@inproceedings{you-etal-2026-latent-dreaming,
title = "Latent Dreaming: Let Language Think Yet Latent See in Driving {VLA}",
author = "You, Zhixuan and
Zhang, Ruixu and
Wang, Junjie and
Meng, Yuxin and
Wu, Lisheng and
Zhou, Lei and
Kou, Wei-Bin and
Yang, Yujiu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.401/",
pages = "8483--8500",
ISBN = "979-8-89176-462-0",
abstract = "Vision-language-action (VLA) models for autonomous driving typically express intermediate reasoning entirely in text, which may discard decision-relevant visual information. We propose Latent Dreaming (LatDM), a latent{--}text reasoning framework that complements textual chain-of-thought with continuous latent states. LatDM uses latent blocks to preserve visually grounded spatial and temporal information, while text handles semantic abstraction and decision communication. A pretrained visual encoder provides latent supervision during training, and the model autonomously interleaves latent and textual reasoning at inference. Experiments on VLADBench, CODA-LM, and NuInstruct show consistent improvements over the underlying VLA backbone, while ablations confirm that the latent channel contributes beyond standard textual fine-tuning."
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<abstract>Vision-language-action (VLA) models for autonomous driving typically express intermediate reasoning entirely in text, which may discard decision-relevant visual information. We propose Latent Dreaming (LatDM), a latent–text reasoning framework that complements textual chain-of-thought with continuous latent states. LatDM uses latent blocks to preserve visually grounded spatial and temporal information, while text handles semantic abstraction and decision communication. A pretrained visual encoder provides latent supervision during training, and the model autonomously interleaves latent and textual reasoning at inference. Experiments on VLADBench, CODA-LM, and NuInstruct show consistent improvements over the underlying VLA backbone, while ablations confirm that the latent channel contributes beyond standard textual fine-tuning.</abstract>
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%0 Conference Proceedings
%T Latent Dreaming: Let Language Think Yet Latent See in Driving VLA
%A You, Zhixuan
%A Zhang, Ruixu
%A Wang, Junjie
%A Meng, Yuxin
%A Wu, Lisheng
%A Zhou, Lei
%A Kou, Wei-Bin
%A Yang, Yujiu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F you-etal-2026-latent-dreaming
%X Vision-language-action (VLA) models for autonomous driving typically express intermediate reasoning entirely in text, which may discard decision-relevant visual information. We propose Latent Dreaming (LatDM), a latent–text reasoning framework that complements textual chain-of-thought with continuous latent states. LatDM uses latent blocks to preserve visually grounded spatial and temporal information, while text handles semantic abstraction and decision communication. A pretrained visual encoder provides latent supervision during training, and the model autonomously interleaves latent and textual reasoning at inference. Experiments on VLADBench, CODA-LM, and NuInstruct show consistent improvements over the underlying VLA backbone, while ablations confirm that the latent channel contributes beyond standard textual fine-tuning.
%U https://aclanthology.org/2026.findings-emnlp.401/
%P 8483-8500
Markdown (Informal)
[Latent Dreaming: Let Language Think Yet Latent See in Driving VLA](https://aclanthology.org/2026.findings-emnlp.401/) (You et al., Findings 2026)
ACL
- Zhixuan You, Ruixu Zhang, Junjie Wang, Yuxin Meng, Lisheng Wu, Lei Zhou, Wei-Bin Kou, and Yujiu Yang. 2026. Latent Dreaming: Let Language Think Yet Latent See in Driving VLA. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 8483–8500, Budapest, Hungary. Association for Computational Linguistics.