AAAI

Bidirectional Channel-selective Semantic Interaction for Semi-Supervised Medical Segmentation

双向通道选择性语义交互用于半监督医学分割

Kaiwen Huang, Yizhe Zhang, Yi Zhou, Tianyang Xu, Tao Zhou

AI总结 本文提出双向通道选择性语义交互框架，通过语义-空间扰动、通道选择性路由器和双向通道交互提升半监督医学图像分割性能。

Comments Accepted to AAAI 2026. Code at: https://github.com/taozh2017/BCSI

SPAN: Benchmarking and Improving Cross-Calendar Temporal Reasoning of Large Language Models

SPAN：大型语言模型跨日历时间推理的基准测试与改进

Zhongjian Miao, Hao Fu, Chen Wei

机构 * Li Auto（利亚特） 纠错

AI总结 SPAN通过开发时间代理提升大型语言模型跨日历时间推理能力，实验显示其准确率高达95.31%

Comments Accepted at the AAAI 2026 conference. This version includes the supplementary appendix

Symbolic Planning and Multi-Agent Path Finding in Extremely Dense Environments with Unassigned Agents

符号规划与在极度密集环境中无分配代理的多智能体路径寻找

Bo Fu, Zhe Chen, Rahul Chandan, Alex Barbosa, Michael Caldara, Joey Durham, Federico Pecora

AI总结 本文提出五种基于搜索的算法，用于解决在极度密集环境中无分配代理的多智能体路径寻找问题，通过图搜索方法高效生成重新排列计划。

Comments AAAI Conference on Artificial Intelligence (AAAI-26)

CliCARE: Grounding Large Language Models in Clinical Guidelines for Decision Support over Longitudinal Cancer Electronic Health Records

CliCARE: 在纵向癌症电子健康记录上基于临床指南 grounding 大型语言模型以支持决策

Dongchen Li, Jitao Liang, Wei Li, Xiaoyu Wang, Longbing Cao, Kun Yu

AI总结 CliCARE 通过将纵向癌症 EHRs 转换为时序知识图谱并结合临床指南，为肿瘤科医生提供证据支持的决策支持。

Comments Accepted in AAAI Conference on Artificial Intelligence (AAAI-26, Oral)

Branch, or Layer? Zeroth-Order Optimization for Continual Learning of Vision-Language Models

分支还是层？零阶优化用于视觉-语言模型的持续学习

Ziwei Liu, Borui Kang, Wei Li, Hangjie Yuan, Yanbing Yang, Wenbin Li, Yifan Zhu, Tao Feng, Jun Luo

AI总结 本文提出一种基于零阶优化的视觉-语言模型持续学习方法，通过模态感知策略提升模型逃避局部极小值的能力，实验表明在四个基准测试中取得最佳性能。

Comments Published in the Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2026)

Detect All-Type Deepfake Audio: Wavelet Prompt Tuning for Enhanced Auditory Perception

检测所有类型的深度伪造音频：用于增强听觉感知的小波提示微调

Yuankun Xie, Ruibo Fu, Zhiyong Wang, Xiaopeng Wang, Songjun Cao, Long Ma, Haonan Cheng, Long Ye

AI总结 本文提出小波提示微调方法，用于增强所有类型深度伪造音频检测性能，通过自监督学习优化模型参数，实现更高效的跨类型音频检测。

Comments Accepted to AAAI 2026