【AI最新動向 2025年12月22日】論文5件・GitHub5件

📝 この記事のポイント

• 🚀 AI技術の最新動向 – 2025年12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 QKV-Core/QKV-Core JarvisPei/SCOPE AmirhosseinHonardoust/Machine-Learning-Warning-Systems AmirhosseinHonardoust/Tumor-Doppelganger-Studio AmirhosseinHonardoust/Physiological-Debt-Accumulation-Engine 📌 関連記事もチェック 📚 最新研究論文 1. Re-Depth Anything: Test-Time Depth Refinement via Self-Supervised Re-lighting 著者: Ananta R. Bhattarai, Helge Rhodin Monocular depth estimation remains challenging as recent foundation models, such as Depth Anything V2 (DA-V2), struggle with real-world images that are far from the training distribution. We introduce Re-Depth Anything, a test-time self-supervision framework that bridges this domain gap by fusing DA… 論文を読む → 2. Adversarial Robustness of Vision in Open Foundation Models 著者: Jonathon Fox, William J Buchanan, Pavlos Papadopoulos With the increase in deep learning, it becomes increasingly difficult to understand the model in which AI systems can identify objects. Thus, an adversary could aim to modify an image by adding unseen elements, which will confuse the AI in its recognition of an entity. This paper thus investigates t… 論文を読む → 3. When Reasoning Meets Its Laws 著者: Junyu Zhang, Yifan Sun, Tianang Leng Despite the superior performance of Large Reasoning Models (LRMs), their reasoning behaviors are often counterintuitive, leading to suboptimal reasoning capabilities. To theoretically formalize the desired reasoning behaviors, this paper presents the Laws of Reasoning (LoRe), a unified framework tha… 論文を読む → 💻 注目のGitHubプロジェクト 1. QKV-Core/QKV-Core "Adaptive Hybrid Quantization Framework for deploying 7B+ LLMs on low-VRAM devices (e.g., GTX 1050). Features surgical block alignment and Numba-accelerated inference. ⭐ 14 stars | 🔀 0 forks リポジトリを見る → 2. JarvisPei/SCOPE SCOPE: Self-evolving Context Optimization via Prompt Evolution – A framework for automatic prompt optimization ⭐ 10 stars | 🔀 2 forks リポジトリを見る → 3. AmirhosseinHonardoust/Machine-Learning-Warning-Systems A long-form article and practical framework for designing machine learning systems that warn instead of decide. Covers regimes vs decimals, levers over labels, reversible alerts, anti-coercion UI patterns, auditability, and the “Warning Card” template, so ML preserves human agency while staying useful under uncertainty. ⭐ 8 stars | 🔀 0 forks リポジトリを見る → 4. AmirhosseinHonardoust/Tumor-Doppelganger-Studio Similarity-first interpretability studio for breast tumor samples: pick a case, find its closest “twins” (benign/malignant look-alikes), visualize neighborhood structure, compare feature fingerprints, and run minimal-change counterfactual edits toward a target class. Educational demo only, not for diagnosis. ⭐ 7 stars | 🔀 0 forks リポジトリを見る → 5. AmirhosseinHonardoust/Physiological-Debt-Accumulation-Engine A systems-level analysis engine that models sleep as a recovery debt process rather than a nightly outcome. Using physiological traits and ecological pressure signals, it estimates predicted sleep need, quantifies sleep debt, and visualizes how stress accumulates silently before visible fatigue or failure occurs. ⭐ 7 stars | 🔀 0 forks リポジトリを見る → 📚 あわせて読みたい コールセンター応答率2倍!? 音声認識AI導入でオペレーター3割減の衝撃！ 【衝撃】画像認識AI頂上決戦！Gemini圧勝の理由 製造業の品質検査、AIでコスト激減！？【完全自動化ガイド】。

📑 目次

1. 💻 注目のGitHubプロジェクト
   1. QKV-Core/QKV-Core
   2. JarvisPei/SCOPE
   3. AmirhosseinHonardoust/Machine-Learning-Warning-Systems
   4. AmirhosseinHonardoust/Tumor-Doppelganger-Studio
   5. AmirhosseinHonardoust/Physiological-Debt-Accumulation-Engine
2. 📌 関連記事もチェック

1. Re-Depth Anything: Test-Time Depth Refinement via Self-Supervised Re-lighting

著者: Ananta R. Bhattarai, Helge Rhodin

Monocular depth estimation remains challenging as recent foundation models, such as Depth Anything V2 (DA-V2), struggle with real-world images that are far from the training distribution. We introduce Re-Depth Anything, a test-time self-supervision framework that bridges this domain gap by fusing DA…

論文を読む →

2. Adversarial Robustness of Vision in Open Foundation Models

著者: Jonathon Fox, William J Buchanan, Pavlos Papadopoulos

With the increase in deep learning, it becomes increasingly difficult to understand the model in which AI systems can identify objects. Thus, an adversary could aim to modify an image by adding unseen elements, which will confuse the AI in its recognition of an entity. This paper thus investigates t…

論文を読む →

3. When Reasoning Meets Its Laws

著者: Junyu Zhang, Yifan Sun, Tianang Leng

Despite the superior performance of Large Reasoning Models (LRMs), their reasoning behaviors are often counterintuitive, leading to suboptimal reasoning capabilities. To theoretically formalize the desired reasoning behaviors, this paper presents the Laws of Reasoning (LoRe), a unified framework tha…

論文を読む →

1. QKV-Core/QKV-Core

"Adaptive Hybrid Quantization Framework for deploying 7B+ LLMs on low-VRAM devices (e.g., GTX 1050). Features surgical block alignment and Numba-accelerated inference.

⭐ 14 stars | 🔀 0 forks

リポジトリを見る →

2. JarvisPei/SCOPE

SCOPE: Self-evolving Context Optimization via Prompt Evolution – A framework for automatic prompt optimization

⭐ 10 stars | 🔀 2 forks

リポジトリを見る →

3. AmirhosseinHonardoust/Machine-Learning-Warning-Systems

A long-form article and practical framework for designing machine learning systems that warn instead of decide. Covers regimes vs decimals, levers over labels, reversible alerts, anti-coercion UI patterns, auditability, and the “Warning Card” template, so ML preserves human agency while staying useful under uncertainty.

⭐ 8 stars | 🔀 0 forks

リポジトリを見る →

4. AmirhosseinHonardoust/Tumor-Doppelganger-Studio

Similarity-first interpretability studio for breast tumor samples: pick a case, find its closest “twins” (benign/malignant look-alikes), visualize neighborhood structure, compare feature fingerprints, and run minimal-change counterfactual edits toward a target class. Educational demo only, not for diagnosis.

⭐ 7 stars | 🔀 0 forks

リポジトリを見る →

5. AmirhosseinHonardoust/Physiological-Debt-Accumulation-Engine

A systems-level analysis engine that models sleep as a recovery debt process rather than a nightly outcome. Using physiological traits and ecological pressure signals, it estimates predicted sleep need, quantifies sleep debt, and visualizes how stress accumulates silently before visible fatigue or failure occurs.

⭐ 7 stars | 🔀 0 forks

リポジトリを見る →

📚 関連記事もチェック