Is VLA Reasoning Faithful? Probing Safety of Chain-of-Causation in Autonomous Driving Models
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 case...
| Authors | Nicanor Mayumu, Xiaoheng Deng, Patrick Mukala |
|---|---|
| Primary category | cs.AI |
| Published | 17 May 2026 |
| Last updated at source | 24 May 2026 |
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