VLADriveBench: Evaluating CoT-Action Relationship in VLA for Autonomous Driving
VLADriveBench is a new framework to evaluate whether chain-of-thought (CoT) reasoning in vision-language-action (VLA) models is relevant, consistent, and causally connected to driving trajectories. It combines observational metrics (mentioning, hallucination, contradiction, action alignment) with a CoT intervention protocol. Applied to three models across two architectures, it reveals that observational and causal analyses can diverge sharply: ORION scores high on observational alignment but its CoT is epiphenomenal, while Alpamayo v1.5 scores lower yet its CoT is strongly causal, with visual salience gating the extent of CoT influence.
[2606.12706] VLADriveBench: Evaluating CoT-Action Relationship in VLA for Autonomous Driving
[Submitted on 10 Jun 2026]
Title:VLADriveBench: Evaluating CoT-Action Relationship in VLA for Autonomous Driving
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Abstract:Vision-language-action (VLA) models generate chain-of-thought (CoT) reasoning alongside driving trajectories, but existing benchmarks evaluate only trajectory quality and do not assess whether the CoT is relevant, consistent, or causally connected to the driving action. We introduce VLADriveBench, a framework that combines observational metrics (mentioning, hallucination, contradiction, action alignment) with a CoT intervention protocol to provide complementary views of the CoT-action relationship. Applying VLADriveBench to three models across two architectures, we find that the two analyses can diverge sharply: ORION scores highest on observational alignment yet its CoT is epiphenomenal, while Alpamayo v1.5 scores lower yet its CoT is strongly causal, with visual salience gating the extent of CoT influence.
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Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.12706 [cs.CV]
(or arXiv:2606.12706v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.12706
arXiv-issued DOI via DataCite
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From: Thach Nguyen [view email] [v1] Wed, 10 Jun 2026 21:53:33 UTC (58 KB)
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