Is VLA Reasoning Faithful? Probing Safety of Chain-of-Causation
A recent study investigates the faithfulness of Vision-Language-Action (VLA) driving models. The research reveals significant issues with reasoning fidelity, with only 42.5% accuracy in matching scene reality. The findings highlight concerns about trajectory fragility and reasoning-action consistency in various scenarios.
- ▪The study analyzed 300 Alpamayo-R1-10B inferences across 100 diverse PhysicalAI-AV scenarios.
- ▪Overall reasoning fidelity in the models was found to be only 42.5%.
- ▪The models exhibited 97.7% trajectory fragility under mild visual perturbations.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.17268 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
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Computer Science > Artificial Intelligence arXiv:2605.17268 (cs) [Submitted on 17 May 2026] Title:Is VLA Reasoning Faithful? Probing Safety of Chain-of-Causation Authors:Nicanor Mayumu, Xiaoheng Deng, Patrick Mukala View a PDF of the paper titled Is VLA Reasoning Faithful? Probing Safety of Chain-of-Causation, by Nicanor Mayumu and 2 other authors View PDF HTML (experimental) 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.
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