# DecoVLN: Decoupling Observation, Reasoning, and Correction for Vision-and-Language Navigation

arXiv: https://arxiv.org/abs/2603.13133 · HTML: https://mlsanity.com/paper/2603.13133/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2026.

## Built on by (most cited first, 5 of 5)

- [LMMs Meet Object-Centric Vision: Understanding, Segmentation, Editing and Generation](https://mlsanity.com/paper/2604.11789/) (2026)
- [What Limits Vision-and-Language Navigation ?](https://mlsanity.com/paper/2605.13328/) (2026)
- [A Comprehensive Survey and Systematic Real-World Evaluation of Embodied Vision-and-Language Navigation](https://mlsanity.com/paper/2607.09792/) (2026)
- [GeoScaffold: Learning Compact Geometric Latents via Reconstruction for Efficient Vision-Language Navigation](https://mlsanity.com/paper/2610.02697/) (2026)
- [Robust Vision-Language Navigation with Awareness of Instruction-Execution Progress](https://mlsanity.com/paper/2610.13104/) (2026)

## Abstract (arXiv)

Vision-and-Language Navigation (VLN) requires agents to follow long-horizon instructions and navigate complex 3D environments. However, existing approaches face two major challenges: constructing an effective long-term memory bank and overcoming the compounding errors problem. To address these issues, we propose DecoVLN, an effective framework designed for robust streaming perception and closed-loop control in long-horizon navigation. First, we formulate long-term memory construction as an optimization problem and introduce adaptive refinement mechanism that selects frames from a historical candidate pool by iteratively optimizing a unified scoring function. This function jointly balances three key criteria: semantic relevance to the instruction, visual diversity from the selected memory, and temporal coverage of the historical trajectory. Second, to alleviate compounding errors, we introduce a state-action pair-level corrective finetuning strategy. By leveraging geodesic distance between states to precisely quantify deviation from the expert trajectory, the agent collects high-quality state-action pairs in the trusted region while filtering out the polluted data with low relevance. This improves both the efficiency and stability of error correction. Extensive experiments demonstrate the effectiveness of DecoVLN, and we have deployed it in real-world environments.