LLMs show near-zero calibration on hidden information decisions
Source published ·Modelwire updated
Original coverage: arXiv cs.LG ↗·How Modelwire adds context
The development
A new study exposes a critical failure mode in language models deployed as decision-making agents: stated confidence bears almost no relationship to actual correctness when reasoning about hidden information. Researchers tested LLMs in a chess variant where piece identities remain secret, then compared the model's probability estimates about hidden state against ground truth. High-confidence moves proved correct in only 1 of 62 cases, with 98+ percent of calibration error concentrated in these high-confidence failures. This directly threatens the safety assumption underlying many agentic systems that gate actions on model-reported confidence scores, suggesting current confidence elicitation methods fail precisely when stakes are highest.
Modelwire’s AI-generated summary of coverage from arXiv cs.LG.
Modelwire analysis
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The study isolates a specific failure: LLMs don't just reason poorly about hidden information, they express unwarranted certainty while doing so. This breaks a core assumption in agentic safety design, where confidence scores are meant to flag when a model should defer or refuse.
This connects directly to the post-training efficiency work covered in 'On-policy Distillation with Verifiable Reward' from late August. That paper focused on integrating task-level correctness signals into LLM training without hyperparameter tuning. The current study suggests a deeper problem: even with better reward signals, models may learn to express high confidence in domains where ground truth is genuinely inaccessible. The calibration failure here isn't a training artifact but a structural feature of how LLMs reason under uncertainty. It also echoes the fairness auditing work from the same period, which found that small input changes can flip verdicts without model behavior actually changing. Here, the model's stated confidence is similarly decoupled from its actual epistemic state.
If researchers can show that fine-tuning on hidden-information tasks (where ground truth is verifiable during training) reduces calibration error below 50 percent on held-out variants, that would suggest the failure is correctable. If calibration remains near 98 percent despite such training, it points to a harder architectural constraint that confidence-gating alone cannot solve.
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Coverage behind this analysis
These archive entries ground the connection in our analysis. They are ordered by source publication date, with links to our coverage and the original sources.
·arXiv cs.LG
Hyperparameter-free method merges distillation with verifiable rewards for LLM training
A new method called OPDVR addresses a fundamental tension in LLM post-training: dense token-level guidance from distillation versus task-level correctness from verifiable rewards. By integrating both signals without introducing additional hyperparameters, the approach sidesteps the manual tuning burden that has plagued prior hybrid methods. This matters because post-training efficiency directly impacts model quality and deployment…
MentionsLLM · agentic systems
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Modelwire summarizes, we don’t republish. arXiv cs.LG originally reported this story as “Confident at the moment of action: belief miscalibration in LLM play under hidden information”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.