VLADriveBench: Evaluating CoT-Action Relationship in VLA for Autonomous Driving

Researchers have developed VLADriveBench, a new evaluation framework that assesses whether the chain-of-thought reasoning produced by vision-language-action (VLA) models for autonomous driving is relevant and causally connected to the driving action, rather than just measuring trajectory quality. Testing on three models across two architectures revealed that observational metrics and causal analysis can produce sharply divergent results, with one model scoring high on alignment yet having epiphenomenal reasoning, while another scored lower yet produced strongly causal chain-of-thought. The findings highlight the need for evaluating the causal relationship between reasoning and action in autonomous driving systems, as visual salience was found to gate the extent of chain-of-thought influence.