【AI最新動向 2026年1月15日】論文5件・GitHub5件

2026年1月15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15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 superdoc-dev/docx-corpus samouraiworld/awesome-mistral tarunnegi196/100-Days-of-ML-Handwritten-Notes christofilojohn/MacVisionTools fdddf/modely 📌 関連記事もチェック 📚 最新研究論文 1. Fast-ThinkAct: Efficient Vision-Language-Action Reasoning via Verbalizable Latent Planning 著者: Chi-Pin Huang, Yunze Man, Zhiding Yu Vision-Language-Action (VLA) tasks require reasoning over complex visual scenes and executing adaptive actions in dynamic environments. While recent studies on reasoning VLAs show that explicit chain-of-thought (CoT) can improve generalization, they suffer from high inference latency due to lengthy … 論文を読む → 2. Value-Aware Numerical Representations for Transformer Language Models 著者: Andreea Dutulescu, Stefan Ruseti, Mihai Dascalu Transformer-based language models often achieve strong results on mathematical reasoning benchmarks while remaining fragile on basic numerical understanding and arithmetic operations. A central limitation is that numbers are processed as symbolic tokens whose embeddings do not explicitly encode nume… 論文を読む → 3. ShortCoder: Knowledge-Augmented Syntax Optimization for Token-Efficient Code Generation 著者: Sicong Liu, Yanxian Huang, Mingwei Liu Code generation tasks aim to automate the conversion of user requirements into executable code, significantly reducing manual development efforts and enhancing software productivity. The emergence of large language models (LLMs) has significantly advanced code generation, though their efficiency is … 論文を読む → 💻 注目のGitHubプロジェクト 1. superdoc-dev/docx-corpus The largest open corpus of .docx files for document processing research ⭐ 36 stars | 🔀 1 forks リポジトリを見る → 2. samouraiworld/awesome-mistral A curated list of awesome resources, tools, libraries, and projects for the Mistral AI ecosystem. ⭐ 30 stars | 🔀 0 forks リポジトリを見る → 3. tarunnegi196/100-Days-of-ML-Handwritten-Notes My "50 page" handwritten notes while following the popular "100 Days of Machine Learning" playlist by CampusX (Nitish Kumar). ⭐ 7 stars | 🔀 1 forks リポジトリを見る → 4. christofilojohn/MacVisionTools A Mac OS menu bar utility that runs on-device Computer Vision models for simple, fun tasks ⭐ 6 stars | 🔀 0 forks リポジトリを見る → 5. fdddf/modely AI models download in one command ⭐ 5 stars | 🔀 0 forks リポジトリを見る → 📚 あわせて読みたい「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三つ巴！Geminiと私が出会って、ストーリー作りは変わった？。

🚀 AI技術の最新動向 – 2026年1月15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. superdoc-dev/docx-corpus
2. samouraiworld/awesome-mistral
3. tarunnegi196/100-Days-of-ML-Handwritten-Notes
4. christofilojohn/MacVisionTools
5. fdddf/modely
📌 関連記事もチェック

📚 最新研究論文

1. Fast-ThinkAct: Efficient Vision-Language-Action Reasoning via Verbalizable Latent Planning

著者: Chi-Pin Huang, Yunze Man, Zhiding Yu

Vision-Language-Action (VLA) tasks require reasoning over complex visual scenes and executing adaptive actions in dynamic environments. While recent studies on reasoning VLAs show that explicit chain-of-thought (CoT) can improve generalization, they suffer from high inference latency due to lengthy …

論文を読む →

2. Value-Aware Numerical Representations for Transformer Language Models

著者: Andreea Dutulescu, Stefan Ruseti, Mihai Dascalu

Transformer-based language models often achieve strong results on mathematical reasoning benchmarks while remaining fragile on basic numerical understanding and arithmetic operations. A central limitation is that numbers are processed as symbolic tokens whose embeddings do not explicitly encode nume…

論文を読む →

3. ShortCoder: Knowledge-Augmented Syntax Optimization for Token-Efficient Code Generation

著者: Sicong Liu, Yanxian Huang, Mingwei Liu

Code generation tasks aim to automate the conversion of user requirements into executable code, significantly reducing manual development efforts and enhancing software productivity. The emergence of large language models (LLMs) has significantly advanced code generation, though their efficiency is …

論文を読む →