多模态持续学习藏有“隐性遗忘”：模型精度不变，但跨模态对齐已悄悄失效，值得关注。
arXiv:2607.02020v1 Announce Type: new Abstract: Multimodal large language models must continually adapt to evolving tasks and domains, yet standard co…
未配对数据也能挖掘生物标志物？CALM提出可解释跨模态对齐新方法，入选MICCAI 2026。
arXiv:2607.01656v1 Announce Type: new Abstract: The interaction between brain structure and genetic influences is key to understanding neuropsychiatri…
首次提出将酉算子映射到LLM可理解的形式，探索AI理解量子世界的新路径
arXiv:2606.13811v1 Announce Type: cross Abstract: Can Large Language Models (LLMs) understand and reason about quantum operators? Despite their remark…
最新研究发现，现代大语言模型与人类脑电图在情感效价轴上存在共享的饱和度规律，揭示AI与大脑情感表征的深层统一性。
arXiv:2606.00129v1 Announce Type: new Abstract: Large language models (LLMs) have emerged as powerful representation learners whose internal features …
针对视觉语言模型的单样本黑盒成员推理攻击，利用跨模态语义对齐检测模型记忆风险，为数据隐私安全提供新评估方法。
arXiv:2605.17341v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have achieved remarkable success, yet their reliance on massive datasets…
医学指代图像分割遇上半监督学习，跨模态对齐降低标注成本，精准融合文本与视觉。
arXiv:2605.15720v1 Announce Type: cross Abstract: Medical referring image segmentation (MRIS) requires pixel-level masks aligned with textual descript…