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cs.AI2026

Forget What's Sensitive, Remember What Matters: Token-Level Differential Privacy in Memory Sculpting for Continual Learning

Bihao Zhan, Jie Zhou, Junsong Li +9

Continual Learning (CL) models, while adept at sequential knowledge acquisition, face significant and often overlooked privacy challenges due to accumulating diverse information. T…

cs.AI2026

PsychAgent: An Experience-Driven Lifelong Learning Agent for Self-Evolving Psychological Counselor

Yutao Yang, Junsong Li, Qianjun Pan +7

Existing methods for AI psychological counselors predominantly rely on supervised fine-tuning using static dialogue datasets. However, this contrasts with human experts, who contin…

cs.AI2026

Building Self-Evolving Agents via Experience-Driven Lifelong Learning: A Framework and Benchmark

Yuxuan Cai, Yipeng Hao, Jie Zhou +14

As AI advances toward general intelligence, the focus is shifting from systems optimized for static tasks to creating open-ended agents that learn continuously. In this paper, we i…

cs.AI2026

PsychEval: A Multi-Session and Multi-Therapy Benchmark for High-Realism AI Psychological Counselor

Qianjun Pan, Junyi Wang, Jie Zhou +10

To develop a reliable AI for psychological assessment, we introduce \texttt{PsychEval}, a multi-session, multi-therapy, and highly realistic benchmark designed to address three key…

cs.AI2025

Black-box Model Merging for Language-Model-as-a-Service with Massive Model Repositories

Shilian Chen, Jie Zhou, Tianyu Huai +9

Model merging refers to the process of integrating multiple distinct models into a unified model that preserves and combines the strengths and capabilities of the individual models…