VerNav: Verifier-First Low-Latency Vision-and-Language Navigation
Vision-and-Language Navigation (VLN) requires an agent to navigate through unseen 3D environments according to natural-language instructions. Explicit reasoning can improve instruction understanding and semantic grounding, but autoregressive generation at every step accumulates large decision-stage latency over multi-step navigation. We propose VerNav, a verifier-first framework for low-latency LLM-based VLN. The verifier reduces decision-stage latency by replacing per-step autoregressive generation with batched action verification, while an entropy-based adaptive generator is invoked only for uncertain decisions to produce compact state evidence. To further improve navigation performance...
| Authors | Zhixin Wang, Chengzheyi Yao, Leyuan Liu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 1 September 2026 |
| Last updated at source | 1 September 2026 |
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