# Run, Ruminate, and Regulate: A Dual-process Thinking System for Vision-and-Language Navigation

arXiv: https://arxiv.org/abs/2511.14131 · HTML: https://mlsanity.com/paper/2511.14131/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 18 of its 80 references that are in the map.
- First posted on arXiv 18 Nov 2025.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)

## Builds on (most cited first, 18 of 18)

- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://mlsanity.com/paper/1908.07490/) (2019)
- [Behavioral Cloning from Observation](https://mlsanity.com/paper/1805.01954/) (2018)
- [NaVid: Video-based VLM Plans the Next Step for Vision-and-Language Navigation](https://mlsanity.com/paper/2402.15852/) (2024)
- [BEVBert: Multimodal Map Pre-training for Language-guided Navigation](https://mlsanity.com/paper/2212.04385/) (2022)
- [Real-Time Anomaly Detection and Reactive Planning with Large Language Models](https://mlsanity.com/paper/2407.08735/) (2024)
- [A2Nav: Action-Aware Zero-Shot Robot Navigation by Exploiting Vision-and-Language Ability of Foundation Models](https://mlsanity.com/paper/2308.07997/) (2023)
- [Agents Thinking Fast and Slow: A Talker-Reasoner Architecture](https://mlsanity.com/paper/2410.08328/) (2024)
- [Offline Reinforcement Learning for Visual Navigation](https://mlsanity.com/paper/2212.08244/) (2022)
- [Fast-Slow Test-Time Adaptation for Online Vision-and-Language Navigation](https://mlsanity.com/paper/2311.13209/) (2023)
- [MC-GPT: Empowering Vision-and-Language Navigation with Memory Map and Reasoning Chains](https://mlsanity.com/paper/2405.10620/) (2024)
- [RoomTour3D: Geometry-Aware Video-Instruction Tuning for Embodied Navigation](https://mlsanity.com/paper/2412.08591/) (2024)
- [A Dual Semantic-Aware Recurrent Global-Adaptive Network For Vision-and-Language Navigation](https://mlsanity.com/paper/2305.03602/) (2023)
- [FASIONAD : FAst and Slow FusION Thinking Systems for Human-Like Autonomous Driving with Adaptive Feedback](https://mlsanity.com/paper/2411.18013/) (2024)
- [Chameleon: Fast-slow Neuro-symbolic Lane Topology Extraction](https://mlsanity.com/paper/2503.07485/) (2025)
- [Scaling Vision-and-Language Navigation With Offline RL](https://mlsanity.com/paper/2403.18454/) (2024)
- [Anticipating the Unseen Discrepancy for Vision and Language Navigation](https://mlsanity.com/paper/2209.04725/) (2022)
- [Agent Journey Beyond RGB: Hierarchical Semantic-Spatial Representation Enrichment for Vision-and-Language Navigation](https://mlsanity.com/paper/2412.06465/) (2024)

## Built on by (most cited first, 2 of 2)

- [See-and-Reach: Precise Vision-Language Navigation for UAVs within the Field of View](https://mlsanity.com/paper/2606.20045/) (2026)
- [What Can Component-Replacement Evidence Establish? A Critical Scoping Review of Local Decisions in LLM Agents](https://mlsanity.com/paper/2609.39989/) (2026)

## Abstract (arXiv)

Vision-and-Language Navigation (VLN) requires an agent to dynamically explore complex 3D environments following human instructions. Recent research underscores the potential of harnessing large language models (LLMs) for VLN, given their commonsense knowledge and general reasoning capabilities. Despite their strengths, a substantial gap in task completion performance persists between LLM-based approaches and domain experts, as LLMs inherently struggle to comprehend real-world spatial correlations precisely. Additionally, introducing LLMs is accompanied with substantial computational cost and inference latency. To address these issues, we propose a novel dual-process thinking framework dubbed R3, integrating LLMs' generalization capabilities with VLN-specific expertise in a zero-shot manner. The framework comprises three core modules: Runner, Ruminator, and Regulator. The Runner is a lightweight transformer-based expert model that ensures efficient and accurate navigation under regular circumstances. The Ruminator employs a powerful multimodal LLM as the backbone and adopts chain-of-thought (CoT) prompting to elicit structured reasoning. The Regulator monitors the navigation progress and controls the appropriate thinking mode according to three criteria, integrating Runner and Ruminator harmoniously. Experimental results illustrate that R3 significantly outperforms other state-of-the-art methods, exceeding 3.28% and 3.30% in SPL and RGSPL respectively on the REVERIE benchmark. This pronounced enhancement highlights the effectiveness of our method in handling challenging VLN tasks.