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Evidence-Order Calibration for Selective Visual Reasoning under Progressive Loss of Question-Critical Evidence
Not provided in the abstract
visual reasoningconfidence calibrationevidence lossmultimodal
2609.09184
Builder Relevance
1h ago70%
Abstract
This paper investigates the reliability of vision-language models under conditions of degraded visual evidence, revealing significant inconsistencies in confidence levels.
Reality Card
Core Claim
The study demonstrates that adding evidence-order supervision to a binary cross-entropy loss reduces the evidence monotonicity violation rate (EMVR) from 0.330 to 0.303.
Method / Result
The evidence-order supervision approach reduced EMVR by 0.027, indicating improved reliability in visual reasoning.
Limitations
The results on AUROC, Brier, and AURC differences are statistically inconclusive, raising concerns about the reproducibility of the findings.
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