arXiv:2601. 12621v2 Announce Type: replace-cross Abstract: It is well known that computing a minimum deterministic finite automaton consistent with a given set of positive and negative examples is NP-hard.
arXiv:2606. 28947v1 Announce Type: cross Abstract: In this study we present a formal definition of large discrete sets having, informally, three properties: their elements are easily recognized, easily generated, and the latter tasks are easily learned from examples.
The paper introduces an SMT-based active learning algorithm for nondeterministic weighted finite automata (WFAs), offering a practical alternative to traditional Hankel/L*-style methods. The algorithm is parametric over a chosen semiring and, upon termination, guarantees the production of minimal WFAs. Experiments demonstrate that it learns numerous minimal WFAs over both finite and infinite semirings, outperforming a naive baseline and competing with state‑of‑the‑art algorithms while yielding smaller automata and requiring less teacher interaction.
arXiv:2511. 15709v2 Announce Type: replace-cross Abstract: Recent works have shown that tokenisation is NP-complete.
The paper presents a polynomial‑time active learning procedure for deterministic register automata (DRAs) over ordered data domains, covering both dense domains like the rationals and non‑dense domains such as the integers. It unifies the learning framework for these domains using membership, equivalence, and memorability queries, and shows that minimization of DRAs over the integer domain is decidable. Additionally, the authors provide improved complexity bounds for several decision problems related to DRAs over ordered domains.
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:2607. 17369v1 Announce Type: cross Abstract: In a previous paper, we began the study of sequence prediction algorithms adapted to stringological word complexity measures.
arXiv:2606. 18807v1 Announce Type: cross Abstract: The field of learning-augmented algorithms has demonstrated that machine-learned predictions can bypass worst-case lower bounds across a wide range of problems.
Transduced language models (TLMs) combine a pretrained source language model with a finite‑state transducer to produce a language model over target strings. The paper introduces an unbiased stochastic estimator that resamples source prefixes without replacement and reweights them, allowing accurate estimation of target prefix probabilities while reducing computation compared to threshold‑pruned beam summing. Experiments on encyclopedic text, DNA, and DNA‑to‑amino‑acid transduction show improved compute–variance trade‑offs and significant runtime reductions, and the method also lowers estimated corpus surprisal in a reading‑time analysis without altering its conclusions.
arXiv:2609.08851v1 Announce Type: new Abstract: Recent advancements in transformer length generalization theory enable us to reliably predict when a transformer can learn to solve a task. In particul...
Recently, Antoniadis et al. (ICLR 2025) proposed a framework for incorporating predictions to approximate NP-hard selection problems.
arXiv:2607. 21188v1 Announce Type: new Abstract: The Single Constant Multiplication problem is a fundamental NP-hard optimization task in hardware design, which seeks to decompose a fixed constant using only additions, subtractions, and bit-shifts.
arXiv:2608. 14102v1 Announce Type: new Abstract: We consider the problem of sequential prediction of an $m$-ary sequence, where at each epoch, (i) the environment selects an outcome from an $m$-ary alphabet, (ii) the learner selects a probability distribution over the same alphabet (unaware of the outcome generated by the environment), and finally, (iii) the learner incurs a cost that depends on the probability assigned to the outcome.