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- 🚀 AI技術の最新動向 - 2026年2月17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-Enginevixhal-baraiya/microgpt-cKuberwastaken/picogptdoramirdor/NadirClawbenoitc/erlang-python📌 関連記事もチェック📚 最新研究論文1. Symmetry in language statistics shapes the geometry of model representations著者: Dhruva Karkada, Daniel J. Korchinski, Andres NavaAlthough learned representations underlie neural networks' success, their fundamental properties remain poorly understood. A striking example is the emergence of simple geometric structures in LLM representations: for example, calendar months organize into a circle, years form a smooth one-dimension...論文を読む →2. Long Context, Less Focus: A Scaling Gap in LLMs Revealed through Privacy and Personalization著者: Shangding GuLarge language models (LLMs) are increasingly deployed in privacy-critical and personalization-oriented scenarios, yet the role of context length in shaping privacy leakage and personalization effectiveness remains largely unexplored. We introduce a large-scale benchmark, PAPerBench, to systematical...論文を読む →3. Rethinking Diffusion Models with Symmetries through Canonicalization with Applications to Molecular Graph Generation著者: Cai Zhou, Zijie Chen, Zian LiMany generative tasks in chemistry and science involve distributions invariant to group symmetries (e.g., permutation and rotation). A common strategy enforces invariance and equivariance through architectural constraints such as equivariant denoisers and invariant priors. In this paper, we challeng...論文を読む →💻 注目のGitHubプロジェクト1. milanm/AutoGrad-EngineA complete GPT language model (training and inference) in ~600 lines of pure C#, zero dependencies⭐ 290 stars | 🔀 31 forksリポジトリを見る →2. vixhal-baraiya/microgpt-cThe most atomic way to train and inference a GPT in pure, dependency-free C⭐ 162 stars | 🔀 31 forksリポジトリを見る →3. Kuberwastaken/picogptGPT in a QR Code ; The actual most atomic way to train and inference a GPT in pure, dependency-free JS/Python.⭐ 84 stars | 🔀 9 forksリポジトリを見る →4. doramirdor/NadirClawOpen-source LLM router that saves you money. Routes simple prompts to cheap/local models, complex ones to premium — automatically. OpenAI-compatible proxy.⭐ 29 stars | 🔀 5 forksリポジトリを見る →5. benoitc/erlang-pythonExecute Python from Erlang using dirty NIFs with GIL-aware execution, rate limiting, and free-threading support⭐ 13 stars | 🔀 1 forksリポジトリを見る →。
🚀 AI技術の最新動向 – 2026年2月17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
- vixhal-baraiya/microgpt-c
- Kuberwastaken/picogpt
- doramirdor/NadirClaw
- benoitc/erlang-python
- 📌 関連記事もチェック
📚 最新研究論文
1. Symmetry in language statistics shapes the geometry of model representations
著者: Dhruva Karkada, Daniel J. Korchinski, Andres Nava
Although learned representations underlie neural networks' success, their fundamental properties remain poorly understood. A striking example is the emergence of simple geometric structures in LLM representations: for example, calendar months organize into a circle, years form a smooth one-dimension…
2. Long Context, Less Focus: A Scaling Gap in LLMs Revealed through Privacy and Personalization
著者: Shangding Gu
Large language models (LLMs) are increasingly deployed in privacy-critical and personalization-oriented scenarios, yet the role of context length in shaping privacy leakage and personalization effectiveness remains largely unexplored. We introduce a large-scale benchmark, PAPerBench, to systematical…
3. Rethinking Diffusion Models with Symmetries through Canonicalization with Applications to Molecular Graph Generation
著者: Cai Zhou, Zijie Chen, Zian Li
Many generative tasks in chemistry and science involve distributions invariant to group symmetries (e.g., permutation and rotation). A common strategy enforces invariance and equivariance through architectural constraints such as equivariant denoisers and invariant priors. In this paper, we challeng…
💻 注目のGitHubプロジェクト
1. milanm/AutoGrad-Engine
A complete GPT language model (training and inference) in ~600 lines of pure C#, zero dependencies
⭐ 290 stars | 🔀 31 forks
2. vixhal-baraiya/microgpt-c
The most atomic way to train and inference a GPT in pure, dependency-free C
⭐ 162 stars | 🔀 31 forks
3. Kuberwastaken/picogpt
GPT in a QR Code ; The actual most atomic way to train and inference a GPT in pure, dependency-free JS/Python.
⭐ 84 stars | 🔀 9 forks
4. doramirdor/NadirClaw
Open-source LLM router that saves you money. Routes simple prompts to cheap/local models, complex ones to premium — automatically. OpenAI-compatible proxy.
⭐ 29 stars | 🔀 5 forks
5. benoitc/erlang-python
Execute Python from Erlang using dirty NIFs with GIL-aware execution, rate limiting, and free-threading support
⭐ 13 stars | 🔀 1 forks
