arXiv:2605. 11644v2 Announce Type: replace-cross Abstract: We study positive-data learning of languages admitting reduced working binary linear nondeleting multiple context-free grammar presentations of bounded fan-out.
By Takayuki Kuriyama
arXiv:2605. 11644v3 Announce Type: replace-cross Abstract: Positive data can show that two tuple occurrences share a successful sentence context without certifying that they are safely interchangeable.
By Takayuki Kuriyama
arXiv:2607. 29496v1 Announce Type: new Abstract: We study transcript management for fixed, finite-precision causal Transformers.
By Sergey Salishev
Gold's theorem shows that no learner can identify an arbitrary language in a superfinite class from positive presentations alone. We prove a positive result of a different shape. Fix an innate \emph{r...
The paper presents a positive result contrasting Gold’s theorem by showing that a learner equipped with a recursive contrast operator and a bounded-capacity InfoNCE-style scorer can achieve PAC conditional identification from positive data alone. Under a faithfulness condition, the trained scorer ranks almost all positives above any out-of-language perturbation within their orbit, with polynomial sample complexity in the operator’s capacity, orbit size, score bound, and error tolerance. The method constructs negatives internally, avoids global normalization, and extends to a membership classifier and a normalization-free Metropolis–Hastings generator, with instantiations for substitutable context‑free and subregular languages.
whyItMatters":"The work demonstrates that contrastive learning can overcome classical limits on language identification by positive data, offering a concrete, sample‑efficient framework for learning structured language classes without external negative examples."
By Rakesh Sengupta
The paper introduces the notion of finitely verifiable segmentation, where a finite automaton accepts exactly the segmentations produced by a given policy. It proves that several key decision problems—such as boundary certification, bounded gaps, mask completable generation, and policy equivalence—become decidable for this class, which lies strictly between online‑scannable and regular‑domain policies. The class is closed under common pipeline operations and supports practical implementations like maximal‑munch lexing and WordPiece, with all results accompanied by executable checks.
By Nicklas Nidh\"ogg