【2024年10月公開 Arxiv論文ランキング】2410.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10月頃に公開されたcsカテゴリの論文 (ID: 2410.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-2410.21276-black.svg)](https://arxiv.org/abs/2410.21276)604|<I>GPT-4o System Card</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.13720-black.svg)](https://arxiv.org/abs/2410.13720)155|<I>Movie Gen: A Cast of Media Foundation Models</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/20543e34-b24a-d48a-b987-677c156fffda.png">](https://youtu.be/8X8nH6p6yYQ)|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.05229-black.svg)](https://arxiv.org/abs/2410.05229)134|<I>GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/3e6717e3-3154-90c8-5e4d-11e3bba031ef.png">](https://youtu.be/zpmk2BRCtZY)|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.24164-black.svg)](https://arxiv.org/abs/2410.24164)93|<I>$\pi_0$: A Vision-Language-Action Flow Model for General Robot Control</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.02713-black.svg)](https://arxiv.org/abs/2410.02713)91|<I>Video Instruction Tuning With Synthetic Data</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.07073-black.svg)](https://arxiv.org/abs/2410.07073)82|<I>Pixtral 12B</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.13848-black.svg)](https://arxiv.org/abs/2410.13848)56|<I>Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.18982-black.svg)](https://arxiv.org/abs/2410.18982)53|<I>O1 Replication Journey: A Strategic Progress Report -- Part 1</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.07864-black.svg)](https://arxiv.org/abs/2410.07864)53|<I>RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.02073-black.svg)](https://arxiv.org/abs/2410.02073)52|<I>Depth Pro: Sharp Monocular Metric Depth in Less Than a Second</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/be57bc5a-15dc-1a0a-3068-b5ea1571cfe0.png">](https://youtu.be/myeBcDYovIY)|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.00037-black.svg)](https://arxiv.org/abs/2410.00037)52|<I>Moshi: a speech-text foundation model for real-time dialogue</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.18451-black.svg)](https://arxiv.org/abs/2410.18451)46|<I>Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.07985-black.svg)](https://arxiv.org/abs/2410.07985)46|<I>Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.03825-black.svg)](https://arxiv.org/abs/2410.03825)46|<I>MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.17725-black.svg)](https://arxiv.org/abs/2410.17725)45|<I>YOLOv11: An Overview of the Key Architectural Enhancements</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.08146-black.svg)](https://arxiv.org/abs/2410.08146)45|<I>Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.06158-black.svg)](https://arxiv.org/abs/2410.06158)43|<I>GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.05954-black.svg)](https://arxiv.org/abs/2410.05954)43|<I>Pyramidal Flow Matching for Efficient Video Generative Modeling</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/8a54b951-2b86-755c-a6ee-d5da7fa4c4ba.png">](https://youtu.be/Fi354UKT_iI)|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.02884-black.svg)](https://arxiv.org/abs/2410.02884)41|<I>LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.17434-black.svg)](https://arxiv.org/abs/2410.17434)40|<I>LongVU: Spatiotemporal Adaptive Compression for Long Video-Language Understanding</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/dd539010-8843-4eab-d021-3584e1bfb12d.png">](https://youtu.be/95KwtyxVyCw)|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.06940-black.svg)](https://arxiv.org/abs/2410.06940)39|<I>Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.05993-black.svg)](https://arxiv.org/abs/2410.05993)38|<I>Aria: An Open Multimodal Native Mixture-of-Experts Model</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.09671-black.svg)](https://arxiv.org/abs/2410.09671)37|<I>OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.04417-black.svg)](https://arxiv.org/abs/2410.04417)36|<I>SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.01560-black.svg)](https://arxiv.org/abs/2410.01560)36|<I>OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.02736-black.svg)](https://arxiv.org/abs/2410.02736)35|<I>Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.02660-black.svg)](https://arxiv.org/abs/2410.02660)35|<I>How to Train Long-Context Language Models (Effectively)</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.13863-black.svg)](https://arxiv.org/abs/2410.13863)34|<I>Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous Tokens</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/0fa7793c-f10a-b755-f304-ec4653a12e5b.png">](https://youtu.be/DkA7K9s8kLU)|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.07095-black.svg)](https://arxiv.org/abs/2410.07095)33|<I>MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/4fe533c5-75b9-d43b-c6ca-f23dc9a6c756.png">](https://youtu.be/YJV7Mx5VbQc)|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.12771-black.svg)](https://arxiv.org/abs/2410.12771)32|<I>Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.02712-black.svg)](https://arxiv.org/abs/2410.02712)30|<I>LLaVA-Critic: Learning to Evaluate Multimodal Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.13126-black.svg)](https://arxiv.org/abs/2410.13126)29|<I>ALOHA Unleashed: A Simple Recipe for Robot Dexterity</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.10819-black.svg)](https://arxiv.org/abs/2410.10819)29|<I>DuoAttention: Efficient Long-Context LLM Inference with Retrieval and Streaming Heads</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/7d4f3bf3-0be2-7716-94ca-25f1acf2e9a5.png">](https://youtu.be/oT8FIpAO9qs)|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.10629-black.svg)](https://arxiv.org/abs/2410.10629)29|<I>SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.10812-black.svg)](https://arxiv.org/abs/2410.10812)28|<I>HART: Efficient Visual Generation with Hybrid Autoregressive Transformer</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.10733-black.svg)](https://arxiv.org/abs/2410.10733)28|<I>Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.11831-black.svg)](https://arxiv.org/abs/2410.11831)27|<I>CoTracker3: Simpler and Better Point Tracking by Pseudo-Labelling Real Videos</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.11190-black.svg)](https://arxiv.org/abs/2410.11190)27|<I>Mini-Omni2: Towards Open-source GPT-4o with Vision, Speech and Duplex Capabilities</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.09024-black.svg)](https://arxiv.org/abs/2410.09024)27|<I>AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.05258-black.svg)](https://arxiv.org/abs/2410.05258)27|<I>Differential Transformer</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/d5f1eb56-076d-dee6-0232-52bc83812f47.png">](https://youtu.be/nQ-M1pktBxo)|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.22570-black.svg)](https://arxiv.org/abs/2410.22570)26|<I>Orb: A Fast, Scalable Neural Network Potential</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.18647-black.svg)](https://arxiv.org/abs/2410.18647)26|<I>Data Scaling Laws in Imitation Learning for Robotic Manipulation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.17247-black.svg)](https://arxiv.org/abs/2410.17247)26|<I>PyramidDrop: Accelerating Your Large Vision-Language Models via Pyramid Visual Redundancy Reduction</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.06885-black.svg)](https://arxiv.org/abs/2410.06885)26|<I>F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/87fd0917-da74-2d88-9754-ed765fed5242.png">](https://youtu.be/QcN8WurZl_c)|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.02089-black.svg)](https://arxiv.org/abs/2410.02089)25|<I>RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/c95910a5-db0a-ce1d-550c-76a4a06db65f.png">](https://youtu.be/rhnBzk8RE-0)|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.01257-black.svg)](https://arxiv.org/abs/2410.01257)25|<I>HelpSteer2-Preference: Complementing Ratings with Preferences</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.19732-black.svg)](https://arxiv.org/abs/2410.19732)24|<I>Rethinking Visual Dependency in Long-Context Reasoning for Large Vision-Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.11081-black.svg)](https://arxiv.org/abs/2410.11081)23|<I>Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/75de0734-af1b-1365-7477-cfab30679db6.png">](https://youtu.be/2vyqNQ0d7aw)|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.10792-black.svg)](https://arxiv.org/abs/2410.10792)23|<I>Semantic Image Inversion and Editing using Rectified Stochastic Differential Equations</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.02757-black.svg)](https://arxiv.org/abs/2410.02757)23|<I>Loong: Generating Minute-level Long Videos with Autoregressive Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.02694-black.svg)](https://arxiv.org/abs/2410.02694)23|<I>HELMET: How to Evaluate Long-Context Language Models Effectively and Thoroughly</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.10803-black.svg)](https://arxiv.org/abs/2410.10803)22|<I>Generalizable Humanoid Manipulation with 3D Diffusion Policies</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.10165-black.svg)](https://arxiv.org/abs/2410.10165)22|<I>HSR-Enhanced Sparse Attention Acceleration</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.05243-black.svg)](https://arxiv.org/abs/2410.05243)22|<I>Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.00083-black.svg)](https://arxiv.org/abs/2410.00083)22|<I>A Survey on Diffusion Models for Inverse Problems</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.10989-black.svg)](https://arxiv.org/abs/2410.10989)21|<I>Liger Kernel: Efficient Triton Kernels for LLM Training</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.10934-black.svg)](https://arxiv.org/abs/2410.10934)21|<I>Agent-as-a-Judge: Evaluate Agents with Agents</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/4768c3e3-e576-eab2-04b4-6b9f3bcb6fce.png">](https://youtu.be/53fmCDMsbtM)|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.10762-black.svg)](https://arxiv.org/abs/2410.10762)21|<I>AFlow: Automating Agentic Workflow Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.09375-black.svg)](https://arxiv.org/abs/2410.09375)21|<I>Looped ReLU MLPs May Be All You Need as Practical Programmable Computers</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.08565-black.svg)](https://arxiv.org/abs/2410.08565)21|<I>Baichuan-Omni Technical Report</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.13862-black.svg)](https://arxiv.org/abs/2410.13862)20|<I>DepthSplat: Connecting Gaussian Splatting and Depth</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.13639-black.svg)](https://arxiv.org/abs/2410.13639)20|<I>A Comparative Study on Reasoning Patterns of OpenAI&#39;s o1 Model</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.05779-black.svg)](https://arxiv.org/abs/2410.05779)20|<I>LightRAG: Simple and Fast Retrieval-Augmented Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.01756-black.svg)](https://arxiv.org/abs/2410.01756)20|<I>ImageFolder: Autoregressive Image Generation with Folded Tokens</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.16198-black.svg)](https://arxiv.org/abs/2410.16198)19|<I>Improve Vision Language Model Chain-of-thought Reasoning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.15458-black.svg)](https://arxiv.org/abs/2410.15458)19|<I>Allegro: Open the Black Box of Commercial-Level Video Generation Model</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/0a3f8f72-3646-aafc-43a8-4147f768a746.png">](https://youtu.be/T8mhgZHtNv8)|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.12832-black.svg)](https://arxiv.org/abs/2410.12832)19|<I>Generative Reward Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.12784-black.svg)](https://arxiv.org/abs/2410.12784)19|<I>JudgeBench: A Benchmark for Evaluating LLM-based Judges</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.10594-black.svg)](https://arxiv.org/abs/2410.10594)19|<I>VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.08202-black.svg)](https://arxiv.org/abs/2410.08202)19|<I>Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-training</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.05160-black.svg)](https://arxiv.org/abs/2410.05160)19|<I>VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.04840-black.svg)](https://arxiv.org/abs/2410.04840)19|<I>Strong Model Collapse</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/ec5bb4d8-15b6-6bc4-32c2-387e17a5e244.png">](https://youtu.be/uyO58ZJZT60)|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.01943-black.svg)](https://arxiv.org/abs/2410.01943)19|<I>CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.24207-black.svg)](https://arxiv.org/abs/2410.24207)18|<I>No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.21465-black.svg)](https://arxiv.org/abs/2410.21465)18|<I>ShadowKV: KV Cache in Shadows for High-Throughput Long-Context LLM Inference</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.19168-black.svg)](https://arxiv.org/abs/2410.19168)18|<I>MMAU: A Massive Multi-Task Audio Understanding and Reasoning Benchmark</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.17038-black.svg)](https://arxiv.org/abs/2410.17038)18|<I>Performance of the CMS high-level trigger during LHC Run 2</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.12896-black.svg)](https://arxiv.org/abs/2410.12896)18|<I>A Survey on Data Synthesis and Augmentation for Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.10818-black.svg)](https://arxiv.org/abs/2410.10818)18|<I>TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.09397-black.svg)](https://arxiv.org/abs/2410.09397)18|<I>Fine-grained Attention I/O Complexity: Comprehensive Analysis for Backward Passes</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.08159-black.svg)](https://arxiv.org/abs/2410.08159)18|<I>DART: Denoising Autoregressive Transformer for Scalable Text-to-Image Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.07176-black.svg)](https://arxiv.org/abs/2410.07176)18|<I>Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/1ccb6cbe-e5bf-8c2f-9cd8-efaf24ba2422.png">](https://youtu.be/_xOVtx-zs3k)|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.23262-black.svg)](https://arxiv.org/abs/2410.23262)17|<I>EMMA: End-to-End Multimodal Model for Autonomous Driving</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.13928-black.svg)](https://arxiv.org/abs/2410.13928)17|<I>Automatically Interpreting Millions of Features in Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.11758-black.svg)](https://arxiv.org/abs/2410.11758)17|<I>Latent Action Pretraining from Videos</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/c8c8f874-b937-97db-56fe-e2d2c558be7d.png">](https://youtu.be/XKR6n_1Xo5M)|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.11261-black.svg)](https://arxiv.org/abs/2410.11261)17|<I>Beyond Linear Approximations: A Novel Pruning Approach for Attention Matrix</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.10781-black.svg)](https://arxiv.org/abs/2410.10781)17|<I>When Attention Sink Emerges in Language Models: An Empirical View</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.08261-black.svg)](https://arxiv.org/abs/2410.08261)17|<I>Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2410.05080-black.svg)](https://arxiv.org/abs/2410.05080)17|<I>ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery</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/