Confident at the moment of action: belief miscalibration in LLM play under hidden information
Agentic systems increasingly gate actions on a model's own stated confidence, which assumes confidence tracks correctness at the moment of acting. We test this in a hidden-information chess variant where royal status can be secretly, repeatedly relocated between pieces, and where an agent's stated probability distribution over the opponent's hidden royal piece -- elicited every turn, separately from the move it chooses -- is scored against ground truth recoverable after the game. Across two independent batches, captures made at high stated confidence ($\geq 0.5$) about the hidden piece's locat
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 47%LLMs know when they are wrong. I made a fix relating to Anthropic's new "global workspace" paper [R] →
- PossiblePossibly related (embedding) · 46%When an AI agent says “done” how do you know it actually happened? [P] →
- FuzzySimilar title/name (fuzzy) · 84%liguodongiot/llm-action →
“Fuzzy title match (0.92): “Confident at the moment of action: belief miscalibration in ” ≈ “liguodongiot/llm-action””
- LinkedLinked via arxiv author · 85%Bhushan Kashinath Joshi →
“Confident at the moment of action: belief miscalibration in LLM play under hidden information”