EvolveNav

EvolveNav: Self-evolving memory and preflection for zero-shot object-goal navigation

EvolveNav is a new framework for Zero-Shot Object-Goal Navigation (ZS-OGN) that enables test-time improvement through a self-evolving agentic rule memory built from past trajectories. A retrieval strategy based on upper confidence bound balances semantic relevance and historical success when selecting rules, while a memory-guided preflection module forecasts action outcomes before execution to reduce inefficient exploration. The method achieves a 10.1% improvement in success rate over existing zero-shot baselines with fewer unnecessary steps.