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arXiv Computer Vision By Xiaoshan Zhou

FearCaut-Qwen: Affective Steering in a Vision-Language Model Shifts the Decision Criterion for Hazard Assessment

Read the original on arXiv Computer Vision →

The paper investigates why vision‑language models (VLMs) often miss hazards, attributing this to an overly conservative decision criterion rather than poor perception. By identifying and steering an affective ‘fear’ circuit within the model, the authors shift the decision threshold, dramatically increasing hazard recall from 27.0 % to 75.7 % without retraining. The study demonstrates that affect‑based inference-time manipulation can correct VLM decision biases in practical applications.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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