)94|<I>HunyuanVideo: A Systematic Framework For Large Video Generative Models</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/92956cac-cd9c-48be-8759-ac8f9719e882.png">](https://youtu.be/LNmDXSBhOmo)|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.08905-black.svg)](https://arxiv.org/abs/2412.08905)82|<I>Phi-4 Technical Report</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/edc3a873-dbfd-4bcc-bbea-24e35080c852.png">](https://youtu.be/3ZOvG9MZX3U)|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.10302-black.svg)](https://arxiv.org/abs/2412.10302)50|<I>DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.21187-black.svg)](https://arxiv.org/abs/2412.21187)48|<I>Do NOT Think That Much for 2+3</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/f6aa01b8-e698-4e3e-ad17-dd8b9cae7351.png">](https://youtu.be/EVVgULCvcB0)|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.06769-black.svg)](https://arxiv.org/abs/2412.06769)43|<I>Training Large Language Models to Reason in a Continuous Latent Space</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/a8314712-11b8-4e86-9d55-6f06da93ce34.png">](https://youtu.be/oNNwf1U9r7I)|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.13663-black.svg)](https://arxiv.org/abs/2412.13663)39|<I>Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.00131-black.svg)](https://arxiv.org/abs/2412.00131)38|<I>Open-Sora Plan: Open-Source Large Video Generation Model</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/2d1f2035-a3a9-49ba-bc3d-d31d3bef21ad.png">](https://youtu.be/BEvAYXxxbvQ)|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.06559-black.svg)](https://arxiv.org/abs/2412.06559)37|<I>ProcessBench: Identifying Process Errors in Mathematical Reasoning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.20404-black.svg)](https://arxiv.org/abs/2412.20404)36|<I>Open-Sora: Democratizing Efficient Video Production for All</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.01506-black.svg)](https://arxiv.org/abs/2412.01506)36|<I>Structured 3D Latents for Scalable and Versatile 3D Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.16339-black.svg)](https://arxiv.org/abs/2412.16339)34|<I>Deliberative Alignment: Reasoning Enables Safer Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.09413-black.svg)](https://arxiv.org/abs/2412.09413)33|<I>Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.05579-black.svg)](https://arxiv.org/abs/2412.05579)30|<I>LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.01981-black.svg)](https://arxiv.org/abs/2412.01981)26|<I>Free Process Rewards without Process Labels</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.14093-black.svg)](https://arxiv.org/abs/2412.14093)25|<I>Alignment faking in large language models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.04431-black.svg)](https://arxiv.org/abs/2412.04431)25|<I>Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/02d8ed76-453e-41dc-aea4-a6779d73a08d.png">](https://youtu.be/EpiqaPCmDmg)|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.15204-black.svg)](https://arxiv.org/abs/2412.15204)23|<I>LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.06264-black.svg)](https://arxiv.org/abs/2412.06264)23|<I>Flow Matching Guide and Code</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.02376-black.svg)](https://arxiv.org/abs/2412.02376)23|<I>Flexible-Antenna Systems: A Pinching-Antenna Perspective</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.18319-black.svg)](https://arxiv.org/abs/2412.18319)22|<I>Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.04468-black.svg)](https://arxiv.org/abs/2412.04468)22|<I>NVILA: Efficient Frontier Visual Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.03304-black.svg)](https://arxiv.org/abs/2412.03304)22|<I>Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.18925-black.svg)](https://arxiv.org/abs/2412.18925)20|<I>HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/d71b5cb6-623d-4be8-9e60-ca1deea21d9c.png">](https://youtu.be/j-5xDjcVSsQ)|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.14171-black.svg)](https://arxiv.org/abs/2412.14171)20|<I>Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.09871-black.svg)](https://arxiv.org/abs/2412.09871)20|<I>Byte Latent Transformer: Patches Scale Better Than Tokens</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/6e34dbfa-c7c6-4792-affd-6603fe359238.png">](https://youtu.be/hE5FtquhMTc)|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.04261-black.svg)](https://arxiv.org/abs/2412.04261)20|<I>Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontier</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.02612-black.svg)](https://arxiv.org/abs/2412.02612)20|<I>GLM-4-Voice: Towards Intelligent and Human-Like End-to-End Spoken Chatbot</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.03069-black.svg)](https://arxiv.org/abs/2412.03069)19|<I>TokenFlow: Unified Image Tokenizer for Multimodal Understanding and Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.08821-black.svg)](https://arxiv.org/abs/2412.08821)18|<I>Large Concept Models: Language Modeling in a Sentence Representation Space</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/8286f10d-8a0b-42f5-a3da-0ee4f9448bd7.png">](https://youtu.be/eOgN3BcWDS4)|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.04467-black.svg)](https://arxiv.org/abs/2412.04467)18|<I>VisionZip: Longer is Better but Not Necessary in Vision Language Models</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/d3d87dde-857a-4731-b099-6b06bd614d15.png">](https://youtu.be/2CgsxZUeDS8)|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.03555-black.svg)](https://arxiv.org/abs/2412.03555)18|<I>PaliGemma 2: A Family of Versatile VLMs for Transfer</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/849a10f8-65d4-40ff-85d7-da242c51e258.png">](https://youtu.be/-EuwOMRo3gw)|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.17316-black.svg)](https://arxiv.org/abs/2412.17316)17|<I>Fast Gradient Computation for RoPE Attention in Almost Linear Time</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.14135-black.svg)](https://arxiv.org/abs/2412.14135)17|<I>Scaling of Search and Learning: A Roadmap to Reproduce o1 from Reinforcement Learning Perspective</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/a9c4eda9-9925-42dc-b039-91f4a3d06aaf.png">](https://youtu.be/gdmqf-Xn0vg)|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.15165-black.svg)](https://arxiv.org/abs/2412.15165)16|<I>Experimental Demonstration of Logical Magic State Distillation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.14164-black.svg)](https://arxiv.org/abs/2412.14164)16|<I>MetaMorph: Multimodal Understanding and Generation via Instruction Tuning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.13196-black.svg)](https://arxiv.org/abs/2412.13196)16|<I>ExBody2: Advanced Expressive Humanoid Whole-Body Control</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.04984-black.svg)](https://arxiv.org/abs/2412.04984)16|<I>Frontier Models are Capable of In-context Scheming</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.00154-black.svg)](https://arxiv.org/abs/2412.00154)16|<I>o1-Coder: an o1 Replication for Coding</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.18547-black.svg)](https://arxiv.org/abs/2412.18547)15|<I>Token-Budget-Aware LLM Reasoning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.04604-black.svg)](https://arxiv.org/abs/2412.04604)15|<I>ARC Prize 2024: Technical Report</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/5dbfff69-23dc-43c6-b3c5-46a63c19e27b.png">](https://youtu.be/_SMIAZMoCEU)|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.03556-black.svg)](https://arxiv.org/abs/2412.03556)15|<I>Best-of-N Jailbreaking</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.18040-black.svg)](https://arxiv.org/abs/2412.18040)14|<I>Theoretical Constraints on the Expressive Power of $\mathsf{RoPE}$-based Tensor Attention Transformers</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.02700-black.svg)](https://arxiv.org/abs/2412.02700)14|<I>Motion Prompting: Controlling Video Generation with Motion Trajectories</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.10117-black.svg)](https://arxiv.org/abs/2412.10117)13|<I>CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.09078-black.svg)](https://arxiv.org/abs/2412.09078)13|<I>Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.07773-black.svg)](https://arxiv.org/abs/2412.07773)13|<I>Mobile-TeleVision: Predictive Motion Priors for Humanoid Whole-Body Control</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.07772-black.svg)](https://arxiv.org/abs/2412.07772)13|<I>From Slow Bidirectional to Fast Autoregressive Video Diffusion Models</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/ae91d625-a2f2-4554-8182-647265a57c54.png">](https://youtu.be/srtdwHR-134)|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.06148-black.svg)](https://arxiv.org/abs/2412.06148)13|<I>The Computational Limits of State-Space Models and Mamba via the Lens of Circuit Complexity</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.16075-black.svg)](https://arxiv.org/abs/2412.16075)12|<I>Formal Mathematical Reasoning: A New Frontier in AI</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.15205-black.svg)](https://arxiv.org/abs/2412.15205)12|<I>FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.14161-black.svg)](https://arxiv.org/abs/2412.14161)12|<I>TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/ad8ec389-6d5d-4c1c-90ad-f247194fbb66.png">](https://youtu.be/a_WMqs-zQuE)|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.13877-black.svg)](https://arxiv.org/abs/2412.13877)12|<I>RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.13171-black.svg)](https://arxiv.org/abs/2412.13171)12|<I>Compressed Chain of Thought: Efficient Reasoning Through Dense Representations</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.10360-black.svg)](https://arxiv.org/abs/2412.10360)12|<I>Apollo: An Exploration of Video Understanding in Large Multimodal Models</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/a5b4ce94-285a-4a49-866d-0bbf15b7b40f.png">](https://youtu.be/8j7jcfQyZGA)|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.06966-black.svg)](https://arxiv.org/abs/2412.06966)12|<I>Machine Unlearning Doesn&#39;t Do What You Think: Lessons for Generative AI Policy, Research, and Practice</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/85f3aa01-b92e-4bb8-b30c-1593c232e2cc.png">](https://youtu.be/Bzt40C0ahgk)|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.20292-black.svg)](https://arxiv.org/abs/2412.20292)11|<I>An analytic theory of creativity in convolutional diffusion models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.15188-black.svg)](https://arxiv.org/abs/2412.15188)11|<I>LMFusion: Adapting Pretrained Language Models for Multimodal Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.05496-black.svg)](https://arxiv.org/abs/2412.05496)11|<I>Flex Attention: A Programming Model for Generating Optimized Attention Kernels</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.01197-black.svg)](https://arxiv.org/abs/2412.01197)11|<I>InstantSwap: Fast Customized Concept Swapping across Sharp Shape Differences</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.19442-black.svg)](https://arxiv.org/abs/2412.19442)10|<I>A Survey on Large Language Model Acceleration based on KV Cache Management</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.19260-black.svg)](https://arxiv.org/abs/2412.19260)10|<I>MEDEC: A Benchmark for Medical Error Detection and Correction in Clinical Notes</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/f5c1de83-fcb8-4151-9052-947066cd6e4e.png">](https://youtu.be/lpGoq6TEC6Q)|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.19048-black.svg)](https://arxiv.org/abs/2412.19048)10|<I>Jasper and Stella: distillation of SOTA embedding models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.17498-black.svg)](https://arxiv.org/abs/2412.17498)10|<I>DRT: Deep Reasoning Translation via Long Chain-of-Thought</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.15322-black.svg)](https://arxiv.org/abs/2412.15322)10|<I>Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.15119-black.svg)](https://arxiv.org/abs/2412.15119)10|<I>Parallelized Autoregressive Visual Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.15084-black.svg)](https://arxiv.org/abs/2412.15084)10|<I>AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.14169-black.svg)](https://arxiv.org/abs/2412.14169)10|<I>Autoregressive Video Generation without Vector Quantization</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.14058-black.svg)](https://arxiv.org/abs/2412.14058)10|<I>Towards Generalist Robot Policies: What Matters in Building Vision-Language-Action Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.12444-black.svg)](https://arxiv.org/abs/2412.12444)10|<I>LazyDiT: Lazy Learning for the Acceleration of Diffusion Transformers</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.09596-black.svg)](https://arxiv.org/abs/2412.09596)10|<I>InternLM-XComposer2.5-OmniLive: A Comprehensive Multimodal System for Long-term Streaming Video and Audio Interactions</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.05467-black.svg)](https://arxiv.org/abs/2412.05467)10|<I>The BrowserGym Ecosystem for Web Agent Research</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.04454-black.svg)](https://arxiv.org/abs/2412.04454)10|<I>Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.04332-black.svg)](https://arxiv.org/abs/2412.04332)10|<I>Liquid: Language Models are Scalable and Unified Multi-modal Generators</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.02104-black.svg)](https://arxiv.org/abs/2412.02104)10|<I>Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.01818-black.svg)](https://arxiv.org/abs/2412.01818)10|<I>[CLS] Attention is All You Need for Training-Free Visual Token Pruning: Make VLM Inference Faster</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.19723-black.svg)](https://arxiv.org/abs/2412.19723)9|<I>OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.17808-black.svg)](https://arxiv.org/abs/2412.17808)9|<I>Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.16429-black.svg)](https://arxiv.org/abs/2412.16429)9|<I>LearnLM: Improving Gemini for Learning</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/56cf5a77-4187-4ac6-ac49-408cb0c5ef54.png">](https://youtu.be/q2oNX_rlBLs)|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.16145-black.svg)](https://arxiv.org/abs/2412.16145)9|<I>Offline Reinforcement Learning for LLM Multi-Step Reasoning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.15579-black.svg)](https://arxiv.org/abs/2412.15579)9|<I>Score-based Generative Diffusion Models for Social Recommendations</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.15287-black.svg)](https://arxiv.org/abs/2412.15287)9|<I>Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.11006-black.svg)](https://arxiv.org/abs/2412.11006)9|<I>Entropy-Regularized Process Reward Model</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.10345-black.svg)](https://arxiv.org/abs/2412.10345)9|<I>TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for Generalist Robotic Policies</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.08629-black.svg)](https://arxiv.org/abs/2412.08629)9|<I>FlowEdit: Inversion-Free Text-Based Editing Using Pre-Trained Flow Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.07774-black.svg)](https://arxiv.org/abs/2412.07774)9|<I>UniReal: Universal Image Generation and Editing via Learning Real-world Dynamics</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.07223-black.svg)](https://arxiv.org/abs/2412.07223)9|<I>A Consolidated Volatility Prediction with Back Propagation Neural Network and Genetic Algorithm</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.07097-black.svg)](https://arxiv.org/abs/2412.07097)9|<I>On Evaluating the Durability of Safeguards for Open-Weight LLMs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.06464-black.svg)](https://arxiv.org/abs/2412.06464)9|<I>Gated Delta Networks: Improving Mamba2 with Delta Rule</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.06410-black.svg)](https://arxiv.org/abs/2412.06410)9|<I>BatchTopK Sparse Autoencoders</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.05536-black.svg)](https://arxiv.org/abs/2412.05536)9|<I>Comprehensive Evaluation of Multimodal AI Models in Medical Imaging Diagnosis: From Data Augmentation to Preference-Based Comparison</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.05237-black.svg)](https://arxiv.org/abs/2412.05237)9|<I>MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.05210-black.svg)](https://arxiv.org/abs/2412.05210)9|<I>Evaluating and Aligning CodeLLMs on Human Preference</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.01827-black.svg)](https://arxiv.org/abs/2412.01827)9|<I>RandAR: Decoder-only Autoregressive Visual Generation in Random Orders</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.00714-black.svg)](https://arxiv.org/abs/2412.00714)9|<I>Scaling New Frontiers: Insights into Large Recommendation Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.21139-black.svg)](https://arxiv.org/abs/2412.21139)8|<I>Training Software Engineering Agents and Verifiers with SWE-Gym</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.16256-black.svg)](https://arxiv.org/abs/2412.16256)8|<I>Aria-UI: Visual Grounding for GUI Instructions</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.15838-black.svg)](https://arxiv.org/abs/2412.15838)8|<I>Align Anything: Training All-Modality Models to Follow Instructions with Language Feedback</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.15024-black.svg)](https://arxiv.org/abs/2412.15024)8|<I>Categorical Symmetries in Spin Models with Atom Arrays</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.13501-black.svg)](https://arxiv.org/abs/2412.13501)8|<I>GUI Agents: A Survey</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.12881-black.svg)](https://arxiv.org/abs/2412.12881)8|<I>RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.07670-black.svg)](https://arxiv.org/abs/2412.07670)8|<I>Fault-Tolerant Operation and Materials Science with Neutral Atom Logical Qubits</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.07197-black.svg)](https://arxiv.org/abs/2412.07197)8|<I>Hierarchical Split Federated Learning: Convergence Analysis and System Optimization</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.05562-black.svg)](https://arxiv.org/abs/2412.05562)8|<I>On the Expressive Power of Modern Hopfield Networks</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.05282-black.svg)](https://arxiv.org/abs/2412.05282)8|<I>International Scientific Report on the Safety of Advanced AI (Interim Report)</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.03293-black.svg)](https://arxiv.org/abs/2412.03293)8|<I>Diffusion-VLA: Scaling Robot Foundation Models via Unified Diffusion and Autoregression</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.03205-black.svg)](https://arxiv.org/abs/2412.03205)8|<I>U-MATH: A University-Level Benchmark for Evaluating Mathematical Skills in LLMs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.02674-black.svg)](https://arxiv.org/abs/2412.02674)8|<I>Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.02211-black.svg)](https://arxiv.org/abs/2412.02211)8|<I>An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.20138-black.svg)](https://arxiv.org/abs/2412.20138)7|<I>TradingAgents: Multi-Agents LLM Financial Trading Framework</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.19990-black.svg)](https://arxiv.org/abs/2412.19990)7|<I>SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.18607-black.svg)](https://arxiv.org/abs/2412.18607)7|<I>DrivingGPT: Unifying Driving World Modeling and Planning with Multi-modal Autoregressive Transformers</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.16833-black.svg)](https://arxiv.org/abs/2412.16833)7|<I>KG4Diagnosis: A Hierarchical Multi-Agent LLM Framework with Knowledge Graph Enhancement for Medical Diagnosis</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.14835-black.svg)](https://arxiv.org/abs/2412.14835)7|<I>Progressive Multimodal Reasoning via Active Retrieval</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.14475-black.svg)](https://arxiv.org/abs/2412.14475)7|<I>MegaPairs: Massive Data Synthesis For Universal Multimodal Retrieval</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.14470-black.svg)](https://arxiv.org/abs/2412.14470)7|<I>Agent-SafetyBench: Evaluating the Safety of LLM Agents</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.13892-black.svg)](https://arxiv.org/abs/2412.13892)7|<I>Minimum Data Rate Maximization for Uplink Pinching-Antenna Systems</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.13432-black.svg)](https://arxiv.org/abs/2412.13432)7|<I>Large Language Model Enhanced Recommender Systems: A Survey</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.13178-black.svg)](https://arxiv.org/abs/2412.13178)7|<I>SafeAgentBench: A Benchmark for Safe Task Planning of Embodied LLM Agents</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.12974-black.svg)](https://arxiv.org/abs/2412.12974)7|<I>Attentive Eraser: Unleashing Diffusion Model&#39;s Object Removal Potential via Self-Attention Redirection Guidance</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.11664-black.svg)](https://arxiv.org/abs/2412.11664)7|<I>C3oT: Generating Shorter Chain-of-Thought without Compromising Effectiveness</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.10400-black.svg)](https://arxiv.org/abs/2412.10400)7|<I>Reinforcement Learning Enhanced LLMs: A Survey</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.10319-black.svg)](https://arxiv.org/abs/2412.10319)7|<I>SCBench: A KV Cache-Centric Analysis of Long-Context Methods</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.10224-black.svg)](https://arxiv.org/abs/2412.10224)7|<I>SPT: Sequence Prompt Transformer for Interactive Image Segmentation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.10193-black.svg)](https://arxiv.org/abs/2412.10193)7|<I>Simple Guidance Mechanisms for Discrete Diffusion Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.05563-black.svg)](https://arxiv.org/abs/2412.05563)7|<I>A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.05270-black.svg)](https://arxiv.org/abs/2412.05270)7|<I>APOLLO: SGD-like Memory, AdamW-level Performance</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.04862-black.svg)](https://arxiv.org/abs/2412.04862)7|<I>EXAONE 3.5: Series of Large Language Models for Real-world Use Cases</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/e201843e-bb86-4136-9eb4-8912a5baefc6.png">](https://youtu.be/NlxT36CnCZ0)|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.04453-black.svg)](https://arxiv.org/abs/2412.04453)7|<I>NaVILA: Legged Robot Vision-Language-Action Model for Navigation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.03606-black.svg)](https://arxiv.org/abs/2412.03606)7|<I>Advanced Risk Prediction and Stability Assessment of Banks Using Time Series Transformer Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.03572-black.svg)](https://arxiv.org/abs/2412.03572)7|<I>Navigation World Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.03104-black.svg)](https://arxiv.org/abs/2412.03104)7|<I>ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.03097-black.svg)](https://arxiv.org/abs/2412.03097)7|<I>Enhancing Recommendation Systems with GNNs and Addressing Over-Smoothing</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.02732-black.svg)](https://arxiv.org/abs/2412.02732)7|<I>Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.01718-black.svg)](https://arxiv.org/abs/2412.01718)7|<I>HUGSIM: A Real-Time, Photo-Realistic and Closed-Loop Simulator for Autonomous Driving</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.01708-black.svg)](https://arxiv.org/abs/2412.01708)7|<I>Are We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.00535-black.svg)](https://arxiv.org/abs/2412.00535)7|<I>FullStack Bench: Evaluating LLMs as Full Stack Coders</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.00081-black.svg)](https://arxiv.org/abs/2412.00081)7|<I>Task Singular Vectors: Reducing Task Interference in Model Merging</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.21059-black.svg)](https://arxiv.org/abs/2412.21059)6|<I>VisionReward: Fine-Grained Multi-Dimensional Human Preference Learning for Image and Video Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.16855-black.svg)](https://arxiv.org/abs/2412.16855)6|<I>GME: Improving Universal Multimodal Retrieval by Multimodal LLMs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.16551-black.svg)](https://arxiv.org/abs/2412.16551)6|<I>Universal Machine Learning Interatomic Potentials are Ready for Phonons</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.15797-black.svg)](https://arxiv.org/abs/2412.15797)6|<I>Ensembling Large Language Models with Process Reward-Guided Tree Search for Better Complex Reasoning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.15487-black.svg)](https://arxiv.org/abs/2412.15487)6|<I>Multi-LLM Text Summarization</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.15206-black.svg)](https://arxiv.org/abs/2412.15206)6|<I>AutoTrust: Benchmarking Trustworthiness in Large Vision Language Models for Autonomous Driving</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.15194-black.svg)](https://arxiv.org/abs/2412.15194)6|<I>MMLU-CF: A Contamination-free Multi-task Language Understanding Benchmark</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.14689-black.svg)](https://arxiv.org/abs/2412.14689)6|<I>How to Synthesize Text Data without Model Collapse?</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.12441-black.svg)](https://arxiv.org/abs/2412.12441)6|<I>Numerical Pruning for Efficient Autoregressive Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.12091-black.svg)](https://arxiv.org/abs/2412.12091)6|<I>Wonderland: Navigating 3D Scenes from a Single Image</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.11990-black.svg)](https://arxiv.org/abs/2412.11990)6|<I>ExecRepoBench: Multi-level Executable Code Completion Evaluation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.11936-black.svg)](https://arxiv.org/abs/2412.11936)6|<I>A Survey of Mathematical Reasoning in the Era of Multimodal Large Language Model: Benchmark, Method &amp; Challenges</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.11605-black.svg)](https://arxiv.org/abs/2412.11605)6|<I>SPaR: Self-Play with Tree-Search Refinement to Improve Instruction-Following in Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2412.11376-black.svg)](https://arxiv.org/abs/2412.11376)6|<I>ChatTime: A Unified Multimodal Time Series Foundation Model Bridging Numerical and Textual Data</I>|not yet|

※ 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/