@inproceedings{shi-etal-2026-vla,
title = "{VLA}-Trace: Diagnosing Vision-Language-Action Models through Representation and Behavior Tracing",
author = "Shi, Haoyuan and
Ren, Xiancong and
Zhang, Yingji and
Zhang, Qinfan and
Hu, Jiayu and
Shan, Haozhe and
Dong, Han and
Lu, Jinpeng and
Chen, Yinda and
Zhang, Yi and
Dai, Yong and
Ju, Xiaozhu",
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.2076/",
pages = "47280--47315",
ISBN = "979-8-89176-459-0",
abstract = "Understanding how Vision-Language-Action (VLA) models transform multimodal knowledge into embodied control remains an open challenge. We present VLA-Trace, a progressive diagnostic framework that analyzes VLA models through a unified evidence chain from representation dynamics to causal control attribution and behavioral manifestation. It specifically combines cross-modal and checkpoint-drift centered kernel alignment (CKA) to trace representation evolution, attention knockout interventions to identify modality-specific control pathways, and rollout-level behavioral probes to examine grounding, shortcut dependence, and semantic following. Experiments on $\pi_{0.5}$ and OpenVLA reveal three key findings. First, the two models exhibit distinct modality-specific adaptation dynamics during VLA finetuning. Second, they rely on different multimodal routing strategies and layer-wise dependencies during action decoding. Third, although VLA policies excel at visually grounded trajectory generation, they remain limited in fine-grained semantic following. These findings highlight future directions for representation-preserving adaptation, causal VLA circuits, and compositional semantic control."
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<abstract>Understanding how Vision-Language-Action (VLA) models transform multimodal knowledge into embodied control remains an open challenge. We present VLA-Trace, a progressive diagnostic framework that analyzes VLA models through a unified evidence chain from representation dynamics to causal control attribution and behavioral manifestation. It specifically combines cross-modal and checkpoint-drift centered kernel alignment (CKA) to trace representation evolution, attention knockout interventions to identify modality-specific control pathways, and rollout-level behavioral probes to examine grounding, shortcut dependence, and semantic following. Experiments on π₀.5 and OpenVLA reveal three key findings. First, the two models exhibit distinct modality-specific adaptation dynamics during VLA finetuning. Second, they rely on different multimodal routing strategies and layer-wise dependencies during action decoding. Third, although VLA policies excel at visually grounded trajectory generation, they remain limited in fine-grained semantic following. These findings highlight future directions for representation-preserving adaptation, causal VLA circuits, and compositional semantic control.</abstract>
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%0 Conference Proceedings
%T VLA-Trace: Diagnosing Vision-Language-Action Models through Representation and Behavior Tracing
%A Shi, Haoyuan
%A Ren, Xiancong
%A Zhang, Yingji
%A Zhang, Qinfan
%A Hu, Jiayu
%A Shan, Haozhe
%A Dong, Han
%A Lu, Jinpeng
%A Chen, Yinda
%A Zhang, Yi
%A Dai, Yong
%A Ju, Xiaozhu
%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 shi-etal-2026-vla
%X Understanding how Vision-Language-Action (VLA) models transform multimodal knowledge into embodied control remains an open challenge. We present VLA-Trace, a progressive diagnostic framework that analyzes VLA models through a unified evidence chain from representation dynamics to causal control attribution and behavioral manifestation. It specifically combines cross-modal and checkpoint-drift centered kernel alignment (CKA) to trace representation evolution, attention knockout interventions to identify modality-specific control pathways, and rollout-level behavioral probes to examine grounding, shortcut dependence, and semantic following. Experiments on π₀.5 and OpenVLA reveal three key findings. First, the two models exhibit distinct modality-specific adaptation dynamics during VLA finetuning. Second, they rely on different multimodal routing strategies and layer-wise dependencies during action decoding. Third, although VLA policies excel at visually grounded trajectory generation, they remain limited in fine-grained semantic following. These findings highlight future directions for representation-preserving adaptation, causal VLA circuits, and compositional semantic control.
%U https://aclanthology.org/2026.emnlp-main.2076/
%P 47280-47315
Markdown (Informal)
[VLA-Trace: Diagnosing Vision-Language-Action Models through Representation and Behavior Tracing](https://aclanthology.org/2026.emnlp-main.2076/) (Shi et al., EMNLP 2026)
ACL
- Haoyuan Shi, Xiancong Ren, Yingji Zhang, Qinfan Zhang, Jiayu Hu, Haozhe Shan, Han Dong, Jinpeng Lu, Yinda Chen, Yi Zhang, Yong Dai, and Xiaozhu Ju. 2026. VLA-Trace: Diagnosing Vision-Language-Action Models through Representation and Behavior Tracing. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 47280–47315, Budapest, Hungary. Association for Computational Linguistics.