@inproceedings{wang-etal-2025-annaagent,
title = "{A}nna{A}gent: Dynamic Evolution Agent System with Multi-Session Memory for Realistic Seeker Simulation",
author = "Wang, Ming and
Wang, Peidong and
Wu, Lin and
Yang, Xiaocui and
Wang, Daling and
Feng, Shi and
Chen, Yuxin and
Wang, Bixuan and
Zhang, Yifei",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1192/",
doi = "10.18653/v1/2025.findings-acl.1192",
pages = "23221--23235",
ISBN = "979-8-89176-256-5",
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 long-term memory across sessions. Evaluation results, both automated and manual, demonstrate that AnnaAgent achieves more realistic seeker simulation in psychological counseling compared to existing baselines. The ethically reviewed and screened code can be found on [https://github.com/sci-m-wang/AnnaAgent](https://github.com/sci-m-wang/AnnaAgent)."
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<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 long-term memory across sessions. Evaluation results, both automated and manual, demonstrate that AnnaAgent achieves more realistic seeker simulation in psychological counseling compared to existing baselines. The ethically reviewed and screened code can be found on [https://github.com/sci-m-wang/AnnaAgent](https://github.com/sci-m-wang/AnnaAgent).</abstract>
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%0 Conference Proceedings
%T AnnaAgent: Dynamic Evolution Agent System with Multi-Session Memory for Realistic Seeker Simulation
%A Wang, Ming
%A Wang, Peidong
%A Wu, Lin
%A Yang, Xiaocui
%A Wang, Daling
%A Feng, Shi
%A Chen, Yuxin
%A Wang, Bixuan
%A Zhang, Yifei
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F wang-etal-2025-annaagent
%X 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 long-term memory across sessions. Evaluation results, both automated and manual, demonstrate that AnnaAgent achieves more realistic seeker simulation in psychological counseling compared to existing baselines. The ethically reviewed and screened code can be found on [https://github.com/sci-m-wang/AnnaAgent](https://github.com/sci-m-wang/AnnaAgent).
%R 10.18653/v1/2025.findings-acl.1192
%U https://aclanthology.org/2025.findings-acl.1192/
%U https://doi.org/10.18653/v1/2025.findings-acl.1192
%P 23221-23235
Markdown (Informal)
[AnnaAgent: Dynamic Evolution Agent System with Multi-Session Memory for Realistic Seeker Simulation](https://aclanthology.org/2025.findings-acl.1192/) (Wang et al., Findings 2025)
ACL
- Ming Wang, Peidong Wang, Lin Wu, Xiaocui Yang, Daling Wang, Shi Feng, Yuxin Chen, Bixuan Wang, and Yifei Zhang. 2025. AnnaAgent: Dynamic Evolution Agent System with Multi-Session Memory for Realistic Seeker Simulation. In Findings of the Association for Computational Linguistics: ACL 2025, pages 23221–23235, Vienna, Austria. Association for Computational Linguistics.