AAAI

VitalDiagnosis: AI-Driven Ecosystem for 24/7 Vital Monitoring and Chronic Disease Management

VitalDiagnosis：基于AI的生态系统，用于24/7生命体征监测和慢性病管理

Zhikai Xue, Tianqianjin Lin, Pengwei Yan, Ruichun Wang, Yuxin Liu, Zhuoren Jiang, Xiaozhong Liu

AI总结 VitalDiagnosis利用AI技术，通过整合可穿戴设备数据与大语言模型，实现24/7生命体征监测和慢性病管理的主动参与，提升患者自我管理能力并减少临床工作量。

Comments Accepted by AAAI 2026 Demo

Language Models Entangle Language and Culture

语言模型融合语言与文化

Shourya Jain, Paras Chopra

机构 * Lossfunk 纠错

AI总结 研究发现语言选择影响LLM生成答案的文化上下文，导致低资源语言回答质量较低。

Comments Accepted at LM4UC Workshop at AAAI'26, Submitted to ACL 2026. 17 pages, 7 figures

Heterogeneous Uncertainty-Guided Composed Image Retrieval with Fine-Grained Probabilistic Learning

异质不确定性引导的复合图像检索与细粒度概率学习

Haomiao Tang, Jinpeng Wang, Minyi Zhao, Guanghao Meng, Ruisheng Luo, Long Chen, Shu-Tao Xia

AI总结 本文提出异质不确定性引导范式，通过细粒度概率学习提升复合图像检索的鲁棒性和准确性。

Comments Accepted for publication and oral presentation at AAAI 2026

Radiation-Preserving Selective Imaging for Pediatric Hip Dysplasia: A Cross-Modal Ultrasound-Xray Policy with Limited Labels

辐射保护的儿童髋关节发育不良选择性成像：一种跨模态超声-X射线策略（有限标注）

Duncan Stothers, Ben Stothers, Emily Schaeffer, Kishore Mulpuri

AI总结 本文提出了一种跨模态超声-X光策略，通过有限标注实现儿童髋关节发育不良的可解释测量和选择性成像。

Comments Accepted (with oral presentation) to the AAAI 2026 AI for Medicine and Healthcare Bridge Program Awarded Best Paper Runner-Up at the AAAI 2026 AI for Medicine and Healthcare Bridge Program

Divide, Conquer and Unite: Hierarchical Style-Recalibrated Prototype Alignment for Federated Medical Segmentation

分割、征服与联合：基于层次化风格校正原型对齐的联邦医学分割

Xingyue Zhao, Wenke Huang, Xingguang Wang, Haoyu Zhao, Linghao Zhuang, Anwen Jiang, Guancheng Wan, Mang Ye

AI总结 FedBCS通过层次化风格校正原型对齐方法，解决联邦学习中医学分割的特征异质性问题，提升模型鲁棒性与分割精度。

Comments Accepted at AAAI-26

DECOR: Deep Embedding Clustering with Orientation Robustness

DECOR：具有方向鲁棒性的深度嵌入聚类

Fiona Victoria Stanley Jothiraj, Arunaggiri Pandian Karunanidhi, Seth A. Eichmeyer

机构 * Micron Technology（micron技术公司） 纠错

AI总结 DECOR通过引入方向鲁棒性，实现了在复杂、不平衡的晶圆数据中可靠且高效的聚类，提升自动化视觉检测系统的性能。

Comments Accepted to the KGML Bridge at AAAI 2026 (non-archival)

Modelling the Effects of Hearing Loss on Neural Coding in the Auditory Midbrain with Variational Conditioning

利用变分条件建模听觉脑干中听觉损失对神经编码的影响

Lloyd Pellatt, Fotios Drakopoulos, Shievanie Sabesan, Nicholas A. Lesica

AI总结 本文提出变分条件模型，直接从听觉中脑神经活动记录中学习听觉损失的空间编码，准确预测神经响应方差并模拟真实活动，为未来参数化听觉损失补偿模型提供基础。

Comments 12 pages, 3 figures, presented at AAAI 2026

Embracing Ambiguity: Bayesian Nonparametrics and Stakeholder Participation for Ambiguity-Aware Safety Evaluation

拥抱模糊性：基于贝叶斯非参数方法和利益相关者参与的模糊性感知安全评估

Yanan Long

AI总结 本文提出了一种基于贝叶斯非参数方法和利益相关者参与的框架，用于模糊性感知的安全评估，通过量化尾部风险和整合利益相关者偏好提升生成模型的可信度。

Comments AAAI 2026 workshop MURE

On the Exponential Convergence for Offline RLHF with Pairwise Comparisons

关于通过成对比较进行离线RLHF的指数收敛性

Zhirui Chen, Vincent Y. F. Tan

AI总结 本文提出了一种在离线RLHF中通过成对比较实现指数收敛的算法RL-LOW，并推导了实例依赖的下界。

Comments Accepted as an oral presentation at AAAI 2026 (AI Alignment Track)