LANDER-Nav: Progressive Landmark-Schema Grounding for Multi-Granularity Open-Vocabulary Navigation

Language-conditioned goal navigation is extending beyond category-level search toward multi-granularity goals disambiguated by landmarks, context, and spatial relations. Current methods encode instructions as a single joint query, conflating these constraints and often causing agents to bind the wrong same-category instance or stop at an unverifiable viewpoint. We disentangle each instruction into a landmark schema comprising target, anchor, context, and relation fields, and propose LANDER-Nav, a progressive grounding framework that preserves these role-specific constraints throughout navigation. Under this schema, LANDER-Nav coordinates three coupled decisions: Evidence Grounding decides where to explore by selecting frontiers likely to reveal missing evidence; Entity Grounding decides which entity to bind through landmark-anchored spatial voting over object memory; and Endpoint Grounding decides where to stop by choosing an executable viewpoint that verifies the required relation. Extensive experiments on LangMap and HM3D-OVON demonstrate that LANDER-Nav improves language-conditioned navigation, with particularly strong gains on long-horizon multi-goal tasks and context- and relation-dependent goals, while improving category-level open-vocabulary performance. Our code will be released.