【AI最新動向 2026年3月2日】論文5件・GitHub5件

2026年3月2日2026年3月6日

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🚀 AI技術の最新動向 – 2026年3月2日世界中から収集したAI・機械学習の最新情報をお届けします 📑 目次 💻 注目のGitHubプロジェクト kossisoroyce/timber gunmetal57qa8q/AimAssist dtormoen/neural-open.nvim Abdulrahman-S-Asiri/Data_Science_Basics hanxiao/umap-mlx 📌 関連記事もチェック 📚 最新研究論文 1. Mode Seeking meets Mean Seeking for Fast Long Video Generation 著者: Shengqu Cai, Weili Nie, Chao Liu Scaling video generation from seconds to minutes faces a critical bottleneck: while short-video data is abundant and high-fidelity, coherent long-form data is scarce and limited to narrow domains. To address this, we propose a training paradigm where Mode Seeking meets Mean Seeking, decoupling local… 論文を読む → 2. DARE-bench: Evaluating Modeling and Instruction Fidelity of LLMs in Data Science 著者: Fan Shu, Yite Wang, Ruofan Wu The fast-growing demands in using Large Language Models (LLMs) to tackle complex multi-step data science tasks create an emergent need for accurate benchmarking. There are two major gaps in existing benchmarks: (i) the lack of standardized, process-aware evaluation that captures instruction adherenc… 論文を読む → 3. Do LLMs Benefit From Their Own Words? 著者: Jenny Y. Huang, Leshem Choshen, Ramon Astudillo Multi-turn interactions with large language models typically retain the assistant's own past responses in the conversation history. In this work, we revisit this design choice by asking whether large language models benefit from conditioning on their own prior responses. Using in-the-wild, multi-tur… 論文を読む → 💻 注目のGitHubプロジェクト 1. kossisoroyce/timber Ollama for classical ML models. AOT compiler that turns XGBoost, LightGBM, scikit-learn, CatBoost & ONNX models into native C99 inference code. One command to load, one command to serve. 336x faster than Python inference. ⭐ 250 stars | 🔀 8 forks リポジトリを見る → 2. gunmetal57qa8q/AimAssist AI | AIM | ASSIST | MORE GAME | CS2 | APEX | 2026 ⭐ 71 stars | 🔀 0 forks リポジトリを見る → 3. dtormoen/neural-open.nvim A smart picker for Snacks.nvim that trains a neural network with your file picking preferences. ⭐ 35 stars | 🔀 1 forks リポジトリを見る → 4. Abdulrahman-S-Asiri/Data_Science_Basics 📊 Complete Data Science Guide — Bilingual (Arabic/English) — From Zero to Hero ⭐ 33 stars | 🔀 5 forks リポジトリを見る → 5. hanxiao/umap-mlx UMAP in pure MLX for Apple Silicon. 30x faster than umap-learn. ⭐ 32 stars | 🔀 3 forks リポジトリを見る → 📚 あわせて読みたい【AI最新動向】一歩先の未来へ！私が触れた最先端AIの世界「プロンプトは戦略だ！」と学んで劇的効率UP！私のAI活用術私が実感！AIで物流の悩み解消、時間もコストも浮いた話。

🚀 AI技術の最新動向 – 2026年3月2日

世界中から収集したAI・機械学習の最新情報をお届けします

📑 目次

💻 注目のGitHubプロジェクト
1. kossisoroyce/timber
2. gunmetal57qa8q/AimAssist
3. dtormoen/neural-open.nvim
4. Abdulrahman-S-Asiri/Data_Science_Basics
5. hanxiao/umap-mlx
📌 関連記事もチェック

📚 最新研究論文

1. Mode Seeking meets Mean Seeking for Fast Long Video Generation

著者: Shengqu Cai, Weili Nie, Chao Liu

Scaling video generation from seconds to minutes faces a critical bottleneck: while short-video data is abundant and high-fidelity, coherent long-form data is scarce and limited to narrow domains. To address this, we propose a training paradigm where Mode Seeking meets Mean Seeking, decoupling local…

論文を読む →

2. DARE-bench: Evaluating Modeling and Instruction Fidelity of LLMs in Data Science

著者: Fan Shu, Yite Wang, Ruofan Wu

The fast-growing demands in using Large Language Models (LLMs) to tackle complex multi-step data science tasks create an emergent need for accurate benchmarking. There are two major gaps in existing benchmarks: (i) the lack of standardized, process-aware evaluation that captures instruction adherenc…

論文を読む →

3. Do LLMs Benefit From Their Own Words?

著者: Jenny Y. Huang, Leshem Choshen, Ramon Astudillo

Multi-turn interactions with large language models typically retain the assistant's own past responses in the conversation history. In this work, we revisit this design choice by asking whether large language models benefit from conditioning on their own prior responses. Using in-the-wild, multi-tur…

論文を読む →

💻 注目のGitHubプロジェクト

1. kossisoroyce/timber

Ollama for classical ML models. AOT compiler that turns XGBoost, LightGBM, scikit-learn, CatBoost & ONNX models into native C99 inference code. One command to load, one command to serve. 336x faster than Python inference.

⭐ 250 stars | 🔀 8 forks

リポジトリを見る →