Visual language models (VLMs) have achieved an amazing ability to interpret images and generate descriptions, but one of the biggest challenges remains how to quantify when they don't know the correct answer. This problem, known as epistemic uncertainty, has traditionally been addressed by analyzing the token distribution of the final answer. However, recent research points to a much richer indicator: the internal chains of reasoning that some models generate before responding. Just as humans doubt and reflect—sometimes with effort—VLMs that 'think' produce thought sequences that can reveal clues about their true confidence. In this paper we explore the phenomenon of 'when thinking hurts', where epistemic signals extracted from those chains turn out to be more reliable predictors than simple response probabilities, and how companies can leverage this perspective to build more robust and transparent AI systems.
The novelty of recent approaches lies in the fact that not all models behave in the same way when asked to generate a chain of reasoning. When evaluating identical adversarial samples, three qualitatively distinct patterns are observed. In the former, the entropy of the response collapses completely: the model fails to reflect uncertainty in its final token, so the ability to detect errors is practically zero. In the second, the entropy remains robust, indicating that the model knows when it is unsure even without the need to analyze its internal process. The third pattern, perhaps the most interesting, is selective reasoning: the model only generates thought chains in about half of the queries, and it is precisely in those cases that the chain offers a more informative signal than the final answer. These findings suggest that the presence or absence of strings is not random, but that the model itself decides when to 'think' based on the difficulty of the question.
Why does the entropy of the chain of reasoning exceed the entropy of the response? The answer seems to lie in the granularity of the information. A reasoning chain contains multiple steps, each with its own token distribution. When the model hesitates, that doubt manifests itself in the dispersion of the tokens throughout the chain, while the final token can be issued with high confidence even if the entire previous process is unstable. In other words, the chain acts as a 'thermometer' of uncertainty that the model cannot hide at the last moment. Experiments on datasets such as VQAv2 confirm that the entropy of the chain (0.680) far exceeds that of the answer (0.595), and the gap becomes even greater in free-response questions, where the creativity of the model can hide its ignorance. Even in harder reasoning tasks, such as those involving visual hallucinations, the chain signal is still moderately useful, though not perfect.
An additional aspect that emerges from these analyses is structured abstention: a significant fraction of the queries – between 12% and 22% – are answered with some kind of explicit or implicit rejection, especially when asking about objects that are not present in the image. This asymmetrical behavior reveals that models are not always willing to 'guess' and that their silence or evasion may be an epistemic signal in itself. In fact, implementing a practical abstention door – a threshold that decides not to respond when uncertainty is too high – allows the accuracy to be raised from 71% to 93.8%, at the cost of covering only 62.7% of the consultations. For enterprise applications where the cost of a failure is high, this trade-off may be perfectly acceptable.
From a technical perspective, these results have direct implications for the design of systems based on artificial intelligence. Instead of treating the VLM as a black box that produces a response, we can instrument it to expose its chain of reasoning and subject it to an entropy analysis. This is especially relevant for enterprise AI solutions where interpretability and trust are non-negotiable requirements. For example, in an image-assisted diagnostic system, a VLM that generates a dubious chain of reasoning can be referred to a human expert, rather than blindly relying on an answer that could be hallucinated. Integrating these types of epistemic signals into workflows allows you to build AI agents that are more accountable and aligned with business needs.
At Q2BSTudio we understand that the true power of artificial intelligence lies not only in average accuracy, but in the ability to know when you don't know. That is why we develop custom applications that incorporate uncertainty quantification mechanisms, from chained reasoning models to configurable abstention gates. Our team combines expertise in AWS and Azure cloud services with a deep understanding of deep neural networks and visual language models, enabling these solutions to be deployed at scale with optimized inference costs. In addition, we integrate power bi dashboards and other business intelligence services so that teams can monitor in real time the confidence of the answers and detect patterns of uncertainty that require intervention.
Cybersecurity also benefits from this approach. Adversarial attacks often exploit the overconfidence of models; if a VLM has a robust epistemic signal, it is less likely to be fooled by manipulated images. We offer penetration testing and cybersecurity advisory services to assess the vulnerability of AI systems to malicious input, ensuring that reasoning chains do not become an attack vector. In addition, our custom software projects allow you to customize every component, from the model architecture to the data pipeline, to suit the specific requirements of each industry.
In conclusion, the next frontier in VLM reliability is to listen not only to what they say, but how they say it. Chains of reasoning are open windows into a model's internal process, and their analysis offers epistemic signals that can transform the way companies adopt artificial intelligence. Whether it's filtering out dubious answers, training models more aware of their uncertainty, or building hybrid human-machine systems, the lesson is clear: when thinking hurts, it hurts for the model too, but that pain is valuable information. At Q2BSTudio we are committed to extracting that value for our clients, combining technical innovation with a pragmatic and results-oriented approach.
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