SAFE-EQA: Semantic-Aware Efficient Exploration for Embodied Question Answering

Embodied Question Answering (EQA) requires an agent toexplore unseen environments, gather visual evidence, and answer natural-language questions. Efficient exploration requires both global planningand task-aware prioritization of informative regions. However, existingfrontier-based methods typically rely on greedy, step-wise decisions, whichoften cause redundant motion and frequent Vision-Language Model (VLM)queries. We propose SAFE-EQA, a semantic-aware framework that im-proves exploration efficiency through globally coherent path planning andreduced VLM interaction. SAFE-EQA incrementally builds a landmarkgraph to capture scene topology and a query-conditioned semantic mapto estimate task relevance of different regions. To support comprehen-sive, question-oriented exploration, we formulate planning as a Semantic-Aware Traveling Salesman Problem (TSP), which optimizes the visita-tion order over discovered landmarks. We further introduce an adaptivereplanning mechanism that selectively interrupts local navigation andre-optimizes trajectories when higher-priority frontiers emerge. Exten-sive experiments on OpenEQA, EXPRESS-Bench and HM-EQA showthat SAFE-EQA matches or outperforms state-of-the-art EQA methodswhile reducing VLM token consumption by 27.8–45.6%.