Towards Open Environments and Instructions: General Vision-Language Navigation via Fast-Slow Interactive Reasoning

2026-01-14

AI Summary

This work addresses the limited generalization of traditional vision-and-language navigation (VLN) models trained in closed settings to unseen environments and novel instructions in open-world scenarios. To overcome this, the authors propose slow4fast-VLN, a novel framework that introduces dynamic interaction between dual cognitive systems inspired by human cognition: a fast module that makes real-time navigation decisions while logging experience, and a slow module that performs deep reflective reasoning over accumulated memory to extract structured, generalizable knowledge. This knowledge continuously refines the fast module’s policy. Unlike prior approaches where fast and slow components operate independently, their tight integration enables significant improvements in robustness and generalization under the Generalized Scene Adaptation VLN (GSA-VLN) setting, particularly when facing unfamiliar environments and semantically inconsistent instructions.

Technology Category

• Computer Vision: Language and Vision
• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Vision-Language Navigation aims to enable agents to navigate to a target location based on language instructions. Traditional VLN often follows a close-set assumption, i.e., training and test data share the same style of the input images and instructions. However, the real world is open and filled with various unseen environments, posing enormous difficulties for close-set methods. To this end, we focus on the General Scene Adaptation (GSA-VLN) task, aiming to learn generalized navigation ability by introducing diverse environments and inconsistent intructions.Towards this task, when facing unseen environments and instructions, the challenge mainly lies in how to enable the agent to dynamically produce generalized strategies during the navigation process. Recent research indicates that by means of fast and slow cognition systems, human beings could generate stable policies, which strengthen their adaptation for open world. Inspired by this idea, we propose the slow4fast-VLN, establishing a dynamic interactive fast-slow reasoning framework. The fast-reasoning module, an end-to-end strategy network, outputs actions via real-time input. It accumulates execution records in a history repository to build memory. The slow-reasoning module analyze the memories generated by the fast-reasoning module. Through deep reflection, it extracts experiences that enhance the generalization ability of decision-making. These experiences are structurally stored and used to continuously optimize the fast-reasoning module. Unlike traditional methods that treat fast-slow reasoning as independent mechanisms, our framework enables fast-slow interaction. By leveraging the experiences from slow reasoning. This interaction allows the system to continuously adapt and efficiently execute navigation tasks when facing unseen scenarios.

Problem

• Vision-Language Navigation
• Open Environments
• Generalization
• Unseen Instructions
• General Scene Adaptation

Innovation

• Vision-Language Navigation
• Fast-Slow Reasoning
• General Scene Adaptation
• Interactive Reasoning
• Open-World Generalization