When Thinking Hurts: Epistemic Signals in the Reasoning Chains of Visual Language Models

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 thinking-only).

Across all three thinking-mode models, thinking chain entropy outperforms answer entropy on the subset where chains are generated (0.647, 0.759, 0.608 vs.

0.492, 0.716, 0.602 respectively), suggesting chain signals are the more reliable predictor whenever chains are present.

This holds strongly for Qwen and GLM, but with only marginal and statistically unreliable advantage for InternVL3 (n_FP = 17).

A 300-sample VQAv2 pilot confirms chain entropy (0.680) outperforms answer entropy (0.595) on VQAv2 questions, with the gap largest for free-form answers (0.733 vs.

0.467).

On harder reasoning tasks (HallusionBench) both Qwen models show moderate signal (approx.

0.64), consistent with incomplete pre-commitment on difficult questions.

We additionally document structured abstention affecting 12-22% of queries with asymmetry toward absent-object queries, and a practical abstention gate raising accuracy from 71.0% to 93.8% at 62.7% coverage with no additional inference cost.