A Recursive Self-Improving Harness for Training-Free Generalist Navigation

Generalist embodied navigation requires agents to adapt their behavior as task objectives, spatial contexts, and environmental conditions evolve. While recent agentic navigation methods leverage pretrained vision-language models (VLMs) for flexible reasoning, they remain largely task-specific and lack mechanisms for reusing experience across tasks and environments. We introduce RING-Nav, a recursive self-improving (RSI) harness for training-free generalist navigation. RING-Nav maintains a contextualized navigation state (CNS) that unifies task progress, spatial evidence, episodic memory, and tool feedback, enabling a frozen VLM to dynamically orchestrate shared navigation tools. To support continuous improvement across episodes, an RSI experience graph (RSI-EG) records contextualized tool transitions and their outcomes, retrieves relevant past experience, and guides subsequent tool decisions without updating model parameters. RING-Nav thus unifies spatially grounded tool orchestration (SGTO) with experience-driven self-improvement, enabling a single navigation agent to adapt across diverse tasks and environments. Extensive experiments demonstrate state-of-the-art performance on both individual navigation tasks and heterogeneous multi-stage instructions.