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• Causal Scene Narration (CSN) improves Driving Score by +31.1% on original LMDrive and +24.5% on the preference-aligned variant in multi-town closed-loop CARLA evaluation.result 95%
• Causal structure accounts for 39.1% of the performance gain from CSN, with the remainder attributable to information content alone.result 90%
• CSN restructures VLA text inputs through intent-constraint alignment, quantitative grounding, and structured separation at inference time with zero GPU cost.method 90%
• Semantic safety supervision improves Infraction Score in VLA driving systems.result 85%
• Reactive Time-To-Collision monitoring degrades performance in VLA driving systems.result 85%
• CSN's benefit is robust to realistic sensing errors as confirmed by perception noise ablation.technical 80%
• Current VLA systems for autonomous driving often present textual inputs as disconnected fragments, forcing models to discover relevant environmental constraints.limitation 90%
• Intent-aware monitoring is needed for VLA systems rather than reactive monitoring approaches.technical 80%

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