【2024年11月公開 Arxiv論文ランキング】2411.xxxxx

# AI論文解説 Youtubeチャンネル: AI時代の羅針盤 

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2024年11月頃に公開されたcsカテゴリの論文 (ID: 2411.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-2411.15124-black.svg)](https://arxiv.org/abs/2411.15124)83|<I>Tulu 3: Pushing Frontiers in Open Language Model Post-Training</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.15594-black.svg)](https://arxiv.org/abs/2411.15594)72|<I>A Survey on LLM-as-a-Judge</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.10440-black.svg)](https://arxiv.org/abs/2411.10440)58|<I>LLaVA-CoT: Let Vision Language Models Reason Step-by-Step</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/9465b0a6-1a67-3f0d-07cd-7ee82c921923.png">](https://youtu.be/YcM4wmfGc9E)|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.16594-black.svg)](https://arxiv.org/abs/2411.16594)48|<I>From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.10109-black.svg)](https://arxiv.org/abs/2411.10109)45|<I>Generative Agent Simulations of 1,000 People</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/e036d058-c564-58eb-deeb-9479efc70d53.png">](https://youtu.be/JG0iWFdGjCU)|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.14405-black.svg)](https://arxiv.org/abs/2411.14405)42|<I>Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/27b7267f-899d-515e-6a5a-b62666a13ea0.png">](https://youtu.be/wQb8OWApeuA)|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.04872-black.svg)](https://arxiv.org/abs/2411.04872)40|<I>FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/db0766fd-70ac-3d9e-37cc-0f72363aa3c4.png">](https://youtu.be/u4nTudEg1EI)|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.16489-black.svg)](https://arxiv.org/abs/2411.16489)30|<I>O1 Replication Journey -- Part 2: Surpassing O1-preview through Simple Distillation, Big Progress or Bitter Lesson?</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.04368-black.svg)](https://arxiv.org/abs/2411.04368)30|<I>Measuring short-form factuality in large language models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.10442-black.svg)](https://arxiv.org/abs/2411.10442)29|<I>Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.15098-black.svg)](https://arxiv.org/abs/2411.15098)27|<I>OminiControl: Minimal and Universal Control for Diffusion Transformer</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.11822-black.svg)](https://arxiv.org/abs/2411.11822)27|<I>Logical computation demonstrated with a neutral atom quantum processor</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.00776-black.svg)](https://arxiv.org/abs/2411.00776)26|<I>Randomized Autoregressive Visual Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.04905-black.svg)](https://arxiv.org/abs/2411.04905)25|<I>OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.02385-black.svg)](https://arxiv.org/abs/2411.02385)25|<I>How Far is Video Generation from World Model: A Physical Law Perspective</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/c9988335-5b0e-cdcb-8b5b-7e94f544edbe.png">](https://youtu.be/LXX6KTyPf-Y)|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.12372-black.svg)](https://arxiv.org/abs/2411.12372)24|<I>RedPajama: an Open Dataset for Training Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.00774-black.svg)](https://arxiv.org/abs/2411.00774)24|<I>Freeze-Omni: A Smart and Low Latency Speech-to-speech Dialogue Model with Frozen LLM</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.10541-black.svg)](https://arxiv.org/abs/2411.10541)22|<I>Does Prompt Formatting Have Any Impact on LLM Performance?</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.04330-black.svg)](https://arxiv.org/abs/2411.04330)22|<I>Scaling Laws for Precision</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/a57dc168-3282-5dea-2e9d-3de563bf4c7e.png">](https://youtu.be/BRgGt2747ZE)|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.11694-black.svg)](https://arxiv.org/abs/2411.11694)21|<I>Enhancing LLM Reasoning with Reward-guided Tree Search</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.07602-black.svg)](https://arxiv.org/abs/2411.07602)21|<I>Circuit Complexity Bounds for RoPE-based Transformer Architecture</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.13503-black.svg)](https://arxiv.org/abs/2411.13503)20|<I>VBench++: Comprehensive and Versatile Benchmark Suite for Video Generative Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.10406-black.svg)](https://arxiv.org/abs/2411.10406)20|<I>How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.04468-black.svg)](https://arxiv.org/abs/2411.04468)19|<I>Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/b62bbc77-4a4a-6706-1db3-c404a18e0594.png">](https://youtu.be/cp_Xq3zv1-0)|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.19666-black.svg)](https://arxiv.org/abs/2411.19666)18|<I>Multimodal Whole Slide Foundation Model for Pathology</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.17451-black.svg)](https://arxiv.org/abs/2411.17451)18|<I>VLRewardBench: A Challenging Benchmark for Vision-Language Generative Reward Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.05007-black.svg)](https://arxiv.org/abs/2411.05007)18|<I>SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.04746-black.svg)](https://arxiv.org/abs/2411.04746)18|<I>Taming Rectified Flow for Inversion and Editing</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.18613-black.svg)](https://arxiv.org/abs/2411.18613)17|<I>CAT4D: Create Anything in 4D with Multi-View Video Diffusion Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.17440-black.svg)](https://arxiv.org/abs/2411.17440)17|<I>Identity-Preserving Text-to-Video Generation by Frequency Decomposition</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.16579-black.svg)](https://arxiv.org/abs/2411.16579)17|<I>Enhancing LLM Reasoning via Critique Models with Test-Time and Training-Time Supervision</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.14432-black.svg)](https://arxiv.org/abs/2411.14432)17|<I>Insight-V: Exploring Long-Chain Visual Reasoning with Multimodal Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.13577-black.svg)](https://arxiv.org/abs/2411.13577)17|<I>WavChat: A Survey of Spoken Dialogue Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.07140-black.svg)](https://arxiv.org/abs/2411.07140)17|<I>Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.04998-black.svg)](https://arxiv.org/abs/2411.04998)17|<I>HourVideo: 1-Hour Video-Language Understanding</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.02265-black.svg)](https://arxiv.org/abs/2411.02265)17|<I>Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.18279-black.svg)](https://arxiv.org/abs/2411.18279)16|<I>Large Language Model-Brained GUI Agents: A Survey</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.17522-black.svg)](https://arxiv.org/abs/2411.17522)16|<I>On Statistical Rates of Conditional Diffusion Transformers: Approximation, Estimation and Minimax Optimality</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.04928-black.svg)](https://arxiv.org/abs/2411.04928)16|<I>DimensionX: Create Any 3D and 4D Scenes from a Single Image with Controllable Video Diffusion</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.10414-black.svg)](https://arxiv.org/abs/2411.10414)15|<I>Llama Guard 3 Vision: Safeguarding Human-AI Image Understanding Conversations</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.06326-black.svg)](https://arxiv.org/abs/2411.06326)15|<I>Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.03336-black.svg)](https://arxiv.org/abs/2411.03336)15|<I>Towards evaluations-based safety cases for AI scheming</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.00027-black.svg)](https://arxiv.org/abs/2411.00027)15|<I>Personalization of Large Language Models: A Survey</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.16646-black.svg)](https://arxiv.org/abs/2411.16646)14|<I>Self-Generated Critiques Boost Reward Modeling for Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.16525-black.svg)](https://arxiv.org/abs/2411.16525)14|<I>Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.13676-black.svg)](https://arxiv.org/abs/2411.13676)14|<I>Hymba: A Hybrid-head Architecture for Small Language Models</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/98ef50f1-a0cf-45e8-ba8b-d57fffe43591.png">](https://youtu.be/CI8VqLoNVh8)|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.07279-black.svg)](https://arxiv.org/abs/2411.07279)14|<I>The Surprising Effectiveness of Test-Time Training for Few-Shot Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.15114-black.svg)](https://arxiv.org/abs/2411.15114)13|<I>RE-Bench: Evaluating frontier AI R&amp;D capabilities of language model agents against human experts</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.14386-black.svg)](https://arxiv.org/abs/2411.14386)13|<I>Learning Humanoid Locomotion with Perceptive Internal Model</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.14199-black.svg)](https://arxiv.org/abs/2411.14199)13|<I>OpenScholar: Synthesizing Scientific Literature with Retrieval-augmented LMs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.10958-black.svg)](https://arxiv.org/abs/2411.10958)13|<I>SageAttention2: Efficient Attention with Thorough Outlier Smoothing and Per-thread INT4 Quantization</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.06374-black.svg)](https://arxiv.org/abs/2411.06374)13|<I>Metric Learning for Tag Recommendation: Tackling Data Sparsity and Cold Start Issues</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.03350-black.svg)](https://arxiv.org/abs/2411.03350)13|<I>A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.02193-black.svg)](https://arxiv.org/abs/2411.02193)13|<I>Improving Steering Vectors by Targeting Sparse Autoencoder Features</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.17501-black.svg)](https://arxiv.org/abs/2411.17501)12|<I>Inference Scaling fLaws: The Limits of LLM Resampling with Imperfect Verifiers</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.15421-black.svg)](https://arxiv.org/abs/2411.15421)12|<I>OphCLIP: Hierarchical Retrieval-Augmented Learning for Ophthalmic Surgical Video-Language Pretraining</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.14347-black.svg)](https://arxiv.org/abs/2411.14347)12|<I>DINO-X: A Unified Vision Model for Open-World Object Detection and Understanding</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/09c8db67-361a-4c68-b714-5b02c141213d.png">](https://youtu.be/7Aofpq9-tlM)|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.12151-black.svg)](https://arxiv.org/abs/2411.12151)12|<I>Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.08599-black.svg)](https://arxiv.org/abs/2411.08599)12|<I>A Preview of XiYan-SQL: A Multi-Generator Ensemble Framework for Text-to-SQL</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.08088-black.svg)](https://arxiv.org/abs/2411.08088)12|<I>Safety case template for frontier AI: A cyber inability argument</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.07975-black.svg)](https://arxiv.org/abs/2411.07975)12|<I>JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified Multimodal Understanding and Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.03286-black.svg)](https://arxiv.org/abs/2411.03286)12|<I>DiT4Edit: Diffusion Transformer for Image Editing</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.02293-black.svg)](https://arxiv.org/abs/2411.02293)12|<I>Hunyuan3D 1.0: A Unified Framework for Text-to-3D and Image-to-3D Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.02038-black.svg)](https://arxiv.org/abs/2411.02038)12|<I>Addressing Representation Collapse in Vector Quantized Models with One Linear Layer</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.00855-black.svg)](https://arxiv.org/abs/2411.00855)12|<I>Vision-Language Models Can Self-Improve Reasoning via Reflection</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.00836-black.svg)](https://arxiv.org/abs/2411.00836)12|<I>DynaMath: A Dynamic Visual Benchmark for Evaluating Mathematical Reasoning Robustness of Vision Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.00820-black.svg)](https://arxiv.org/abs/2411.00820)12|<I>AutoGLM: Autonomous Foundation Agents for GUIs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.00566-black.svg)](https://arxiv.org/abs/2411.00566)12|<I>PatternBoost: Constructions in Mathematics with a Little Help from AI</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.19943-black.svg)](https://arxiv.org/abs/2411.19943)11|<I>Critical Tokens Matter: Token-Level Contrastive Estimation Enhances LLM&#39;s Reasoning Capability</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.17607-black.svg)](https://arxiv.org/abs/2411.17607)11|<I>Scaling Speech-Text Pre-training with Synthetic Interleaved Data</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.16567-black.svg)](https://arxiv.org/abs/2411.16567)11|<I>Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.16508-black.svg)](https://arxiv.org/abs/2411.16508)11|<I>All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.15598-black.svg)](https://arxiv.org/abs/2411.15598)11|<I>Optimizing Gesture Recognition for Seamless UI Interaction Using Convolutional Neural Networks</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.15195-black.svg)](https://arxiv.org/abs/2411.15195)11|<I>Graph Neural Network-Based Entity Extraction and Relationship Reasoning in Complex Knowledge Graphs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.11581-black.svg)](https://arxiv.org/abs/2411.11581)11|<I>OASIS: Open Agent Social Interaction Simulations with One Million Agents</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.10803-black.svg)](https://arxiv.org/abs/2411.10803)11|<I>Multi-Stage Vision Token Dropping: Towards Efficient Multimodal Large Language Model</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.05003-black.svg)](https://arxiv.org/abs/2411.05003)11|<I>ReCapture: Generative Video Camera Controls for User-Provided Videos using Masked Video Fine-Tuning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.02571-black.svg)](https://arxiv.org/abs/2411.02571)11|<I>MM-Embed: Universal Multimodal Retrieval with Multimodal LLMs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.02310-black.svg)](https://arxiv.org/abs/2411.02310)11|<I>MdEval: Massively Multilingual Code Debugging</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.01111-black.svg)](https://arxiv.org/abs/2411.01111)11|<I>Rule Based Rewards for Language Model Safety</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.00860-black.svg)](https://arxiv.org/abs/2411.00860)11|<I>Survey of Cultural Awareness in Language Models: Text and Beyond</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.00640-black.svg)](https://arxiv.org/abs/2411.00640)11|<I>Adding Error Bars to Evals: A Statistical Approach to Language Model Evaluations</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.19939-black.svg)](https://arxiv.org/abs/2411.19939)10|<I>VLSBench: Unveiling Visual Leakage in Multimodal Safety</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.19799-black.svg)](https://arxiv.org/abs/2411.19799)10|<I>INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.19650-black.svg)](https://arxiv.org/abs/2411.19650)10|<I>CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.19443-black.svg)](https://arxiv.org/abs/2411.19443)10|<I>Auto-RAG: Autonomous Retrieval-Augmented Generation for Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.18622-black.svg)](https://arxiv.org/abs/2411.18622)10|<I>Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Data</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.17465-black.svg)](https://arxiv.org/abs/2411.17465)10|<I>ShowUI: One Vision-Language-Action Model for GUI Visual Agent</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.12161-black.svg)](https://arxiv.org/abs/2411.12161)10|<I>Adaptive Cache Management for Complex Storage Systems Using CNN-LSTM-Based Spatiotemporal Prediction</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.11922-black.svg)](https://arxiv.org/abs/2411.11922)10|<I>SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/33a2cc92-0d04-afe9-3b29-34e0064d7302.png">](https://youtu.be/LlKfrk0rIo4)|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.11708-black.svg)](https://arxiv.org/abs/2411.11708)10|<I>High-fidelity universal gates in the $^{171}$Yb ground state nuclear spin qubit</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.11285-black.svg)](https://arxiv.org/abs/2411.11285)10|<I>Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.10323-black.svg)](https://arxiv.org/abs/2411.10323)10|<I>The Dawn of GUI Agent: A Preliminary Case Study with Claude 3.5 Computer Use</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.09947-black.svg)](https://arxiv.org/abs/2411.09947)10|<I>LoRA-LiteE: A Computationally Efficient Framework for Chatbot Preference-Tuning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2411.07763-black.svg)](https://arxiv.org/abs/2411.07763)10|<I>Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows</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/