SPOT-E: Test-Time Entropy Shaping with Visual Spotlights for Frozen VLMs
Vision-language models (VLMs) often underperform on evidence intensive tasks because decisive visual evidence are small, localized, and easy to overlook, leading to failures in evidence readout even when high-level reasoning is intact. Prior inference-time visual interventions can improve grounding without retraining, but they are largely open-loop and lack a mechanism to verify whether highlighted evidence is actually used. We study answer-span prediction entropy as a model-internal feedback signal and show that naive entropy minimization is ambiguous, since low entropy may arise from evidence-grounded confidence or shortcut collapse. To resolve this ambiguity, we introduce low-entropy a...
| Authors | Bo Yin, Xiaobin Hu, Chengming Xu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 18 June 2026 |
| Last updated at source | 19 June 2026 |
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