EviNav: Evidence-Grounded Search and Active Verification for Training-Free Aerial Object Navigation

Large-scale Aerial Object Navigation (Aerial ObjectNav) requires a UAV to locate semantically relevant regions and determine whether an observed candidate is the requested target instance under partial and viewpoint-dependent observations. Existing aerial ObjectNav methods primarily strengthen spatial memory, exploration, and target grounding, while candidate-level evidence is not typically maintained and reused as a unified persistent state across search, verification, and stopping. We present EviNav, a training-free framework that treats candidate evidence as a persistent navigation state connecting evidence-grounded search, persistent target hypotheses, and active verification. EviNav constructs a shared evidence specification that distinguishes search-supporting cues from acceptance-critical evidence, accumulates candidate evidence across observations, and actively acquires complementary views when the available evidence is insufficient. Unresolved evidence guides further observation, contradictory evidence supports candidate rejection, and STOP is authorized when the available evidence provides sufficient support for the target identity under the applicable acceptance conditions. On the complete 1,000-episode, 14-scene UAV-ON test protocol, EviNav achieves a Success Rate (SR) of 27.00%, an Oracle Success Rate (OSR) of 36.60%, and a Success weighted by Path Length (SPL) of 22.98%, improving the previous best full-benchmark SR by 7.50 percentage points without task-specific navigation training. These results indicate that persistent evidence and active verification improve target-acceptance reliability while maintaining effective search and path efficiency in large-scale aerial ObjectNav.