【AI最新動向 2026年1月11日】論文5件・GitHub5件

2026年1月11日2026年2月10日

📝 この記事のポイント

🚀 AI技術の最新動向 – 2026年1月11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 panyisheng095-ux/VisionQuant-Pro superdoc-dev/docx-corpus samouraiworld/awesome-mistral leockl/sklearn-diagnose rasidi3112/IMAGINE-CUP-MICROSOFT-2026 📌 関連記事もチェック 📚 最新研究論文 1. Optimal Lower Bounds for Online Multicalibration 著者: Natalie Collina, Jiuyao Lu, Georgy Noarov We prove tight lower bounds for online multicalibration, establishing an information-theoretic separation from marginal calibration. In the general setting where group functions can depend on both context and the learner's predictions, we prove an $Ω(T^{2/3})$ lower bound on expected multicalibrat… 論文を読む → 2. GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization 著者: Shih-Yang Liu, Xin Dong, Ximing Lu As language models become increasingly capable, users expect them to provide not only accurate responses but also behaviors aligned with diverse human preferences across a variety of scenarios. To achieve this, Reinforcement learning (RL) pipelines have begun incorporating multiple rewards, each cap… 論文を読む → 3. RoboVIP: Multi-View Video Generation with Visual Identity Prompting Augments Robot Manipulation 著者: Boyang Wang, Haoran Zhang, Shujie Zhang The diversity, quantity, and quality of manipulation data are critical for training effective robot policies. However, due to hardware and physical setup constraints, collecting large-scale real-world manipulation data remains difficult to scale across diverse environments. Recent work uses text-pro… 論文を読む → 💻 注目のGitHubプロジェクト 1. panyisheng095-ux/VisionQuant-Pro 🤖 基于深度学习的AI量化投资系统 | Vision-Based Quantitative Trading System with Deep Learning ⭐ 33 stars | 🔀 3 forks リポジトリを見る → 2. superdoc-dev/docx-corpus The largest open corpus of .docx files for document processing research ⭐ 23 stars | 🔀 1 forks リポジトリを見る → 3. samouraiworld/awesome-mistral A curated list of awesome resources, tools, libraries, and projects for the Mistral AI ecosystem. ⭐ 17 stars | 🔀 0 forks リポジトリを見る → 4. leockl/sklearn-diagnose 🔍 AI-powered diagnosis for Scikit-learn models: Detect overfitting, data leakage, class imbalance & more with LLM-generated insights ⭐ 9 stars | 🔀 0 forks リポジトリを見る → 5. rasidi3112/IMAGINE-CUP-MICROSOFT-2026 🌾 AI-powered plant disease detection app for farmers in West Nusa Tenggara, Indonesia. Built with Flutter, Azure Custom Vision & Azure OpenAI. ⭐ 7 stars | 🔀 0 forks リポジトリを見る → 📚 あわせて読みたいコールセンター応答率2倍!? 音声認識AI導入でオペレーター3割減の衝撃！【衝撃】画像認識AI頂上決戦！Gemini圧勝の理由製造業の品質検査、AIでコスト激減！？【完全自動化ガイド】。

🚀 AI技術の最新動向 – 2026年1月11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. panyisheng095-ux/VisionQuant-Pro
2. superdoc-dev/docx-corpus
3. samouraiworld/awesome-mistral
4. leockl/sklearn-diagnose
5. rasidi3112/IMAGINE-CUP-MICROSOFT-2026
📌 関連記事もチェック

📚 最新研究論文

1. Optimal Lower Bounds for Online Multicalibration

著者: Natalie Collina, Jiuyao Lu, Georgy Noarov

We prove tight lower bounds for online multicalibration, establishing an information-theoretic separation from marginal calibration.
In the general setting where group functions can depend on both context and the learner's predictions, we prove an $Ω(T^{2/3})$ lower bound on expected multicalibrat…

論文を読む →

2. GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL Optimization

著者: Shih-Yang Liu, Xin Dong, Ximing Lu

As language models become increasingly capable, users expect them to provide not only accurate responses but also behaviors aligned with diverse human preferences across a variety of scenarios. To achieve this, Reinforcement learning (RL) pipelines have begun incorporating multiple rewards, each cap…

論文を読む →

3. RoboVIP: Multi-View Video Generation with Visual Identity Prompting Augments Robot Manipulation

著者: Boyang Wang, Haoran Zhang, Shujie Zhang

The diversity, quantity, and quality of manipulation data are critical for training effective robot policies. However, due to hardware and physical setup constraints, collecting large-scale real-world manipulation data remains difficult to scale across diverse environments. Recent work uses text-pro…

論文を読む →