Visual Semantic Entropy: Do Vision Language Models Recognize Visual Ambiguity?
Vision-language models exhibit a critical failure mode: they generate high-confidence predictions on visually ambiguous inputs while standard uncertainty quantification methods fail to detect it. Researchers show that entropy-based approaches like Semantic Entropy underestimate uncertainty because overconfident visual embeddings suppress output diversity during decoding. Perturbation-based alternatives designed to probe robustness instead conflate textual sensitivity with visual understanding, masking the core problem. This work exposes a fundamental gap in how we measure VLM reliability, with direct implications for deployment in safety-critical domains where false confidence on ambiguous visual inputs poses real risk.
Modelwire context
ExplainerThe paper's sharpest contribution isn't just that VLMs are overconfident, which is well-documented, but that the tools we use to detect overconfidence are themselves blind to visually-sourced uncertainty. The failure is in the measurement layer, not only the model.
This connects directly to the benchmark integrity thread running through recent Modelwire coverage. The RVL-CDIP audit ('Revising RVL-CDIP') showed that corrupted labels and test-train leakage let models appear reliable when they aren't, and this paper surfaces an analogous problem one level up: even when benchmarks are clean, the uncertainty metrics used to validate VLM deployment may be structurally incapable of flagging the failure modes that matter most in production. Both stories point to the same underlying risk, that the scaffolding we use to certify model trustworthiness has quiet blind spots. The GoodQ quantization work also touched this space by noting that edge vision systems face compounded reliability constraints, which makes robust uncertainty signaling even more critical at deployment.
Watch whether safety-critical VLM deployments in medical imaging or autonomous systems begin citing Visual Semantic Entropy as an evaluation requirement within the next two to three conference cycles. Adoption there would confirm the gap is operationally recognized, not just academically noted.
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MentionsVision-language models · Semantic Entropy · Visual Semantic Entropy
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