【2024年6月公開 Arxiv論文ランキング】2406.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6月頃に公開されたcsカテゴリの論文 (ID: 2406.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。
(2024年12月9日更新)
   
| 被引用数　　|タイトル|動画|
|------:|:--------|:------------|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.12793-black.svg)](https://arxiv.org/abs/2406.12793)146|<I>ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.09246-black.svg)](https://arxiv.org/abs/2406.09246)106|<I>OpenVLA: An Open-Source Vision-Language-Action Model</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.16860-black.svg)](https://arxiv.org/abs/2406.16860)93|<I>Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.11931-black.svg)](https://arxiv.org/abs/2406.11931)87|<I>DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.09414-black.svg)](https://arxiv.org/abs/2406.09414)79|<I>Depth Anything V2</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.17557-black.svg)](https://arxiv.org/abs/2406.17557)77|<I>The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.06525-black.svg)](https://arxiv.org/abs/2406.06525)76|<I>Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.01574-black.svg)](https://arxiv.org/abs/2406.01574)74|<I>MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.07476-black.svg)](https://arxiv.org/abs/2406.07476)71|<I>VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.00515-black.svg)](https://arxiv.org/abs/2406.00515)61|<I>A Survey on Large Language Models for Code Generation</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/64ebc289-029f-447f-19cb-b776e1a9c9c7.png">](https://youtu.be/hG-O_sqVl_k)|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.11704-black.svg)](https://arxiv.org/abs/2406.11704)59|<I>Nemotron-4 340B Technical Report</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.11939-black.svg)](https://arxiv.org/abs/2406.11939)58|<I>From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.04093-black.svg)](https://arxiv.org/abs/2406.04093)57|<I>Scaling and evaluating sparse autoencoders</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.04325-black.svg)](https://arxiv.org/abs/2406.04325)52|<I>ShareGPT4Video: Improving Video Understanding and Generation with Better Captions</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.12845-black.svg)](https://arxiv.org/abs/2406.12845)51|<I>Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.02918-black.svg)](https://arxiv.org/abs/2406.02918)49|<I>U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.13155-black.svg)](https://arxiv.org/abs/2406.13155)47|<I>Convolutional Kolmogorov-Arnold Networks</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.11838-black.svg)](https://arxiv.org/abs/2406.11838)46|<I>Autoregressive Image Generation without Vector Quantization</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.06608-black.svg)](https://arxiv.org/abs/2406.06608)45|<I>The Prompt Report: A Systematic Survey of Prompting Techniques</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/c2ec9761-6e5e-7deb-7149-251d4ed448d4.png">](https://youtu.be/ww1IecF1NI8)|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.11717-black.svg)](https://arxiv.org/abs/2406.11717)44|<I>Refusal in Language Models Is Mediated by a Single Direction</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.08464-black.svg)](https://arxiv.org/abs/2406.08464)41|<I>Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.11794-black.svg)](https://arxiv.org/abs/2406.11794)40|<I>DataComp-LM: In search of the next generation of training sets for language models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.16852-black.svg)](https://arxiv.org/abs/2406.16852)39|<I>Long Context Transfer from Language to Vision</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.15877-black.svg)](https://arxiv.org/abs/2406.15877)39|<I>BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.11409-black.svg)](https://arxiv.org/abs/2406.11409)38|<I>CodeGemma: Open Code Models Based on Gemma</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.04313-black.svg)](https://arxiv.org/abs/2406.04313)38|<I>Improving Alignment and Robustness with Circuit Breakers</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.20094-black.svg)](https://arxiv.org/abs/2406.20094)36|<I>Scaling Synthetic Data Creation with 1,000,000,000 Personas</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.08673-black.svg)](https://arxiv.org/abs/2406.08673)36|<I>HelpSteer2: Open-source dataset for training top-performing reward models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.06592-black.svg)](https://arxiv.org/abs/2406.06592)34|<I>Improve Mathematical Reasoning in Language Models by Automated Process Supervision</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.07887-black.svg)](https://arxiv.org/abs/2406.07887)33|<I>An Empirical Study of Mamba-based Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.02496-black.svg)](https://arxiv.org/abs/2406.02496)33|<I>Kolmogorov-Arnold Networks for Time Series: Bridging Predictive Power and Interpretability</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.01465-black.svg)](https://arxiv.org/abs/2406.01465)33|<I>AIFS -- ECMWF&#39;s data-driven forecasting system</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.12199-black.svg)](https://arxiv.org/abs/2406.12199)32|<I>Time Series Modeling for Heart Rate Prediction: From ARIMA to Transformers</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.04692-black.svg)](https://arxiv.org/abs/2406.04692)31|<I>Mixture-of-Agents Enhances Large Language Model Capabilities</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/fa72a4c3-ddfc-eb76-aa6b-dc8797909623.png">](https://youtu.be/IBnqYJ7Gv5Y)|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.07456-black.svg)](https://arxiv.org/abs/2406.07456)30|<I>fKAN: Fractional Kolmogorov-Arnold Networks with trainable Jacobi basis functions</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.02430-black.svg)](https://arxiv.org/abs/2406.02430)30|<I>Seed-TTS: A Family of High-Quality Versatile Speech Generation Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.06470-black.svg)](https://arxiv.org/abs/2406.06470)29|<I>GKAN: Graph Kolmogorov-Arnold Networks</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.02486-black.svg)](https://arxiv.org/abs/2406.02486)29|<I>A Temporal Kolmogorov-Arnold Transformer for Time Series Forecasting</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.01034-black.svg)](https://arxiv.org/abs/2406.01034)29|<I>FourierKAN-GCF: Fourier Kolmogorov-Arnold Network -- An Effective and Efficient Feature Transformation for Graph Collaborative Filtering</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.15126-black.svg)](https://arxiv.org/abs/2406.15126)28|<I>On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.19314-black.svg)](https://arxiv.org/abs/2406.19314)27|<I>LiveBench: A Challenging, Contamination-Free LLM Benchmark</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.08801-black.svg)](https://arxiv.org/abs/2406.08801)27|<I>Hallo: Hierarchical Audio-Driven Visual Synthesis for Portrait Image Animation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.11903-black.svg)](https://arxiv.org/abs/2406.11903)26|<I>A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.07550-black.svg)](https://arxiv.org/abs/2406.07550)26|<I>An Image is Worth 32 Tokens for Reconstruction and Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.06521-black.svg)](https://arxiv.org/abs/2406.06521)26|<I>PGSR: Planar-based Gaussian Splatting for Efficient and High-Fidelity Surface Reconstruction</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.05946-black.svg)](https://arxiv.org/abs/2406.05946)26|<I>Safety Alignment Should Be Made More Than Just a Few Tokens Deep</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.05370-black.svg)](https://arxiv.org/abs/2406.05370)26|<I>VALL-E 2: Neural Codec Language Models are Human Parity Zero-Shot Text to Speech Synthesizers</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.04770-black.svg)](https://arxiv.org/abs/2406.04770)26|<I>WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.04264-black.svg)](https://arxiv.org/abs/2406.04264)26|<I>MLVU: A Comprehensive Benchmark for Multi-Task Long Video Understanding</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.03816-black.svg)](https://arxiv.org/abs/2406.03816)26|<I>ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.00600-black.svg)](https://arxiv.org/abs/2406.00600)26|<I>Kolmogorov-Arnold Network for Satellite Image Classification in Remote Sensing</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.13113-black.svg)](https://arxiv.org/abs/2406.13113)25|<I>CU-Net: a U-Net architecture for efficient brain-tumor segmentation on BraTS 2019 dataset</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.07522-black.svg)](https://arxiv.org/abs/2406.07522)25|<I>Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.14449-black.svg)](https://arxiv.org/abs/2406.14449)24|<I>APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.02061-black.svg)](https://arxiv.org/abs/2406.02061)24|<I>Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/e7742a7f-322c-b288-e6c8-e5c15c5e14ea.png">](https://youtu.be/Y5uFjkilarI)|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.18629-black.svg)](https://arxiv.org/abs/2406.18629)23|<I>Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.18380-black.svg)](https://arxiv.org/abs/2406.18380)23|<I>KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.16554-black.svg)](https://arxiv.org/abs/2406.16554)23|<I>LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-training</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.09411-black.svg)](https://arxiv.org/abs/2406.09411)23|<I>MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.04405-black.svg)](https://arxiv.org/abs/2406.04405)23|<I>Streamlining and standardizing software citations with The Software Citation Station</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.02509-black.svg)](https://arxiv.org/abs/2406.02509)23|<I>CamCo: Camera-Controllable 3D-Consistent Image-to-Video Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.10149-black.svg)](https://arxiv.org/abs/2406.10149)22|<I>BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.07524-black.svg)](https://arxiv.org/abs/2406.07524)22|<I>Simple and Effective Masked Diffusion Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.04744-black.svg)](https://arxiv.org/abs/2406.04744)22|<I>CRAG -- Comprehensive RAG Benchmark</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.19680-black.svg)](https://arxiv.org/abs/2406.19680)21|<I>MimicMotion: High-Quality Human Motion Video Generation with Confidence-aware Pose Guidance</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.18403-black.svg)](https://arxiv.org/abs/2406.18403)21|<I>LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.14598-black.svg)](https://arxiv.org/abs/2406.14598)21|<I>SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal Behaviors</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.09087-black.svg)](https://arxiv.org/abs/2406.09087)21|<I>Suitability of KANs for Computer Vision: A preliminary investigation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.07496-black.svg)](https://arxiv.org/abs/2406.07496)21|<I>TextGrad: Automatic &#34;Differentiation&#34; via Text</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.04904-black.svg)](https://arxiv.org/abs/2406.04904)21|<I>XTTS: a Massively Multilingual Zero-Shot Text-to-Speech Model</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.04329-black.svg)](https://arxiv.org/abs/2406.04329)21|<I>Simplified and Generalized Masked Diffusion for Discrete Data</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.03733-black.svg)](https://arxiv.org/abs/2406.03733)21|<I>Credit Card Fraud Detection Using Advanced Transformer Model</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.02900-black.svg)](https://arxiv.org/abs/2406.02900)21|<I>Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.02543-black.svg)](https://arxiv.org/abs/2406.02543)21|<I>To Believe or Not to Believe Your LLM</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/d0a9b450-af46-c5a5-8d5f-d323748bf5fc.png">](https://youtu.be/QTXB3dhLgq0)|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.02069-black.svg)](https://arxiv.org/abs/2406.02069)21|<I>PyramidKV: Dynamic KV Cache Compression based on Pyramidal Information Funneling</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.01171-black.svg)](https://arxiv.org/abs/2406.01171)21|<I>Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.19226-black.svg)](https://arxiv.org/abs/2406.19226)20|<I>Simulating Classroom Education with LLM-Empowered Agents</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.18495-black.svg)](https://arxiv.org/abs/2406.18495)20|<I>WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.13121-black.svg)](https://arxiv.org/abs/2406.13121)20|<I>Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.04520-black.svg)](https://arxiv.org/abs/2406.04520)20|<I>NATURAL PLAN: Benchmarking LLMs on Natural Language Planning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.01900-black.svg)](https://arxiv.org/abs/2406.01900)20|<I>Follow-Your-Emoji: Fine-Controllable and Expressive Freestyle Portrait Animation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.01304-black.svg)](https://arxiv.org/abs/2406.01304)20|<I>CodeR: Issue Resolving with Multi-Agent and Task Graphs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.00083-black.svg)](https://arxiv.org/abs/2406.00083)20|<I>BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.14916-black.svg)](https://arxiv.org/abs/2406.14916)19|<I>Demonstrating the Efficacy of Kolmogorov-Arnold Networks in Vision Tasks</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.14532-black.svg)](https://arxiv.org/abs/2406.14532)19|<I>RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.10454-black.svg)](https://arxiv.org/abs/2406.10454)19|<I>HumanPlus: Humanoid Shadowing and Imitation from Humans</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.10216-black.svg)](https://arxiv.org/abs/2406.10216)19|<I>Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.09756-black.svg)](https://arxiv.org/abs/2406.09756)19|<I>Grounding Image Matching in 3D with MASt3R</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.04449-black.svg)](https://arxiv.org/abs/2406.04449)19|<I>MAIRA-2: Grounded Radiology Report Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.04127-black.svg)](https://arxiv.org/abs/2406.04127)19|<I>Are We Done with MMLU?</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.02511-black.svg)](https://arxiv.org/abs/2406.02511)19|<I>V-Express: Conditional Dropout for Progressive Training of Portrait Video Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.18665-black.svg)](https://arxiv.org/abs/2406.18665)18|<I>RouteLLM: Learning to Route LLMs with Preference Data</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/72f99b03-fbc3-d4a3-0246-4614a6605aac.png">](https://youtu.be/XJLBNJIz8wI)|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.15319-black.svg)](https://arxiv.org/abs/2406.15319)18|<I>LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.14495-black.svg)](https://arxiv.org/abs/2406.14495)18|<I>rKAN: Rational Kolmogorov-Arnold Networks</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.11173-black.svg)](https://arxiv.org/abs/2406.11173)18|<I>BSRBF-KAN: A combination of B-splines and Radial Basis Functions in Kolmogorov-Arnold Networks</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.07478-black.svg)](https://arxiv.org/abs/2406.07478)18|<I>Incompressibility and spectral gaps of random circuits</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.07394-black.svg)](https://arxiv.org/abs/2406.07394)18|<I>Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.19662-black.svg)](https://arxiv.org/abs/2406.19662)17|<I>Finite basis Kolmogorov-Arnold networks: domain decomposition for data-driven and physics-informed problems</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.18583-black.svg)](https://arxiv.org/abs/2406.18583)17|<I>Lumina-Next: Making Lumina-T2X Stronger and Faster with Next-DiT</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2406.17294-black.svg)](https://arxiv.org/abs/2406.17294)17|<I>Math-LLaVA: Bootstrapping Mathematical Reasoning for Multimodal Large Language Models</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/