@inproceedings{yu-etal-2026-lookstep,
title = "{L}ook{S}tep: Efficient Vision-Language Navigation with Linguistic Foresight and Event Driven Memory",
author = "Yu, Kun-Yang and
Li, Yingzhe and
Xu, Hongyu and
Tian, Shi-Yu and
Zhou, Zhi and
Chen, Yang and
Yang, Ming and
Wang, Sheng and
Yu, Qing and
Guo, Lan-Zhe and
Li, Yu-Feng",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.239/",
pages = "5465--5483",
ISBN = "979-8-89176-459-0",
abstract = "Vision-Language Navigation (VLN) requires an embodied agent to follow natural-language instructions in unseen environments. Recent progress has been largely driven by Multimodal Large Language Models (MLLMs). Existing methods follow a next-step action prediction paradigm, supervising only the expert action, which requires a high quantity of data for training. They also rely on cognitive maps, accumulated historical frames, or external 3D tools to maintain states, leading to high computational and memory overhead. To realize resource efficiency VLN, we propose \textbf{LookStep}, a unified end-to-end framework that combines \textbf{Language Centric Future State Modeling} and \textbf{Event Driven Rolling Memory} that uses language labels to generate coarse-grained navigation progress and future states for each candidate action, while autonomously deciding whether to write each observation into a bounded rolling memory with a semantic role. We validate LookStep empirically. On VLN-CE tasks, LookStep outperforms existing methods under the same training settings, achieving a 49.7{\%} success rate on R2R-CE Val-Unseen with better memory efficiency and less data usage. Code and model is available at \url{https://github.com/kunyang-YU/LookStep}."
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<abstract>Vision-Language Navigation (VLN) requires an embodied agent to follow natural-language instructions in unseen environments. Recent progress has been largely driven by Multimodal Large Language Models (MLLMs). Existing methods follow a next-step action prediction paradigm, supervising only the expert action, which requires a high quantity of data for training. They also rely on cognitive maps, accumulated historical frames, or external 3D tools to maintain states, leading to high computational and memory overhead. To realize resource efficiency VLN, we propose LookStep, a unified end-to-end framework that combines Language Centric Future State Modeling and Event Driven Rolling Memory that uses language labels to generate coarse-grained navigation progress and future states for each candidate action, while autonomously deciding whether to write each observation into a bounded rolling memory with a semantic role. We validate LookStep empirically. On VLN-CE tasks, LookStep outperforms existing methods under the same training settings, achieving a 49.7% success rate on R2R-CE Val-Unseen with better memory efficiency and less data usage. Code and model is available at https://github.com/kunyang-YU/LookStep.</abstract>
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%0 Conference Proceedings
%T LookStep: Efficient Vision-Language Navigation with Linguistic Foresight and Event Driven Memory
%A Yu, Kun-Yang
%A Li, Yingzhe
%A Xu, Hongyu
%A Tian, Shi-Yu
%A Zhou, Zhi
%A Chen, Yang
%A Yang, Ming
%A Wang, Sheng
%A Yu, Qing
%A Guo, Lan-Zhe
%A Li, Yu-Feng
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F yu-etal-2026-lookstep
%X Vision-Language Navigation (VLN) requires an embodied agent to follow natural-language instructions in unseen environments. Recent progress has been largely driven by Multimodal Large Language Models (MLLMs). Existing methods follow a next-step action prediction paradigm, supervising only the expert action, which requires a high quantity of data for training. They also rely on cognitive maps, accumulated historical frames, or external 3D tools to maintain states, leading to high computational and memory overhead. To realize resource efficiency VLN, we propose LookStep, a unified end-to-end framework that combines Language Centric Future State Modeling and Event Driven Rolling Memory that uses language labels to generate coarse-grained navigation progress and future states for each candidate action, while autonomously deciding whether to write each observation into a bounded rolling memory with a semantic role. We validate LookStep empirically. On VLN-CE tasks, LookStep outperforms existing methods under the same training settings, achieving a 49.7% success rate on R2R-CE Val-Unseen with better memory efficiency and less data usage. Code and model is available at https://github.com/kunyang-YU/LookStep.
%U https://aclanthology.org/2026.emnlp-main.239/
%P 5465-5483
Markdown (Informal)
[LookStep: Efficient Vision-Language Navigation with Linguistic Foresight and Event Driven Memory](https://aclanthology.org/2026.emnlp-main.239/) (Yu et al., EMNLP 2026)
ACL
- Kun-Yang Yu, Yingzhe Li, Hongyu Xu, Shi-Yu Tian, Zhi Zhou, Yang Chen, Ming Yang, Sheng Wang, Qing Yu, Lan-Zhe Guo, and Yu-Feng Li. 2026. LookStep: Efficient Vision-Language Navigation with Linguistic Foresight and Event Driven Memory. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 5465–5483, Budapest, Hungary. Association for Computational Linguistics.