Measuring in-context algorithmic reasoning in language models against an exact Bayes-optimal reference
Whether large language models perform algorithmic inference or pattern completion is hard to test, because most benchmarks supply answers but no distributional reference for what the shown evidence licenses. F-ICL supplies one exactly: we exhaustively enumerate the 86 million valid programs of length at most 13 on a Turing-complete machine F, complement-symmetrised to remove output-polarity bias, and compute the exact posterior under a declared bounded Levin--Solomonoff prior. It is Bayes-optimal for that stated prior rather than universal, and models are never told it exists, so the score reads the inductive prior their served distribution already encodes. Across 105 serving configuratio...
| Authors | Luan Ozelim, Hector Zenil |
|---|---|
| Primary category | cs.LG |
| Published | 3 August 2026 |
| Last updated at source | 23 August 2026 |
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