Bayesian Intelligence from the Outside
Inferring intelligence from observable behavior is a foundational challenge in artificial intelligence. We develop a theory of Bayesian intelligence for agents such as language models. Each prompt induces a possibly imperfect internal experiment; the agent updates a full-support prior by Bayes' rule and faithfully reports its posterior over the possible answers to the question. Repetitions draw fresh, independent outcomes from the same unobserved experiment at one fixed state. We show that the agent's behavior admits this explanation if and only if its reports are not fully contradictory, i.e., some state remains possible under every report across all prompts. Report frequencies and the s...
| Authors | Alex Smolin, Bryan Wilder |
|---|---|
| Primary category | cs.AI |
| Published | 13 September 2026 |
| Last updated at source | 13 September 2026 |
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