# Vision-and-Language Navigation with Analogical Textual Descriptions in LLMs

arXiv: https://arxiv.org/abs/2509.25139 · HTML: https://mlsanity.com/paper/2509.25139/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 35 references that are in the map.
- First posted on arXiv 29 Sep 2025.

## Builds on (most cited first, 24 of 24)

- [Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments](https://mlsanity.com/paper/1711.07280/) (2017)
- [InternVideo: General Video Foundation Models via Generative and Discriminative Learning](https://mlsanity.com/paper/2212.03191/) (2022)
- [Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Grounding](https://mlsanity.com/paper/2010.07954/) (2020)
- [REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environments](https://mlsanity.com/paper/1904.10151/) (2019)
- [History Aware Multimodal Transformer for Vision-and-Language Navigation](https://mlsanity.com/paper/2110.13309/) (2021)
- [Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout](https://mlsanity.com/paper/1904.04195/) (2019)
- [Towards Learning a Generalist Model for Embodied Navigation](https://mlsanity.com/paper/2312.02010/) (2023)
- [Airbert: In-domain Pretraining for Vision-and-Language Navigation](https://mlsanity.com/paper/2108.09105/) (2021)
- [Scaling Data Generation in Vision-and-Language Navigation](https://mlsanity.com/paper/2307.15644/) (2023)
- [Discuss Before Moving: Visual Language Navigation via Multi-expert Discussions](https://mlsanity.com/paper/2309.11382/) (2023)
- [MapGPT: Map-Guided Prompting with Adaptive Path Planning for Vision-and-Language Navigation](https://mlsanity.com/paper/2401.07314/) (2024)
- [Vision-and-Language Navigation Today and Tomorrow: A Survey in the Era of Foundation Models](https://mlsanity.com/paper/2407.07035/) (2024)
- [LangNav: Language as a Perceptual Representation for Navigation](https://mlsanity.com/paper/2310.07889/) (2023)
- [Rethinking Vision-Language Model in Face Forensics: Multi-Modal Interpretable Forged Face Detector](https://mlsanity.com/paper/2503.20188/) (2025)
- [Thought Propagation: An Analogical Approach to Complex Reasoning with Large Language Models](https://mlsanity.com/paper/2310.03965/) (2023)
- [March in Chat: Interactive Prompting for Remote Embodied Referring Expression](https://mlsanity.com/paper/2308.10141/) (2023)
- [SPARTUN3D: Situated Spatial Understanding of 3D World in Large Language Models](https://mlsanity.com/paper/2410.03878/) (2024)
- [VLN-Trans: Translator for the Vision and Language Navigation Agent](https://mlsanity.com/paper/2302.09230/) (2023)
- [Bootstrapping Language-Guided Navigation Learning with Self-Refining Data Flywheel](https://mlsanity.com/paper/2412.08467/) (2024)
- [CREMA: Generalizable and Efficient Video-Language Reasoning via Multimodal Modular Fusion](https://mlsanity.com/paper/2402.05889/) (2024)
- [Towards Navigation by Reasoning over Spatial Configurations](https://mlsanity.com/paper/2105.06839/) (2021)
- [NavHint: Vision and Language Navigation Agent with a Hint Generator](https://mlsanity.com/paper/2402.02559/) (2024)
- [Breaking Down and Building Up: Mixture of Skill-Based Vision-and-Language Navigation Agents](https://mlsanity.com/paper/2508.07642/) (2025)
- [Which One? Leveraging Context Between Objects and Multiple Views for Language Grounding](https://mlsanity.com/paper/2311.06694/) (2023)

## Built on by (most cited first, 3 of 3)

- [IntentionNav: A Benchmark for Intent-Driven Object Navigation from Implicit Human Instruction](https://mlsanity.com/paper/2605.23187/) (2026)
- [A Comprehensive Survey and Systematic Real-World Evaluation of Embodied Vision-and-Language Navigation](https://mlsanity.com/paper/2607.09792/) (2026)
- [Personalizing Embodied Multimodal Large Language Model Agents over Long-term User Interactions](https://mlsanity.com/paper/2605.26256/) (2026)

## Abstract (arXiv)

Integrating large language models (LLMs) into embodied AI models is becoming increasingly prevalent. However, existing zero-shot LLM-based Vision-and-Language Navigation (VLN) agents either encode images as textual scene descriptions, potentially oversimplifying visual details, or process raw image inputs, which can fail to capture abstract semantics required for high-level reasoning. In this paper, we improve the navigation agent's contextual understanding by incorporating textual descriptions from multiple perspectives that facilitate analogical reasoning across images. By leveraging text-based analogical reasoning, the agent enhances its global scene understanding and spatial reasoning, leading to more accurate action decisions. We evaluate our approach on the R2R dataset, where our experiments demonstrate significant improvements in navigation performance.