# Hierarchical GFM–VLM fusion

This work introduces AdaGeoVLN, a streaming vision-language navigation framework that couples VGGT geometry-foundation-model representations at depths 11, 17, and 23 to the first three Qwen3.5-4B decoder layers and retains historical VGGT global-attention KV states under a fixed budget according to instruction relevance, geometric confidence, and transition novelty, achieving 55.7%/51.4% and 54.1%/44.7% SR/SPL on R2R-CE and RxR-CE Val-Unseen with a single RGB stream and no additional navigation-specific external data, with ablations showing that multi-depth coupling substantially outperforms repeated terminal-feature injection at matched fusion locations and that bounded navigation-aware retention preserves navigation performance while reducing GFM-KV memory.

This work introduces AdaGeoVLN, a streaming vision-language navigation framework that couples VGGT geometry-foundation-model representations at depths 11, 17, and 23 to the first three Qwen3.5-4B decoder layers and retains historical VGGT global-attention KV states under a fixed budget according to instruction relevance, geometric confidence, and transition novelty, achieving 55.7%/51.4% and 54.1%/44.7% SR/SPL on R2R-CE and RxR-CE Val-Unseen with a single RGB stream and no additional navigation-specific external data, with ablations showing that multi-depth coupling substantially outperforms repeated terminal-feature injection at matched fusion locations and that bounded navigation-aware retention preserves navigation performance while reducing GFM-KV memory.