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arXiv · 2605.17268

Is VLA Reasoning Faithful? Probing Safety of Chain-of-Causation in Autonomous Driving Models

Nicanor Mayumu·Xiaoheng Deng·Patrick Mukala
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Abstract

We present the first systematic study of faithfulness in Vision-Language-Action (VLA) driving models, analyzing 300 Alpamayo-R1-10B inferences across 100 diverse PhysicalAI-AV scenarios. Our main finding is that output natural-language rationales with trajectories may be significantly unfaithful: (i) overall reasoning fidelity is only 42.5%, with Chain-of-Causation matching scene reality less than half the time; (ii) 94 missed pedestrians in one-third of pedestrian-relevant scenes; (iii) 97.7% trajectory fragility under mild visual perturbations; and (iv) only 48.3% mean reasoning-action consistency, with 53.3% of inferences exhibiting low consistency, including 37.9% of stop-claimed cases where the model continues instead. We formalize faithfulness information-theoretically, define entity and action fidelity with verification criteria, and outline a four-component safety architecture aligned with these results.

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Nicanor Mayumu, Xiaoheng Deng, Patrick Mukala. 2026-05-17. Is VLA Reasoning Faithful? Probing Safety of Chain-of-Causation in Autonomous Driving Models. https://arxiv.org/abs/2605.17268

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