【AI最新動向 2026年2月20日】論文5件・GitHub5件

2026年2月20日2026年3月27日

📝 この記事のポイント

🚀 AI技術の最新動向 – 2026年2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 vixhal-baraiya/microgpt-c QwertyMcQwertz/monkeys-with-typewriters WB2024/Essentia-to-Metadata benoitc/erlang-python greynewell/evaldriven.org 📌 関連記事もチェック 📚 最新研究論文 1. Sink-Aware Pruning for Diffusion Language Models 著者: Aidar Myrzakhan, Tianyi Li, Bowei Guo Diffusion Language Models (DLMs) incur high inference cost due to iterative denoising, motivating efficient pruning. Existing pruning heuristics largely inherited from autoregressive (AR) LLMs, typically preserve attention sink tokens because AR sinks serve as stable global anchors. We show that thi… 論文を読む → 2. CLEF HIPE-2026: Evaluating Accurate and Efficient Person-Place Relation Extraction from Multilingual Historical Texts 著者: Juri Opitz, Corina Raclé, Emanuela Boros HIPE-2026 is a CLEF evaluation lab dedicated to person-place relation extraction from noisy, multilingual historical texts. Building on the HIPE-2020 and HIPE-2022 campaigns, it extends the series toward semantic relation extraction by targeting the task of identifying person–place associations in … 論文を読む → 3. MARS: Margin-Aware Reward-Modeling with Self-Refinement 著者: Payel Bhattacharjee, Osvaldo Simeone, Ravi Tandon Reward modeling is a core component of modern alignment pipelines including RLHF and RLAIF, underpinning policy optimization methods including PPO and TRPO. However, training reliable reward models relies heavily on human-labeled preference data, which is costly and limited, motivating the use of da… 論文を読む → 💻 注目のGitHubプロジェクト 1. vixhal-baraiya/microgpt-c The most atomic way to train and inference a GPT in pure, dependency-free C ⭐ 197 stars | 🔀 38 forks リポジトリを見る → 2. QwertyMcQwertz/monkeys-with-typewriters The complete AI platform on a $3 microcontroller. Sub-millisecond inference. Zero hallucinations. ⭐ 44 stars | 🔀 4 forks リポジトリを見る → 3. WB2024/Essentia-to-Metadata Intelligent audio analysis and automatic genre/mood tagging using Essentia ML models ⭐ 21 stars | 🔀 2 forks リポジトリを見る → 4. benoitc/erlang-python Execute Python from Erlang using dirty NIFs with GIL-aware execution, rate limiting, and free-threading support ⭐ 18 stars | 🔀 2 forks リポジトリを見る → 5. greynewell/evaldriven.org Ship evals before you ship features. ⭐ 12 stars | 🔀 4 forks リポジトリを見る → 📚 あわせて読みたい【AI最新動向】一歩先の未来へ！私が触れた最先端AIの世界「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技術の最新動向 – 2026年2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. vixhal-baraiya/microgpt-c
2. QwertyMcQwertz/monkeys-with-typewriters
3. WB2024/Essentia-to-Metadata
4. benoitc/erlang-python
5. greynewell/evaldriven.org
📌 関連記事もチェック

📚 最新研究論文

1. Sink-Aware Pruning for Diffusion Language Models

著者: Aidar Myrzakhan, Tianyi Li, Bowei Guo

Diffusion Language Models (DLMs) incur high inference cost due to iterative denoising, motivating efficient pruning. Existing pruning heuristics largely inherited from autoregressive (AR) LLMs, typically preserve attention sink tokens because AR sinks serve as stable global anchors. We show that thi…

論文を読む →

2. CLEF HIPE-2026: Evaluating Accurate and Efficient Person-Place Relation Extraction from Multilingual Historical Texts

著者: Juri Opitz, Corina Raclé, Emanuela Boros

HIPE-2026 is a CLEF evaluation lab dedicated to person-place relation extraction from noisy, multilingual historical texts. Building on the HIPE-2020 and HIPE-2022 campaigns, it extends the series toward semantic relation extraction by targeting the task of identifying person–place associations in …

論文を読む →

3. MARS: Margin-Aware Reward-Modeling with Self-Refinement

著者: Payel Bhattacharjee, Osvaldo Simeone, Ravi Tandon

Reward modeling is a core component of modern alignment pipelines including RLHF and RLAIF, underpinning policy optimization methods including PPO and TRPO. However, training reliable reward models relies heavily on human-labeled preference data, which is costly and limited, motivating the use of da…

論文を読む →