TRACE: Task Completion Determination for Aerial Navigation with Semantic Evidence

Embodied navigation can fail even when an agent reaches the goal region: execution may continue until the agent terminates elsewhere. We study task completion determination in aerial navigation, asking how target-related observations gathered during execution can inform an explicit termination decision. We introduce TRACE, **T**ermination via **R**epeated **A**ssessment of **C**ompletion **E**vidence, a training-free, plug-and-play semantic termination module that attaches to an existing navigation policy without changing its weights. A pretrained vision-language model (VLM) checks the current image against a target description at fixed action-step intervals and reports whether the target is visible. TRACE adds a STOP after consecutive positive reports, while preserving the navigator's own STOP decisions and otherwise leaving movement actions unchanged. The module uses no privileged goal-distance input. Visibility reports guide stopping, while final success is evaluated separately at the terminal position. We evaluate TRACE with frozen OpenFly-Agent and three pretrained VLMs serving as semantic judgers across two simulators. On routes starting outside the goal region, consecutive confirmation yields higher mean success than first-positive stopping across all matched comparisons, while dataset-specific selected configurations also outperform the original navigator with all three VLMs, improving successful termination within the goal region.