【2024年7月公開 Arxiv論文ランキング】2407.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7月頃に公開されたcsカテゴリの論文 (ID: 2407.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-2407.21783-black.svg)](https://arxiv.org/abs/2407.21783)1751|<I>The Llama 3 Herd of Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.10671-black.svg)](https://arxiv.org/abs/2407.10671)558|<I>Qwen2 Technical Report</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/a864acd2-6127-f424-cd30-b259a0c9e267.png">](https://youtu.be/O9YBy0rmSFo)|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.07726-black.svg)](https://arxiv.org/abs/2407.07726)65|<I>PaliGemma: A versatile 3B VLM for transfer</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.07895-black.svg)](https://arxiv.org/abs/2407.07895)64|<I>LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.21787-black.svg)](https://arxiv.org/abs/2407.21787)57|<I>Large Language Monkeys: Scaling Inference Compute with Repeated Sampling</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/55ae1b6d-c510-ad32-4798-9e0808cb3427.png">](https://youtu.be/Q8u2Fwvk3WE)|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.10759-black.svg)](https://arxiv.org/abs/2407.10759)50|<I>Qwen2-Audio Technical Report</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.03320-black.svg)](https://arxiv.org/abs/2407.03320)46|<I>InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.07754-black.svg)](https://arxiv.org/abs/2407.07754)38|<I>Random unitaries in extremely low depth</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.08608-black.svg)](https://arxiv.org/abs/2407.08608)37|<I>FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/86cc7933-ecf9-9af9-872f-69f0e6eb0f09.png">](https://youtu.be/5AsdP0ihBpY)|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.16741-black.svg)](https://arxiv.org/abs/2407.16741)35|<I>OpenHands: An Open Platform for AI Software Developers as Generalist Agents</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/6a96f2ab-f443-ea26-c4f6-adf7a2a58650.png">](https://youtu.be/AJHrWWgyXtg)|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.04620-black.svg)](https://arxiv.org/abs/2407.04620)35|<I>Learning to (Learn at Test Time): RNNs with Expressive Hidden States</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.01489-black.svg)](https://arxiv.org/abs/2407.01489)34|<I>Agentless: Demystifying LLM-based Software Engineering Agents</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.05407-black.svg)](https://arxiv.org/abs/2407.05407)31|<I>CosyVoice: A Scalable Multilingual Zero-shot Text-to-speech Synthesizer based on Supervised Semantic Tokens</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.17032-black.svg)](https://arxiv.org/abs/2407.17032)30|<I>Gymnasium: A Standard Interface for Reinforcement Learning Environments</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.00215-black.svg)](https://arxiv.org/abs/2407.00215)28|<I>LLM Critics Help Catch LLM Bugs</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/f09d3c94-d66e-44e9-cd68-a55446b63cdf.png">](https://youtu.be/vtHMwASmtFw)|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.06204-black.svg)](https://arxiv.org/abs/2407.06204)27|<I>A Survey on Mixture of Experts</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.21075-black.svg)](https://arxiv.org/abs/2407.21075)25|<I>Apple Intelligence Foundation Language Models</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/c132c687-efe2-64e7-f3aa-3e4820d0c4b9.png">](https://youtu.be/AZ0srxhm38o)|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.16674-black.svg)](https://arxiv.org/abs/2407.16674)25|<I>KAN or MLP: A Fairer Comparison</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.02371-black.svg)](https://arxiv.org/abs/2407.02371)25|<I>OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.00079-black.svg)](https://arxiv.org/abs/2407.00079)25|<I>Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.02490-black.svg)](https://arxiv.org/abs/2407.02490)24|<I>MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/fb9e1f4c-7fef-10b6-c4f0-fd7d50ca7cfe.png">](https://youtu.be/HTWL1jNwVhA)|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.01512-black.svg)](https://arxiv.org/abs/2407.01512)24|<I>Open-TeleVision: Teleoperation with Immersive Active Visual Feedback</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.14679-black.svg)](https://arxiv.org/abs/2407.14679)23|<I>Compact Language Models via Pruning and Knowledge Distillation</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/77d0373e-f238-9c90-856d-979087d9d170.png">](https://youtu.be/yllgCh0irSY)|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.14435-black.svg)](https://arxiv.org/abs/2407.14435)23|<I>Jumping Ahead: Improving Reconstruction Fidelity with JumpReLU Sparse Autoencoders</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.06460-black.svg)](https://arxiv.org/abs/2407.06460)22|<I>MUSE: Machine Unlearning Six-Way Evaluation for Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.19594-black.svg)](https://arxiv.org/abs/2407.19594)21|<I>Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/3328b44b-c13f-d27f-f5ac-76f3d9fdc8b9.png">](https://youtu.be/2SR9RuurQa0)|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.11691-black.svg)](https://arxiv.org/abs/2407.11691)21|<I>VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.15595-black.svg)](https://arxiv.org/abs/2407.15595)20|<I>Discrete Flow Matching</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.12772-black.svg)](https://arxiv.org/abs/2407.12772)20|<I>LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.04295-black.svg)](https://arxiv.org/abs/2407.04295)20|<I>Jailbreak Attacks and Defenses Against Large Language Models: A Survey</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.02392-black.svg)](https://arxiv.org/abs/2407.02392)20|<I>TokenPacker: Efficient Visual Projector for Multimodal LLM</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.01502-black.svg)](https://arxiv.org/abs/2407.01502)20|<I>AI Agents That Matter</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.13623-black.svg)](https://arxiv.org/abs/2407.13623)19|<I>Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.08083-black.svg)](https://arxiv.org/abs/2407.08083)19|<I>MambaVision: A Hybrid Mamba-Transformer Vision Backbone</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/fd2751e9-7c6f-a5b0-45c6-9ca11fcf0584.png">](https://youtu.be/nbYXg8FBxsc)|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.07061-black.svg)](https://arxiv.org/abs/2407.07061)19|<I>Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.00631-black.svg)](https://arxiv.org/abs/2407.00631)19|<I>TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.14358-black.svg)](https://arxiv.org/abs/2407.14358)18|<I>Stable Audio Open</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/9fdb9777-7545-6527-494e-d5088df8f482.png">](https://youtu.be/P-RxN_w3BYg)|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.12874-black.svg)](https://arxiv.org/abs/2407.12874)18|<I>SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.12781-black.svg)](https://arxiv.org/abs/2407.12781)18|<I>VD3D: Taming Large Video Diffusion Transformers for 3D Camera Control</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.08136-black.svg)](https://arxiv.org/abs/2407.08136)18|<I>EchoMimic: Lifelike Audio-Driven Portrait Animations through Editable Landmark Conditions</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.02485-black.svg)](https://arxiv.org/abs/2407.02485)18|<I>RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.20311-black.svg)](https://arxiv.org/abs/2407.20311)17|<I>Physics of Language Models: Part 2.1, Grade-School Math and the Hidden Reasoning Process</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.12768-black.svg)](https://arxiv.org/abs/2407.12768)17|<I>A polynomial-time classical algorithm for noisy quantum circuits</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.08739-black.svg)](https://arxiv.org/abs/2407.08739)17|<I>MAVIS: Mathematical Visual Instruction Tuning with an Automatic Data Engine</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.01599-black.svg)](https://arxiv.org/abs/2407.01599)17|<I>JailbreakZoo: Survey, Landscapes, and Horizons in Jailbreaking Large Language and Vision-Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.01370-black.svg)](https://arxiv.org/abs/2407.01370)17|<I>Summary of a Haystack: A Challenge to Long-Context LLMs and RAG Systems</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/96a11fec-b609-2da8-e23b-91797e8235b2.png">](https://youtu.be/6tCC3-G-qto)|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.21705-black.svg)](https://arxiv.org/abs/2407.21705)16|<I>Tora: Trajectory-oriented Diffusion Transformer for Video Generation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.15754-black.svg)](https://arxiv.org/abs/2407.15754)16|<I>LongVideoBench: A Benchmark for Long-context Interleaved Video-Language Understanding</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.13278-black.svg)](https://arxiv.org/abs/2407.13278)16|<I>Deep Time Series Models: A Comprehensive Survey and Benchmark</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.06581-black.svg)](https://arxiv.org/abs/2407.06581)16|<I>Vision language models are blind</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.05975-black.svg)](https://arxiv.org/abs/2407.05975)16|<I>LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.03168-black.svg)](https://arxiv.org/abs/2407.03168)16|<I>LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.01603-black.svg)](https://arxiv.org/abs/2407.01603)16|<I>A Review of Large Language Models and Autonomous Agents in Chemistry</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.01476-black.svg)](https://arxiv.org/abs/2407.01476)16|<I>Tree Search for Language Model Agents</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.17611-black.svg)](https://arxiv.org/abs/2407.17611)15|<I>Adaptive Training of Grid-Dependent Physics-Informed Kolmogorov-Arnold Networks</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.15841-black.svg)](https://arxiv.org/abs/2407.15841)15|<I>SlowFast-LLaVA: A Strong Training-Free Baseline for Video Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.14981-black.svg)](https://arxiv.org/abs/2407.14981)15|<I>Open Problems in Technical AI Governance</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.13764-black.svg)](https://arxiv.org/abs/2407.13764)15|<I>Shape of Motion: 4D Reconstruction from a Single Video</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.13690-black.svg)](https://arxiv.org/abs/2407.13690)15|<I>DART-Math: Difficulty-Aware Rejection Tuning for Mathematical Problem-Solving</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.10817-black.svg)](https://arxiv.org/abs/2407.10817)15|<I>Foundational Autoraters: Taming Large Language Models for Better Automatic Evaluation</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.07614-black.svg)](https://arxiv.org/abs/2407.07614)15|<I>MARS: Mixture of Auto-Regressive Models for Fine-grained Text-to-image Synthesis</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.01494-black.svg)](https://arxiv.org/abs/2407.01494)15|<I>FoleyCrafter: Bring Silent Videos to Life with Lifelike and Synchronized Sounds</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.01392-black.svg)](https://arxiv.org/abs/2407.01392)15|<I>Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.00952-black.svg)](https://arxiv.org/abs/2407.00952)15|<I>SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.00634-black.svg)](https://arxiv.org/abs/2407.00634)15|<I>Tarsier: Recipes for Training and Evaluating Large Video Description Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.20184-black.svg)](https://arxiv.org/abs/2407.20184)14|<I>Benchmarking and fidelity response theory of high-fidelity Rydberg entangling gates</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.19669-black.svg)](https://arxiv.org/abs/2407.19669)14|<I>mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.18921-black.svg)](https://arxiv.org/abs/2407.18921)14|<I>Mobile Edge Intelligence for Large Language Models: A Contemporary Survey</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.16216-black.svg)](https://arxiv.org/abs/2407.16216)14|<I>A Comprehensive Survey of LLM Alignment Techniques: RLHF, RLAIF, PPO, DPO and More</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.14933-black.svg)](https://arxiv.org/abs/2407.14933)14|<I>Consent in Crisis: The Rapid Decline of the AI Data Commons</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.11963-black.svg)](https://arxiv.org/abs/2407.11963)14|<I>NeedleBench: Can LLMs Do Retrieval and Reasoning in 1 Million Context Window?</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.06023-black.svg)](https://arxiv.org/abs/2407.06023)14|<I>Distilling System 2 into System 1</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/64a97196-f2f3-e2f2-af5a-b51dd3a43f80.png">](https://youtu.be/kC942dg9ek4)|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.03835-black.svg)](https://arxiv.org/abs/2407.03835)14|<I>7th ABAW Competition: Multi-Task Learning and Compound Expression Recognition</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.15549-black.svg)](https://arxiv.org/abs/2407.15549)13|<I>Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.14507-black.svg)](https://arxiv.org/abs/2407.14507)13|<I>Internal Consistency and Self-Feedback in Large Language Models: A Survey</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.12994-black.svg)](https://arxiv.org/abs/2407.12994)13|<I>A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/6d8d746f-ce89-0ecf-f2ed-48ae53ee9400.png">](https://youtu.be/7V42y28JbHw)|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.08683-black.svg)](https://arxiv.org/abs/2407.08683)13|<I>SEED-Story: Multimodal Long Story Generation with Large Language Model</I>|[<img width="180" src="https://qiita-image-store.s3.ap-northeast-1.amazonaws.com/0/3843924/e747e41f-77fb-1eeb-1fcf-88ae1e7834c5.png">](https://youtu.be/bUB8NRNeZGE)|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.07694-black.svg)](https://arxiv.org/abs/2407.07694)13|<I>Scalable, high-fidelity all-electronic control of trapped-ion qubits</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.06886-black.svg)](https://arxiv.org/abs/2407.06886)13|<I>Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.06551-black.svg)](https://arxiv.org/abs/2407.06551)13|<I>OffsetBias: Leveraging Debiased Data for Tuning Evaluators</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.01492-black.svg)](https://arxiv.org/abs/2407.01492)13|<I>RegMix: Data Mixture as Regression for Language Model Pre-training</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.21772-black.svg)](https://arxiv.org/abs/2407.21772)12|<I>ShieldGemma: Generative AI Content Moderation Based on Gemma</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.12784-black.svg)](https://arxiv.org/abs/2407.12784)12|<I>AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.12043-black.svg)](https://arxiv.org/abs/2407.12043)12|<I>The Art of Saying No: Contextual Noncompliance in Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.11062-black.svg)](https://arxiv.org/abs/2407.11062)12|<I>EfficientQAT: Efficient Quantization-Aware Training for Large Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.07403-black.svg)](https://arxiv.org/abs/2407.07403)12|<I>A Survey of Attacks on Large Vision-Language Models: Resources, Advances, and Future Trends</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.07263-black.svg)](https://arxiv.org/abs/2407.07263)12|<I>Reuse, Don&#39;t Retrain: A Recipe for Continued Pretraining of Language Models</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.05633-black.svg)](https://arxiv.org/abs/2407.05633)12|<I>AdaPI: Facilitating DNN Model Adaptivity for Efficient Private Inference in Edge Computing</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.04675-black.svg)](https://arxiv.org/abs/2407.04675)12|<I>Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.03162-black.svg)](https://arxiv.org/abs/2407.03162)12|<I>Bunny-VisionPro: Real-Time Bimanual Dexterous Teleoperation for Imitation Learning</I>|not yet|
|[![arxiv](https://img.shields.io/badge/arXiv-2407.02646-black.svg)](https://arxiv.org/abs/2407.02646)12|<I>A Practical Review of Mechanistic Interpretability for Transformer-Based 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/