聚焦智能体任务，提出以对象为中心的新颖环境建模方法，提升复杂场景下的感知与推理能力。
arXiv:2607.02846v1 Announce Type: new Abstract: Large language model (LLM) agents can improve through accumulated experience, but free-form textual me…
用可验证解剖证据为医学多模态大模型装上“可信护栏”，感知-推理协同治理框架获MICCAI 2026早期接收（Top 9%）。
arXiv:2607.00060v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) show strong promise for clinical VQA and radiology report gen…
医学深度伪造检测新方法，通过伪造感知推理实现高可解释性与鲁棒性，为医疗影像安全提供创新思路。
arXiv:2603.18577v2 Announce Type: replace Abstract: Text-guided image editors can now manipulate authentic medical scans with high fidelity, enabling …
提升观察精度反而降低问题解决能力——这项研究挑战了具身LLM的传统认知，揭示保真度与推理之间的意外权衡。
arXiv:2605.20072v1 Announce Type: new Abstract: Large Language Models are increasingly proposed as cognitive components for robotic systems, yet their…