Causal Scene Narration: Inference-Time Text Restructuring for Vision-Language-Action Driving

A controlled ablation separating information content from text organization reveals that information content carries most of CSN's gain across both model weights (+6.17 DS on original LMDrive; +6.88 DS on PL-DPO-NLL). It comes from a paper posted to arXiv on 20 October 2026. Vision-Language-Action (VLA) models for autonomous driving receive navigation commands and hazard notices as disconnected text fragments, forcing the model to discover on its own which environmental constraints are relevant to the current maneuver. We introduce Causal Scene Narration (CSN), which restructures VLA text inputs through intent-constraint alignment, quantitative grounding, and structured separation at inference time with zero GPU cost. To test CSN across different model states and safety layers, we evaluate it alongside training-time preference alignment (PL-DPO-NLL) and runtime supervision as experimental conditions. A multi-town CARLA evaluation (16 routes, 8 towns, N=5, 95% bootstrap CIs) shows that CSN yields relative improvements in CARLA Driving Score (DS, route completion weighted by infraction penalty) of +31.1% on original LMDrive and +24.5% on the PL-DPO-NLL variant. While causal structure accounts for an estimated 39.1% (95% CI [6.6%, 71.4%]) of the gain on original LMDrive versus 13.5% on PL-DPO-NLL, the structural share on the aligned model and the cross-configuration asymmetry remain statistically inconclusive at N=5 due to wide bootstrap intervals. A perception noise ablation further confirms that CSN's benefit survives distance errors up to +/-5 m, speed noise up to +/-30%, and 20% actor miss rates. Finally, we report a negative result on combining CSN with runtime supervision: a naive direction-conflict monitor never fires on this benchmark and, when paired with CSN, degrades DS via passive control clamping that truncates CSN-guided evasive maneuvers. Code is available at https://github.com/LiYun0607/CausalSceneNarration.

Why it counts

A controlled ablation separating information content from text organization reveals that information content carries most of CSN's gain across both model weights (+6.17 DS on original LMDrive; +6.88 DS on PL-DPO-NLL). While causal structure accounts for an estimated 39.1% (95% CI [6.6%, 71.4%]) of the gain on original LMDrive versus 13.5% on PL-DPO-NLL, the structural share on the aligned model and the cross-configuration asymmetry remain statistically inconclusive at N=5 due to wide bootstrap intervals.

Sources

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What is not known

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