ConvApparel: Measuring and bridging the realism gap in user simulators (opens in new tab)

Modern conversational AI agents can typically handle complex, multi-turn tasks like asking clarifying questions and proactively assisting users. However, they frequently struggle with long interactions, often forgetting constraints or generating irrelevant responses. Improving these systems requires continuous training and feedback, but relying on the "gold standard" of live human testing is prohibitively expensive, time-consuming, and notoriously difficult to scale.