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This paper extends prior-fitted networks (TabPFN) to natural language by training a 300M-parameter byte-level transformer entirely on synthetic sequences drawn from randomly sampled recurrent causal models — a 'prior over languages'. With frozen weights, the model learns to predict real languages in context across six languages, and can also count, compare numbers, add approximately, and predict deterministic sequences like primes and Kolakoski, showing in-context language learning can emerge from a synthetic non-linguistic prior.
RecPFN introduces a prior-fitted network for in-context learning in sequential recommendations, pretrained on synthetic clickstream data to achieve state-of-the-art zero-shot performance across benchmarks.
This paper proposes CRUMB, a three-stage inference wrapper that clusters test queries and selects a distributionally matched training subset via MMD minimization to enable efficient Prior-Fitted Network inference on large datasets, achieving state-of-the-art context selection on 51 TabArena datasets.