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- 🚀 AI技術の最新動向 – 2026年4月11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 📌 関連記事もチェック 📚 最新研究論文 1. Act Wisely: Cultivating Meta-Cognitive Tool Use in Agentic Multimodal Models 著者: Shilin Yan, Jintao Tong, Hongwei Xue The advent of agentic multimodal models has empowered systems to actively interact with external environments. However, current agents suffer from a profound meta-cognitive deficit: they struggle to arbitrate between leveraging internal knowledge and querying external utilities. Consequently, they f… 論文を読む → 2. SIM1: Physics-Aligned Simulator as Zero-Shot Data Scaler in Deformable Worlds 著者: Yunsong Zhou, Hangxu Liu, Xuekun Jiang Robotic manipulation with deformable objects represents a data-intensive regime in embodied learning, where shape, contact, and topology co-evolve in ways that far exceed the variability of rigids. Although simulation promises relief from the cost of real-world data acquisition, prevailing sim-to-re… 論文を読む → 3. Seeing but Not Thinking: Routing Distraction in Multimodal Mixture-of-Experts 著者: Haolei Xu, Haiwen Hong, Hongxing Li Multimodal Mixture-of-Experts (MoE) models have achieved remarkable performance on vision-language tasks. However, we identify a puzzling phenomenon termed Seeing but Not Thinking: models accurately perceive image content yet fail in subsequent reasoning, while correctly solving identical problems p… 論文を読む → 📚 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4月11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
- 📌 関連記事もチェック
📚 最新研究論文
1. Act Wisely: Cultivating Meta-Cognitive Tool Use in Agentic Multimodal Models
著者: Shilin Yan, Jintao Tong, Hongwei Xue
The advent of agentic multimodal models has empowered systems to actively interact with external environments. However, current agents suffer from a profound meta-cognitive deficit: they struggle to arbitrate between leveraging internal knowledge and querying external utilities. Consequently, they f…
2. SIM1: Physics-Aligned Simulator as Zero-Shot Data Scaler in Deformable Worlds
著者: Yunsong Zhou, Hangxu Liu, Xuekun Jiang
Robotic manipulation with deformable objects represents a data-intensive regime in embodied learning, where shape, contact, and topology co-evolve in ways that far exceed the variability of rigids. Although simulation promises relief from the cost of real-world data acquisition, prevailing sim-to-re…
3. Seeing but Not Thinking: Routing Distraction in Multimodal Mixture-of-Experts
著者: Haolei Xu, Haiwen Hong, Hongxing Li
Multimodal Mixture-of-Experts (MoE) models have achieved remarkable performance on vision-language tasks. However, we identify a puzzling phenomenon termed Seeing but Not Thinking: models accurately perceive image content yet fail in subsequent reasoning, while correctly solving identical problems p…
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