arXiv Computation and Language
Sep 1

CARE: Privacy-Compliant Agentic Reasoning with Evidence Discordance

The paper introduces MIMIC-DOS, a dataset derived from MIMIC-IV that focuses on ICU cases where patient symptoms and medical signs are discordant. It presents CARE, a privacy‑compliant multi‑stage agentic reasoning framework that uses a proprietary LLM to generate structured categories and transitions, while a local LLM performs evidence acquisition and decision‑making. In retrospective evaluations on MIMIC‑DOS, CARE outperforms other LLMs and agentic workflows, demonstrating stronger handling of conflicting clinical evidence while preserving patient privacy.

By Haochen Liu, Weien Li, Rui Song, Zeyu Li, Chun Jason Xue, Xiao-Yang Liu, Sam Nallaperuma-Herzberg, Xue Liu, Ye Yuan
arXiv Machine Learning
Jun 9

Federated Large Language Models: Current Progress and Future Directions

arXiv:2409. 15723v3 Announce Type: replace Abstract: Large Language Models have achieved impressive performance across diverse applications, yet their training typically depends on centralized data collection, raising serious privacy and governance concerns.

By Yuhang Yao, Jianyi Zhang, Junda Wu, Chengkai Huang, Yu Xia, Tong Yu, Ruiyi Zhang, Sungchul Kim, Ryan Rossi, Ang Li, Lina Yao, Julian McAuley, Yiran Chen, Carlee Joe-Wong