Calibration Drift Under Reasoning: How Chain-of-Thought Budgets Induce Overconfidence in Large Language Models
arXiv:2606.11211v1 Announce Type: new Abstract: The ability of large language models (LLMs) to express calibrated uncertainty is important for safe deployment. Chain-of-thought (CoT) reasoning is widely used to improve accuracy and reliability, but its effect on calibration is not fully understood. We show that this picture is incomplete: in some settings, increasing the reasoning budget beyond a task-specific threshold can cause models to become systematically overconfident, assigning high confidence to incorrect answers. We call this phenomenon Calibration Drift Under Reasoning (CDUR) and st
As LLMs become more integrated into critical applications, understanding the nuances of their reasoning and reliability, especially under increased computational 'budgets,' is becoming an urgent research area.
This research reveals a systemic limitation in LLMs' ability to manage uncertainty, suggesting that more 'thought' does not always lead to better judgment, which affects trust and safe deployment across industries.
The conventional wisdom that increased reasoning steps universally improves LLM performance and reliability is challenged, requiring a re-evaluation of how CoT and similar techniques are designed and implemented.
- · Researchers focused on LLM safety and reliability
- · Frameworks/platforms that monitor and mitigate model overconfidence
- · Overly simplistic deployments of LLMs in high-stakes environments
- · Organizations relying solely on CoT to guarantee LLM trustworthiness
LLM developers will need to incorporate advanced calibration techniques beyond simply increasing reasoning steps.
There will be a push for more explainable AI methods that can account for and correct 'Calibration Drift Under Reasoning' before deployment.
Regulatory bodies may begin to consider model calibration and overconfidence as key safety metrics for AI systems, influencing future compliance requirements.
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