VERA: Verification and Environmental Risk Awareness for Functional Model Induction through Open-Ended Web Exploration

Open-ended GUI exploration offers a way to acquire application knowledge before downstream tasks are specified. However, successful tool execution does not establish that a functional claim is supported, and the interactions used to test unknown functionality may involve environmental risks. We introduce VERA, a new framework for constructing functional models through evidence-based knowledge admission and contextual risk awareness. Each function is represented as a hypothesis with an observable outcome specified before execution. VERA compares subsequent interface observations against this expectation, admits supported claims, and retains unresolved or unsuccessful attempts for inspection and further exploration. Persistent hypothesis pools and context recovery sustain evidence collection, while risk annotations associate selected actions with their acquisition context. In controlled web exploration studies, evidence-based admission raises knowledge precision from 82.5% to 97.0% while retaining 32 of 33 human-supported functions. Full exploration attains greater functional coverage at additional cost, while visual context improves risk categorization on an external context challenge. These results demonstrate VERA's effectiveness in acquiring reliable functional knowledge while making the environmental risks of its acquisition explicit and traceable. The source code is available at https://anonymous.4open.science/r/AI-Web-explorer-4516.