【AI最新動向 2026年1月22日】論文5件・GitHub5件

2026年1月22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22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 Infatoshi/batmobile buyukakyuz/rig RAZZULLIX/fast_topk_batched analyticalrohit/llms-from-scratch thinkbigcd/embedding-service 📌 関連記事もチェック 📚 最新研究論文 1. Iterative Refinement Improves Compositional Image Generation 著者: Shantanu Jaiswal, Mihir Prabhudesai, Nikash Bhardwaj Text-to-image (T2I) models have achieved remarkable progress, yet they continue to struggle with complex prompts that require simultaneously handling multiple objects, relations, and attributes. Existing inference-time strategies, such as parallel sampling with verifiers or simply increasing denoisi… 論文を読む → 2. Rethinking Video Generation Model for the Embodied World 著者: Yufan Deng, Zilin Pan, Hongyu Zhang Video generation models have significantly advanced embodied intelligence, unlocking new possibilities for generating diverse robot data that capture perception, reasoning, and action in the physical world. However, synthesizing high-quality videos that accurately reflect real-world robotic interact… 論文を読む → 3. MolecularIQ: Characterizing Chemical Reasoning Capabilities Through Symbolic Verification on Molecular Graphs 著者: Christoph Bartmann, Johannes Schimunek, Mykyta Ielanskyi A molecule's properties are fundamentally determined by its composition and structure encoded in its molecular graph. Thus, reasoning about molecular properties requires the ability to parse and understand the molecular graph. Large Language Models (LLMs) are increasingly applied to chemistry, tackl… 論文を読む → 💻 注目のGitHubプロジェクト 1. Infatoshi/batmobile High-performance CUDA kernels for equivariant graph neural networks (MACE, NequIP, Allegro). 10-20x faster than e3nn. ⭐ 37 stars | 🔀 6 forks リポジトリを見る → 2. buyukakyuz/rig Distributed LLM inference across machines over WiFi ⭐ 22 stars | 🔀 2 forks リポジトリを見る → 3. RAZZULLIX/fast_topk_batched High-performance batched Top-K selection for CPU inference. Up to 80x faster than PyTorch, optimized for LLM sampling with AVX2 SIMD. ⭐ 15 stars | 🔀 1 forks リポジトリを見る → 4. analyticalrohit/llms-from-scratch Build a ChatGPT like LLM from scratch in PyTorch, explained step by step. ⭐ 11 stars | 🔀 4 forks リポジトリを見る → 5. thinkbigcd/embedding-service api service for generating and managing text embeddings ⭐ 4 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22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. Infatoshi/batmobile
2. buyukakyuz/rig
3. RAZZULLIX/fast_topk_batched
4. analyticalrohit/llms-from-scratch
5. thinkbigcd/embedding-service
📌 関連記事もチェック

📚 最新研究論文

1. Iterative Refinement Improves Compositional Image Generation

著者: Shantanu Jaiswal, Mihir Prabhudesai, Nikash Bhardwaj

Text-to-image (T2I) models have achieved remarkable progress, yet they continue to struggle with complex prompts that require simultaneously handling multiple objects, relations, and attributes. Existing inference-time strategies, such as parallel sampling with verifiers or simply increasing denoisi…

論文を読む →

2. Rethinking Video Generation Model for the Embodied World

著者: Yufan Deng, Zilin Pan, Hongyu Zhang

Video generation models have significantly advanced embodied intelligence, unlocking new possibilities for generating diverse robot data that capture perception, reasoning, and action in the physical world. However, synthesizing high-quality videos that accurately reflect real-world robotic interact…

論文を読む →

3. MolecularIQ: Characterizing Chemical Reasoning Capabilities Through Symbolic Verification on Molecular Graphs

著者: Christoph Bartmann, Johannes Schimunek, Mykyta Ielanskyi

A molecule's properties are fundamentally determined by its composition and structure encoded in its molecular graph. Thus, reasoning about molecular properties requires the ability to parse and understand the molecular graph. Large Language Models (LLMs) are increasingly applied to chemistry, tackl…

論文を読む →