Can an AI system learn to simulate realistic human behavior by evaluating and improving its own outputs? This hands-on demonstration invites participants to build a self-improving user simulator using a working scaffold and a closed-loop evaluation pipeline.

Participants begin with an agent that generates synthetic users and conversational interactions from persona specifications. They then connect the agent to external evaluation signals, including held-out user-simulation benchmarks and a discriminator trained to distinguish real human interactions from synthetic ones. Based on these signals, the system iteratively revises its persona-generation code, generates new samples, and measures whether its outputs become more realistic.

Unlike unconstrained self-critique or open-ended evolutionary search, the system uses explicit evaluation criteria, versioned code changes, and persistent experiment results to make progress measurable and failures inspectable. Participants will investigate how simulation quality changes over iterations, where evaluators fail, and how optimization can produce realistic-looking but behaviorally inaccurate outputs.

The demonstration concludes with a room-wide “Spot the Human” challenge in which attendees distinguish real human responses from generated ones. By combining hands-on engineering with an interactive evaluation challenge, the demonstration explores user simulation as a research problem at the intersection of autonomous data generation, model evaluation, and alignment.

Participants will leave with a working self-improvement loop, an understanding of its evaluation methodology and limitations, and an artifact they can continue developing beyond the Expo.