EGNav: Evidence-Guided Memory Repair for Zero-Shot Goal-Oriented Navigation

Zero-shot goal-oriented navigation requires more than exploring new regions: a previously visited location may still contain a target that was misidentified or remained outside the agent's view. This ambiguity makes it difficult to determine whether to continue exploring or revisit a potential target location. We propose EGNav, a zero-shot navigation framework that treats goal search as sequential evidence management. At its core is a task-conditioned evidence memory that associates spatially grounded candidates with direct, contextual, and negative evidence. Source- and visibility-aware updates maintain evolving belief and uncertainty, distinguishing contextual support from direct target matches and reducing the influence of negative observations obtained under limited visibility. A unified memory-repair policy then ranks unexplored frontiers and memory candidates using accumulated belief, expected information gain, and execution costs. Within this policy, direct repair re-examines previously observed target-like instances, while contextual repair revisits locations that may contain an unseen target. Both modes update the same evidence state and participate in a shared decision process. Experiments across object-, image-, and text-goal navigation on HM3D and MP3D show that EGNav outperforms the compared universal zero-shot methods, with further gains over the compared training-free baselines on GOAT-Bench. Ablations support the complementarity of the two repair modes and the contributions of evidence updating and candidate ranking.