Memoryless generation succeeds for any countable collection of infinite languages under an enumeration restriction, with optimal minimax densities for finite collections via Sperner's theorem; sliding windows add no worst-case benefit while adaptive storage does, and approximate identification works
Language generation in the limit
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Relaxing eventual validity to vanishing error rates in language generation in the limit strictly increases recall under partial revelation by the adversary.
Task information structure determines ML scaling success, with code's dense verifiable signals enabling predictable progress while sparse-feedback tasks like typical RL do not.
citing papers explorer
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On Language Generation in the Limit with Bounded Memory
Memoryless generation succeeds for any countable collection of infinite languages under an enumeration restriction, with optimal minimax densities for finite collections via Sperner's theorem; sliding windows add no worst-case benefit while adaptive storage does, and approximate identification works
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Generating in the Limit with Infinitely Many Hallucinations
Relaxing eventual validity to vanishing error rates in language generation in the limit strictly increases recall under partial revelation by the adversary.
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Why Code, Why Now: An Information-Theoretic Perspective on the Limits of Machine Learning
Task information structure determines ML scaling success, with code's dense verifiable signals enabling predictable progress while sparse-feedback tasks like typical RL do not.