【AI最新動向 2026年2月22日】論文5件・GitHub5件

2026年2月22日

📝 この記事のポイント

🚀 AI技術の最新動向 - 2026年2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QwertyMcQwertz/monkeys-with-typewritersVisiblegrupour/AI-Face-Changer-Real-TimeWB2024/Essentia-to-Metadatacraigm26/OpenCastorAyush-Aditya/decoder-only-seq2seq📌 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 GuoDiffusion 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 BorosHIPE-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 TandonReward 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. QwertyMcQwertz/monkeys-with-typewritersThe complete AI platform on a $3 microcontroller. Sub-millisecond inference. Zero hallucinations.⭐ 44 stars | 🔀 4 forksリポジトリを見る →2. Visiblegrupour/AI-Face-Changer-Real-TimeAI Face Changer is a powerful AI-powered face swap and face editing software using deep learning and computer vision. Instantly replace faces in photos and videos with high-quality, realistic results. Perfect for content creators, social media, marketing, and fun entertainment projects. Fast, secure, and easy to use.⭐ 26 stars | 🔀 0 forksリポジトリを見る →3. WB2024/Essentia-to-MetadataIntelligent audio analysis and automatic genre/mood tagging using Essentia ML models⭐ 23 stars | 🔀 2 forksリポジトリを見る →4. craigm26/OpenCastorOpen-source AI robotics framework — tiered brain, 8 AI providers, multi-robot swarm, self-improving loop⭐ 8 stars | 🔀 0 forksリポジトリを見る →5. Ayush-Aditya/decoder-only-seq2seqMinimal decoder-only seq2seq pipeline with proper causal masking, teacher forcing, Ignite training loop, and checkpointed inference⭐ 6 stars | 🔀 0 forksリポジトリを見る →。

🚀 AI技術の最新動向 – 2026年2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. QwertyMcQwertz/monkeys-with-typewriters
2. Visiblegrupour/AI-Face-Changer-Real-Time
3. WB2024/Essentia-to-Metadata
4. craigm26/OpenCastor
5. Ayush-Aditya/decoder-only-seq2seq
📌 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. 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 →

2. Visiblegrupour/AI-Face-Changer-Real-Time

AI Face Changer is a powerful AI-powered face swap and face editing software using deep learning and computer vision. Instantly replace faces in photos and videos with high-quality, realistic results. Perfect for content creators, social media, marketing, and fun entertainment projects. Fast, secure, and easy to use.

⭐ 26 stars | 🔀 0 forks

リポジトリを見る →