AnnaAgent: Dynamic Evolution Agent System with Multi-Session Memory for Realistic Seeker Simulation
Ming Wang, Peidong Wang, Lin Wu, Xiaocui Yang, Daling Wang, Shi Feng, Yuxin Chen, Bixuan Wang, Yifei Zhang

TL;DR
AnnaAgent is a novel AI system that simulates seekers in mental health counseling with dynamic emotional states and multi-session memory, improving realism over previous models.
Contribution
It introduces a dynamic agent with tertiary memory and emotion modulation trained on real dialogues, advancing realistic multi-session seeker simulation in mental health AI.
Findings
Achieves more realistic seeker simulation than existing baselines.
Effective integration of short-term and long-term memory across sessions.
Validated through automated and manual evaluation methods.
Abstract
Constrained by the cost and ethical concerns of involving real seekers in AI-driven mental health, researchers develop LLM-based conversational agents (CAs) with tailored configurations, such as profiles, symptoms, and scenarios, to simulate seekers. While these efforts advance AI in mental health, achieving more realistic seeker simulation remains hindered by two key challenges: dynamic evolution and multi-session memory. Seekers' mental states often fluctuate during counseling, which typically spans multiple sessions. To address this, we propose AnnaAgent, an emotional and cognitive dynamic agent system equipped with tertiary memory. AnnaAgent incorporates an emotion modulator and a complaint elicitor trained on real counseling dialogues, enabling dynamic control of the simulator's configurations. Additionally, its tertiary memory mechanism effectively integrates short-term and…
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Taxonomy
TopicsArtificial Intelligence in Games
