Beyond Confidence: Active Falsification for Embodied Question Answering

Embodied question answering (EQA) requires an agent to explore an unfamiliar environment and gather visual evidence before answering. However, question-relevant observations may fail to distinguish competing answers, allowing an early, confident error to persist. We propose Active Falsification for Embodied Question Answering (AFE-EQA), a framework that treats the current answer as a revisable hypothesis and actively seeks observations capable of refuting it. Three-state grounding distinguishes decidable, partial, and not-visible observations to regulate belief updates and evidence retention. A structured counter-evidence memory links testable propositions to their source hypotheses and visual assessments, while falsification-aware imagination guides exploration toward viewpoints that distinguish competing answers. At termination, an offline-trained resolver integrates accumulated visual and trajectory evidence to determine whether the current answer should be revised. These components operate along a single exploration trajectory. Experiments on HM-EQA show improved final answer accuracy under matched conditions, while component ablations and paired analyses characterize the contributions of exploration and terminal revision, including both corrected errors and harmful switches. These findings support active hypothesis testing as a design principle for coupling embodied exploration with evidence-based answer revision.