@inproceedings{yu-etal-2026-twinbrainvla,
title = "{T}win{B}rain{VLA}: Unleashing the Potential of Generalist {VLM}s for Embodied Tasks via Asymmetric Mixture-of-Transformers",
author = "Yu, Bin and
Lian, Shijie and
Lin, Xiaopeng and
Shen, Zhaolong and
Wu, Changti and
Miao, Yuzhuo and
Wang, Xinming and
Wang, Bailing and
Huang, Cong and
Chen, Kai",
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.217/",
pages = "4533--4552",
ISBN = "979-8-89176-462-0",
abstract = "Vision-Language-Action (VLA) models are built on the premise that pre-trained Vision-Language Models (VLMs) can supply general semantic understanding ability for embodied tasks. However, we find that robotic fine-tuning can severely erode this capability: VQA benchmarks and layer-wise CKA analysis reveal rapid representational drift, and co-training with general vision-language data does not prevent the collapse. We propose TwinBrainVLA, an asymmetric dual-stream VLA that keeps a pre-trained ``Left Brain'' strictly frozen as a semantic generalist while training a ``Right Brain'' as an embodied control specialist. Its Asymmetric Mixture-of-Transformers (AsyMoT) mechanism lets the Right Brain query frozen Left-Brain key-value representations at every transformer layer, integrating intact semantic knowledge without corrupting it. Across SimplerEnv, RoboCasa, LIBERO, and real-robot experiments, TwinBrainVLA outperforms strong baselines and large-scale embodied models, achieving a 14.3{\%} improvement on SimplerEnv and a 2.7$\times$ gain in physical-robot instruction generalization."
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<abstract>Vision-Language-Action (VLA) models are built on the premise that pre-trained Vision-Language Models (VLMs) can supply general semantic understanding ability for embodied tasks. However, we find that robotic fine-tuning can severely erode this capability: VQA benchmarks and layer-wise CKA analysis reveal rapid representational drift, and co-training with general vision-language data does not prevent the collapse. We propose TwinBrainVLA, an asymmetric dual-stream VLA that keeps a pre-trained “Left Brain” strictly frozen as a semantic generalist while training a “Right Brain” as an embodied control specialist. Its Asymmetric Mixture-of-Transformers (AsyMoT) mechanism lets the Right Brain query frozen Left-Brain key-value representations at every transformer layer, integrating intact semantic knowledge without corrupting it. Across SimplerEnv, RoboCasa, LIBERO, and real-robot experiments, TwinBrainVLA outperforms strong baselines and large-scale embodied models, achieving a 14.3% improvement on SimplerEnv and a 2.7\times gain in physical-robot instruction generalization.</abstract>
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%0 Conference Proceedings
%T TwinBrainVLA: Unleashing the Potential of Generalist VLMs for Embodied Tasks via Asymmetric Mixture-of-Transformers
%A Yu, Bin
%A Lian, Shijie
%A Lin, Xiaopeng
%A Shen, Zhaolong
%A Wu, Changti
%A Miao, Yuzhuo
%A Wang, Xinming
%A Wang, Bailing
%A Huang, Cong
%A Chen, Kai
%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 yu-etal-2026-twinbrainvla
%X Vision-Language-Action (VLA) models are built on the premise that pre-trained Vision-Language Models (VLMs) can supply general semantic understanding ability for embodied tasks. However, we find that robotic fine-tuning can severely erode this capability: VQA benchmarks and layer-wise CKA analysis reveal rapid representational drift, and co-training with general vision-language data does not prevent the collapse. We propose TwinBrainVLA, an asymmetric dual-stream VLA that keeps a pre-trained “Left Brain” strictly frozen as a semantic generalist while training a “Right Brain” as an embodied control specialist. Its Asymmetric Mixture-of-Transformers (AsyMoT) mechanism lets the Right Brain query frozen Left-Brain key-value representations at every transformer layer, integrating intact semantic knowledge without corrupting it. Across SimplerEnv, RoboCasa, LIBERO, and real-robot experiments, TwinBrainVLA outperforms strong baselines and large-scale embodied models, achieving a 14.3% improvement on SimplerEnv and a 2.7\times gain in physical-robot instruction generalization.
%U https://aclanthology.org/2026.findings-emnlp.217/
%P 4533-4552
Markdown (Informal)
[TwinBrainVLA: Unleashing the Potential of Generalist VLMs for Embodied Tasks via Asymmetric Mixture-of-Transformers](https://aclanthology.org/2026.findings-emnlp.217/) (Yu et al., Findings 2026)
ACL
- Bin Yu, Shijie Lian, Xiaopeng Lin, Zhaolong Shen, Changti Wu, Yuzhuo Miao, Xinming Wang, Bailing Wang, Cong Huang, and Kai Chen. 2026. TwinBrainVLA: Unleashing the Potential of Generalist VLMs for Embodied Tasks via Asymmetric Mixture-of-Transformers. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 4533–4552, Budapest, Hungary. Association for Computational Linguistics.