Inference-Time Attention Steering for Vision-Language-Action Driving Models
arXiv:2608.17095v1 Announce Type: new Abstract: Vision-language-action (VLA) driving models couple a reasoning stage with a diffusion-based trajectory decoder, but do not give a direct way to redirect attention toward safety-critical actors at inference time without retraining. We studied a bounded additive pre-softmax attention bias on the visual tokens of detector localized traffic actors on Alpamayo-R1's Qwen3-VL backbone. It is applied as a fail open forward pre-hook with no weight changes. On 50 lane-change scenarios from the Physical AI World Model Synthetic dataset. The trajectory decoder shows a monotonic dose response in the bias magnitude, separate from a paired zero bias control at every tested magnitude. It reaches $\approx 17$\,cm mean displacement with lateral shifts up to $\sim 140$\ cm at the clamp. A layer ablation places the action-relevant signal in late layers, where the effect increases with the number of hooked layers (2.0cm for the first 8 layers; 67.6cm for all 36). A per call injection audit explains why the Chain-of-Causation text never changes. The mask based bias never reaches the reasoning pathway in this serving stack, so the invariance is verified exposure, not robustness. Steered trajectories tend to shift toward the attended actor, suggesting the bias governs where the model looks rather than encoding a target behavior.
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[Submitted on 17 Aug 2026]
Title:Inference-Time Attention Steering for Vision-Language-Action Driving Models
View a PDF of the paper titled Inference-Time Attention Steering for Vision-Language-Action Driving Models, by Darshan Nagendra Prasad and 2 other authors
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Abstract:Vision-language-action (VLA) driving models couple a reasoning stage with a diffusion-based trajectory decoder, but do not give a direct way to redirect attention toward safety-critical actors at inference time without retraining. We studied a bounded additive pre-softmax attention bias on the visual tokens of detector localized traffic actors on Alpamayo-R1's Qwen3-VL backbone. It is applied as a fail open forward pre-hook with no weight changes. On 50 lane-change scenarios from the Physical AI World Model Synthetic dataset. The trajectory decoder shows a monotonic dose response in the bias magnitude, separate from a paired zero bias control at every tested magnitude. It reaches $\approx 17$\,cm mean displacement with lateral shifts up to $\sim 140$\ cm at the clamp. A layer ablation places the action-relevant signal in late layers, where the effect increases with the number of hooked layers (2.0cm for the first 8 layers; 67.6cm for all 36). A per call injection audit explains why the Chain-of-Causation text never changes. The mask based bias never reaches the reasoning pathway in this serving stack, so the invariance is verified exposure, not robustness. Steered trajectories tend to shift toward the attended actor, suggesting the bias governs where the model looks rather than encoding a target behavior.
Comments: Attention Steering, Vision-Language-Action, AutonomousDriving, Inference-Time Intervention
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2608.17095 [cs.CV]
(or arXiv:2608.17095v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.17095
arXiv-issued DOI via DataCite (pending registration)
Journal reference: European Conference on Computer Vision 2026
Submission history
From: Lars Ullrich [view email] [v1] Mon, 17 Aug 2026 20:03:38 UTC (2,640 KB)
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