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In this paper we present a controlled perturbation study of Vision-Language-Action (VLA) robustness in autonomous driving, evaluating Alpamayo R1 (10B parameters) across 1,996 scenarios under eight sensor perturbations (Gaussian noise at four intensities, two lighting extremes, and two fog levels; ${\\sim}18{,}000$ inference trials). We find that reasoning consistency is a high-fidelity indicator of trajectory reliability: when Chain-of-Causation (CoC) explanations change after perturbation, trajectory deviation spikes $5.3{\\times}$ (21.8m vs 4.1m), with $r\\!=\\!0.99$ across attack types and $r_{pb}\\!=\\!0.53$ per-sample (Cohen's $d\\!=\\!1.12$). A controlled ablation provides evidence that enabling CoC generation is associated with improved trajectory accuracy (","title":"Lost in Fog: Sensor Perturbations Expose Reasoning Fragility in Driving VLAs","url":"https://arxiv.org/abs/2605.21446","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.21446v2 Announce Type: replace-cross \nAbstract: Interpretable autonomous driving planners depend not only on generating explanations, but also on those explanations remaining reliable under real-world sensor degradation. In this paper we present a controlled perturbation study of Vision-Language-Action (VLA) robustness in autonomous driving, evaluating Alpamayo R1 (10B parameters) across 1,996 scenarios under eight sensor perturbations (Gaussian noise at four intensities, two lighting extremes, and two fog levels; ${\\sim}18{,}000$ inference trials). We find that reasoning consistency is a high-fidelity indicator of trajectory reliability: when Chain-of-Causation (CoC) explanations change after perturbation, trajectory deviation spikes $5.3{\\times}$ (21.8m vs 4.1m), with $r\\!=\\!0.99$ across attack types and $r_{pb}\\!=\\!0.53$ per-sample (Cohen's $d\\!=\\!1.12$). 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