【AI最新動向 2026年1月20日】論文5件・GitHub5件

2026年1月20日2026年2月10日

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🚀 AI技術の最新動向 – 2026年1月20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 fdddf/modely 📌 関連記事もチェック 📚 最新研究論文 1. Do explanations generalize across large reasoning models? 著者: Koyena Pal, David Bau, Chandan Singh Large reasoning models (LRMs) produce a textual chain of thought (CoT) in the process of solving a problem, which serves as a potentially powerful tool to understand the problem by surfacing a human-readable, natural-language explanation. However, it is unclear whether these explanations generalize,… 論文を読む → 2. Building Production-Ready Probes For Gemini 著者: János Kramár, Joshua Engels, Zheng Wang Frontier language model capabilities are improving rapidly. We thus need stronger mitigations against bad actors misusing increasingly powerful systems. Prior work has shown that activation probes may be a promising misuse mitigation technique, but we identify a key remaining challenge: probes fail … 論文を読む → 3. ShapeR: Robust Conditional 3D Shape Generation from Casual Captures 著者: Yawar Siddiqui, Duncan Frost, Samir Aroudj Recent advances in 3D shape generation have achieved impressive results, but most existing methods rely on clean, unoccluded, and well-segmented inputs. Such conditions are rarely met in real-world scenarios. We present ShapeR, a novel approach for conditional 3D object shape generation from casuall… 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. ⭐ 16 stars | 🔀 4 forks リポジトリを見る → 2. buyukakyuz/rig Distributed LLM inference across machines over WiFi ⭐ 15 stars | 🔀 1 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. ⭐ 14 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. ⭐ 8 stars | 🔀 2 forks リポジトリを見る → 5. fdddf/modely AI models download in one command ⭐ 7 stars | 🔀 1 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20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. fdddf/modely
📌 関連記事もチェック

📚 最新研究論文

1. Do explanations generalize across large reasoning models?

著者: Koyena Pal, David Bau, Chandan Singh

Large reasoning models (LRMs) produce a textual chain of thought (CoT) in the process of solving a problem, which serves as a potentially powerful tool to understand the problem by surfacing a human-readable, natural-language explanation. However, it is unclear whether these explanations generalize,…

論文を読む →

2. Building Production-Ready Probes For Gemini

著者: János Kramár, Joshua Engels, Zheng Wang

Frontier language model capabilities are improving rapidly. We thus need stronger mitigations against bad actors misusing increasingly powerful systems. Prior work has shown that activation probes may be a promising misuse mitigation technique, but we identify a key remaining challenge: probes fail …

論文を読む →

3. ShapeR: Robust Conditional 3D Shape Generation from Casual Captures

著者: Yawar Siddiqui, Duncan Frost, Samir Aroudj

Recent advances in 3D shape generation have achieved impressive results, but most existing methods rely on clean, unoccluded, and well-segmented inputs. Such conditions are rarely met in real-world scenarios. We present ShapeR, a novel approach for conditional 3D object shape generation from casuall…

論文を読む →