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- 🚀 AI技術の最新動向 – 2026年2月13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 milanm/AutoGrad-Engine adoslabsproject-gif/nothumanallowed Kuberwastaken/picogpt ScottT2-spec/mnist-neural-network- berrzebb/zeroquant 📌 関連記事もチェック 📚 最新研究論文 1. Scaling Verification Can Be More Effective than Scaling Policy Learning for Vision-Language-Action Alignment 著者: Jacky Kwok, Xilun Zhang, Mengdi Xu The long-standing vision of general-purpose robots hinges on their ability to understand and act upon natural language instructions. Vision-Language-Action (VLA) models have made remarkable progress toward this goal, yet their generated actions can still misalign with the given instructions. In this… 論文を読む → 2. UniT: Unified Multimodal Chain-of-Thought Test-time Scaling 著者: Leon Liangyu Chen, Haoyu Ma, Zhipeng Fan Unified models can handle both multimodal understanding and generation within a single architecture, yet they typically operate in a single pass without iteratively refining their outputs. Many multimodal tasks, especially those involving complex spatial compositions, multiple interacting objects, o… 論文を読む → 3. AttentionRetriever: Attention Layers are Secretly Long Document Retrievers 著者: David Jiahao Fu, Lam Thanh Do, Jiayu Li Retrieval augmented generation (RAG) has been widely adopted to help Large Language Models (LLMs) to process tasks involving long documents. However, existing retrieval models are not designed for long document retrieval and fail to address several key challenges of long document retrieval, includin… 論文を読む → 💻 注目のGitHubプロジェクト 1. milanm/AutoGrad-Engine A complete GPT language model (training and inference) in pure C# with zero dependencies. ⭐ 106 stars | 🔀 13 forks リポジトリを見る → 2. adoslabsproject-gif/nothumanallowed 42 AI agents. 9-layer consensus. One prompt. | Multi-agent orchestrator with ONNX neural routing, semantic convergence, and cross-LLM validation. https://nothumanallowed.com ⭐ 49 stars | 🔀 10 forks リポジトリを見る → 3. Kuberwastaken/picogpt GPT in a QR Code ; The actual most atomic way to train and inference a GPT in pure, dependency-free Python. ⭐ 18 stars | 🔀 0 forks リポジトリを見る → 4. ScottT2-spec/mnist-neural-network- Neural network built from scratch using only NumPy — 96% accuracy on MNIST. No TensorFlow, no PyTorch, pure math. ⭐ 13 stars | 🔀 0 forks リポジトリを見る → 5. berrzebb/zeroquant Rust 기반 고성능 자동화 트레이딩 시스템 ⭐ 10 stars | 🔀 8 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2月13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
- milanm/AutoGrad-Engine
- adoslabsproject-gif/nothumanallowed
- Kuberwastaken/picogpt
- ScottT2-spec/mnist-neural-network-
- berrzebb/zeroquant
- 📌 関連記事もチェック
📚 最新研究論文
1. Scaling Verification Can Be More Effective than Scaling Policy Learning for Vision-Language-Action Alignment
著者: Jacky Kwok, Xilun Zhang, Mengdi Xu
The long-standing vision of general-purpose robots hinges on their ability to understand and act upon natural language instructions. Vision-Language-Action (VLA) models have made remarkable progress toward this goal, yet their generated actions can still misalign with the given instructions. In this…
2. UniT: Unified Multimodal Chain-of-Thought Test-time Scaling
著者: Leon Liangyu Chen, Haoyu Ma, Zhipeng Fan
Unified models can handle both multimodal understanding and generation within a single architecture, yet they typically operate in a single pass without iteratively refining their outputs. Many multimodal tasks, especially those involving complex spatial compositions, multiple interacting objects, o…
3. AttentionRetriever: Attention Layers are Secretly Long Document Retrievers
著者: David Jiahao Fu, Lam Thanh Do, Jiayu Li
Retrieval augmented generation (RAG) has been widely adopted to help Large Language Models (LLMs) to process tasks involving long documents. However, existing retrieval models are not designed for long document retrieval and fail to address several key challenges of long document retrieval, includin…
💻 注目のGitHubプロジェクト
1. milanm/AutoGrad-Engine
A complete GPT language model (training and inference) in pure C# with zero dependencies.
⭐ 106 stars | 🔀 13 forks
2. adoslabsproject-gif/nothumanallowed
42 AI agents. 9-layer consensus. One prompt. | Multi-agent orchestrator with ONNX neural routing, semantic convergence, and cross-LLM validation. https://nothumanallowed.com
⭐ 49 stars | 🔀 10 forks
3. Kuberwastaken/picogpt
GPT in a QR Code ; The actual most atomic way to train and inference a GPT in pure, dependency-free Python.
⭐ 18 stars | 🔀 0 forks
4. ScottT2-spec/mnist-neural-network-
Neural network built from scratch using only NumPy — 96% accuracy on MNIST. No TensorFlow, no PyTorch, pure math.
⭐ 13 stars | 🔀 0 forks
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