Evaluating recommender systems before deployment requires simulators that remain realistic over long, closed-loop interaction cycles.
Current LLM-based agents generally function as surface imitators that generate realistic short-term responses but fail to capture the underlying user dynamics such as fatigue, affect, and shifting preferences.
We propose PsyBer-Agent, a psychology-inspired simulator that models these dynamics through its Psy-Engine.
This engine represents each user with three evolving, model-defined latent states.
To calibrate simulated behavior against real interaction logs, we align log-conditioned reference trajectories with simulator-generated latent-state trajectories via Unbalanced Gromov--Wasserstein optimal transport.
This approach provides structured supervision for a lightweight policy without the computational burden of fine-tuning the backbone LLM.
We also introduce WebSim and the PsyBer Benchmark, which encompass multimodal scenarios and seven recommender backbones.
Our framework evaluates behavioral fidelity, latent-state consistency, robustness, and responses to social-cue perturbations.
On the tested horizons, PsyBer-Agent shows lower behavioral discrepancy than the prompt-only simulators while maintaining stable performance under large-scale concurrent simulation for up to 5M queued simulation tasks under bounded concurrency.
Code is available in the supplementary material and at: PsyBer-Agent: https://anonymous.4open.science/r/CC66F/, WebSim Platform: https://anonymous.4open.science/r/D8EAX/.