A nna A gent: Dynamic Evolution Agent System with Multi-Session Memory for Realistic Seeker Simulation (2025)

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).

## introduction the issue of mental disorders is a critical challenge for ongoing society @xcite . effective psychological counseling plays a crucial role in addressing these challenges, yet the availability of trained therapists remains limited @xcite . researchers introduce ai to provide mental health support to alleviate the shortage of human counselors @xcite . as one of the most important constituents of mental health research, seekers are crucial for data construction, effect evaluation, and ethical analyses. however, the introduction of large numbers of real seekers for research not only creates a high-cost burden but may also lead to ethical concerns. thus, researchers design llm-based conversational agents (cas) to simulate seekers. @xcite designed prompts for cas based on the psychiatrist's goals of communicating with seekers. similarly to them, @xcite designed specialized prompts and invited psychotherapy professionals to participate in the adaptation of the prompts. @xcite initialized a batch of seeker agents using seeker profiles generated by @xcite collected based on the d 4 dataset @xcite . accommodating different profiles, like seekers' personal backgrounds and symptom descriptions in prompts, is widely used to simulate the conversational behavior of real seekers. undeniably, these works have made a significant contribution to the study of seeker simulation, addressing issues like cas' easily endorsing advice, sycophantic obedience @xcite , and emotional flatness @xcite . however, cas designed with existing methods are still not realistic enough, blocking the further development of ai for mental health research. as shown in figure 1a , a real seeker is usually more sensitive due to the psychological disorders suffered, and there is usually a noticeable dynamic evolution of the emotional state during the counseling process. nevertheless, existing simulators fail to replicate this dynamic evolution. throughout the counseling process, their emotions are usually maintained at the initially emotion state that's really impressive, you've come a long way. really? i've been hearing you say that makes me feel so much more grounded all of a sudden! thanks, i actually have a lot to live up to, but i'll keep working on it. i think you might be a little oversensitive. you have no idea what i've been through! i come here with hope every time, and you dismiss me so easily! oh, maybe, i do overthink things sometimes. most negative most positive neutral simulator real human thanks. that would be a good idea. does the last method work? does the last method work? yes, it does work! i meditate before bed and sleep much better. it's a really good idea to drink a glass of warm milk before going to bed. set state, without noticeable changes. moreover, psychological counseling is typically a long-term, multi-session task, and counselors usually need to review previous session topics, discuss recent circumstances with seekers to build trust, assess progress, and analyze in depth the core dilemmas of seekers @xcite . however, existing methods do not provide cas with multi-session memories, which may lead to confusion and hallucinatory responses from cas when counselors mention the topic of previous sessions. in figure 1b , when the counselor attempts to verify the effectiveness of the suggestions made in the previous counseling session, the simulator, which has no multi-session memory, gives a relevant but incorrect response. thus, to simulate seekers more realistically, we present two pressing challenges: dynamic evolution and multi-session memory. figure 1 visualizes these two challenges with examples of scenes from counseling. under the guidance of licensed counselors, we systematically characterize the dynamic evolution of counseling processes through two key dimensions: emotional fluctuations and progressive shifts in clients' understanding of their "chief complaints". in addition, we divide different sessions' memories in terms of time and define a tertiary memory mechanism to schedule multi-session memory. specifically, we introduce annaagent, an emotional and cognitive dynamic agent system with tertiary memory. annaagent learns the evolutionary patterns of seekers' emotions and complaints from real counseling data and dynamically controls them in the seeker simulation. in addition, it has a tertiary memory mechanism that divides multi-session memories by time and coordinates real-time, short-term, and long-term memories through different scheduling methods. with annaagent and diverse counselor models, conversations of psychological counseling are generated. the performance on the metrics of anthropomorphism, personality fidelity, and accuracy of previous session cognitive indicates that annaagent is able to simulate the seeker more realistically compared to baselines. by simulating more realistic seeker behavior, annaagent opens up new paths for psychological research and counselor training, while setting a benchmark for the ethical use of ai in sensitive areas. the main contributions of this work are: llms have a wide range of promising applications in mental health care and can help improve diagnostic accuracy, treatment effectiveness, and service accessibility @xcite . dialogue systems based on llms, like chatcounselor @xcite and serena @xcite , are designed to provide mental health counseling and support. extensive research on ai for mental health provides a solid foundation and broad application scenarios for this research. besides these researches, this work mainly focuses on seeker simulation in mental health research but also involves llm-based role-playing and multi-agent systems. ## experiments we experimentally verify the superiority of annaagent over the baseline methods for seeker simulation. all ethically reviewed and processed codes foot_1 can be found on https://github.com/sci-mwang/annaagent . ## conclusion we present the dynamic evolution and multisession memory issues in more realistic seeker simulations. to solve these issues, we introduced annaagent, an emotional and cognitive dynamic agent system with tertiary memory. annaagent simulates emotional changes in counseling through real-time emotional inference combined with random perturbations and generates compliant elicitation chains to control cognitive changes in seekers' symptoms. in addition, it coordinates memory during and between sessions with a tertiary memory system. experimental evaluations indicated that annaagent can more realistically simulate seekers compared to baseline methods. in addition, we experimentally demonstrate the necessity of different components and the generalization of annaagent. this research provides innovative solutions to alleviate the global shortage of mental health resources.