arXiv:2607. 23361v1 Announce Type: cross Abstract: Language generation in the limit is an elegant model introduced by Kleinberg and Mullainathan [KM24] to formally study language generation by an algorithm that learns solely based on example strings.
arXiv:2606. 28354v1 Announce Type: cross Abstract: The classic paradigm of language identification in the limit models learning as a game between an adversary, who reveals strings from an unknown target language, and a learner tasked with identifying that language.
arXiv:2608. 01320v1 Announce Type: cross Abstract: Language generation in the limit is a theoretical framework for studying how a generator can learn to produce new valid strings from a stream of positive examples.
arXiv:2601. 21237v2 Announce Type: replace-cross Abstract: Kleinberg and Mullainathan recently proposed a formal framework for studying the phenomenon of language generation, called language generation in the limit.
arXiv:2606. 25777v1 Announce Type: cross Abstract: We initiate a resource-aware theory of \textit{language generation in the limit} under the minimal constraint of space efficiency.
The paper studies the problem of language generation in the limit, where a learner must produce valid unseen elements from any exhaustive positive presentation of an unknown infinite language. It establishes that generation is possible precisely when each target language admits a finite positive witness such that all targets activated by any finite sample share an infinite common intersection. The authors introduce a separation-width hierarchy to measure the size of compatible witnesses, showing that every level of the hierarchy occurs and that countable families admit singleton witnesses while more complex families require unbounded finite witnesses. The results are formalized and verified in Lean, with the development available on GitHub.
arXiv:2601. 08648v2 Announce Type: replace-cross Abstract: Recent results in learning a language in the limit have shown that, although language identification is impossible, language generation is tractable.
The paper investigates integer‑sequence benchmarks from the OEIS by applying a two‑part minimum description length (MDL) learner that searches for P‑recursive recurrences. It finds that MDL difficulty correlates with a combinatorial parameter count, that most sequences fit a recurrence on a prefix but not at full length (the “wilderness” regime), and that language models do not hallucinate in the wilderness but instead hedge, showing that memorisation dominates perceived competence. The study provides a cheap, contamination‑free difficulty signal for OEIS‑derived benchmarks.
arXiv:2607. 12443v1 Announce Type: cross Abstract: Motivated by the power of large language models, there has been renewed interest in the Gold-Angluin model of language identification in the limit, with an eye toward variants of the model that might overcome the negative results for its original formulation.
arXiv:2603. 11784v2 Announce Type: replace Abstract: As scaling laws push the training of frontier large language models (LLMs) toward ever-growing data requirements, training pipelines are approaching a regime where much of the publicly available online text may be consumed.
arXiv:2606. 14688v1 Announce Type: cross Abstract: AI systems coupled to proof assistants now generate formal mathematics at scale, and the gap between what a checker can verify and what a mathematician would value has become the binding constraint.
arXiv:2511.00763v3 Announce Type: replace Abstract: We investigate the performance of large language models (LLMs) on repetitive deterministic prediction tasks and study how the sequence accuracy rat...
arXiv:2608. 15979v1 Announce Type: new Abstract: Large language models produce outputs presented as discoveries - new proofs, conjectures, or molecules.