[Submitted on 29 Aug 2026]
Title:Evidence-Order Calibration for Selective Visual Reasoning under Progressive Loss of Question-Critical Evidence
View a PDF of the paper titled Evidence-Order Calibration for Selective Visual Reasoning under Progressive Loss of Question-Critical Evidence, by Muhamathu Ameer Ali Aacaas Muhamath
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Abstract:Vision-language model (VLM) confidence may change in aggregate when visual evidence is degraded while remaining structurally inconsistent within individual examples. We study answer-level reliability along five-step, question-conditioned evidence-loss trajectories. Using a frozen Qwen2.5-VL-3B-Instruct model, we construct 176 accepted GQA-derived trajectories (880 masking conditions) by progressively masking scene-graph-localized question-critical regions. Native sequence confidence has an evidence monotonicity violation rate (EMVR) of 0.436, and 92.0% of trajectories contain at least one adjacent violation. A matched non-critical-region control shows that full critical masking reduces accuracy by 28.2 percentage points, compared with 0.6 points for equally sized non-critical masks; the paired difference is 27.6 points (95% CI [20.0, 34.7]). We train a lightweight post-hoc reliability head on frozen hidden states, sequence confidence, and entropy. Adding evidence-order supervision to binary cross-entropy (BCE) reduces masking EMVR from 0.330 to 0.303 (paired difference -0.027, 95% CI [-0.044, -0.010]). The same mask-trained objective reduces EMVR from 0.449 to 0.402 on held-out question IDs under unseen local Gaussian blur (difference -0.0468, 95% CI [-0.0739, -0.0199]). AUROC, Brier, and AURC differences between the two learned heads are statistically inconclusive, and native confidence remains stronger for selective-risk ranking. The results separate evidence-order consistency from conventional correctness discrimination rather than establishing generic confidence superiority.
Comments: 8 pages, 6 figures, 4 tables. Code and supplementary materials: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.09184 [cs.CV]
(or arXiv:2609.09184v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.09184
arXiv-issued DOI via DataCite
Submission history
From: Aacaas Muhamath [view email] [v1] Sat, 29 Aug 2026 20:29:03 UTC (1,472 KB)
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