arXiv:2607.08059v1 Announce Type: new Abstract: Uncertainty quantification for visual language models (VLMs) conventionally targets the answer token distribution. We provide the first three-family empirical characterisation of answer entropy behaviour in thinking-mode VLMs. Running four models on identical POPE adversarial samples, we find three qualitatively distinct patterns: Qwen3-VL-8B-Thinking shows complete collapse (ans H AUROC = 0.492); GLM-4.1V-9B-Thinking shows no collapse (0.716); and InternVL3-8B shows selective thinking (chains on only 50% of samples, ans H = 0.675 full / 0.602 th
The proliferation of advanced visual language models and the increasing demand for their reliable deployment necessitates deeper understanding of their internal reasoning processes and uncertainty.
This research provides critical insights into the trustworthiness and predictability of advanced AI models, impacting their integration into high-stakes applications and highlighting performance disparities.
The focus for evaluating VLM reliability expands beyond just answer distribution to include the epistemic signals within their reasoning chains, influencing model selection and development priorities.
- · AI safety researchers
- · Developers of robust VLM architectures
- · Industries requiring high-assurance AI
- · AI models with unreliable uncertainty quantification
- · Developers neglecting internal reasoning transparency
Further research and development will focus on improving uncertainty quantification and transparency in VLM reasoning chains.
Benchmarks for VLM performance will begin to incorporate metrics related to epistemic trustworthiness and reasoning chain analysis.
Public and regulatory trust in AI systems will increasingly depend on demonstrable internal reasoning reliability, not just output accuracy.
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Read at arXiv cs.LG