arXiv Machine Learning

SMT-Based Active Learning of Weighted Automata

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 Machine Learning

Aug 20

Learning Canonical Register Automata over Ordered Data Domains

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.

By Yong Li, Qiyi Tang, Di-De Yen

arXiv Machine Learning

Jun 30

Learning Deterministic Finite-State Machines from the Prefixes of a Single String is NP-Complete

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.

By Radu Cosmin Dumitru, Ryo Yoshinaka, Ayumi Shinohara

arXiv Machine Learning

Jul 15

Language Identification with Succinct Machine-Independent Traces

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.

By Moses Charikar, Jon Kleinberg, Chirag Pabbaraju

llms

arXiv Machine Learning

Jun 2

Incentivized Collaboration in Active Learning

arXiv:2311. 00260v2 Announce Type: replace-cross Abstract: In collaborative active learning, where multiple agents try to learn labels from a common hypothesis, we introduce an innovative framework for incentivized collaboration.

By Lee Cohen, Han Shao

agents

arXiv Machine Learning

Jun 30

Extrapolating from Regularised Solutions for Solving Ill-Conditioned Linear Systems in Machine Learning

arXiv:2606. 30328v1 Announce Type: cross Abstract: Rapid prototyping of algorithms is a critical step in modern machine learning.

By Disha Hegde, Jon Cockayne, Chris. J. Oates

arXiv AI

Jun 30

Machine-learnable Sets

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.

By Veit Elser, Manish Krishan Lal

arXiv AI

Jun 2

ActiveUltraFeedback: Efficient Preference Data Generation using Active Learning

arXiv:2603. 09692v2 Announce Type: replace-cross Abstract: Reinforcement Learning from Human Feedback (RLHF) has become the standard for aligning Large Language Models (LLMs), yet its efficacy is bottlenecked by the high cost of acquiring preference data, especially in low-resource and expert domains.

By Davit Melikidze, Marian Schneider, Jessica Lam, Martin Wertich, Ido Hakimi, Barna P\'asztor, Andreas Krause

llms reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 11

Counterexample Guided Learning in the Large using Reasoning Agents

arXiv:2606. 11521v1 Announce Type: new Abstract: LLMs and LLM agents should improve when given feedback, but identifying when they are able to do so is difficult: feedback is heterogeneous, domain-specific, and difficult to control.

By Hongyi Liu, Frederic Sala, Thomas Reps, Adithya Murali

llms agents

arXiv AI

Sep 2

On Synthesis of Metric Interval Temporal Logics

arXiv:2609.01032v1 Announce Type: cross Abstract: Automated mining of formal specifications is vital for verifying real-time systems. However, existing passive learning approaches remain restricted t...

By Hsi-Ming Ho, Shankaranarayanan Krishna, Khushraj Madnani

benchmarks

arXiv AI

Aug 14

Algebraic Decomposition Theory for Transformer Length Generalization

arXiv:2608. 13433v1 Announce Type: cross Abstract: Transformer-based language models are known to sometimes generalize to sequences longer than seen during training, but we lack a precise characterization of which tasks admit length generalization.

By Andy Yang, Blerta Veseli, Corentin Barloy, Micha\"el Cadilhac, Andreas Krebs, Charles Paperman, Howard Straubing, Michael Hahn

llms

arXiv AI

Aug 17

Semantically Labelled Automata for Multi-Task Reinforcement Learning with LTL Instructions

arXiv:2602. 06746v2 Announce Type: replace Abstract: We study multi-task reinforcement learning (RL), a setting in which an agent learns a single, universal policy capable of generalising to arbitrary, possibly unseen tasks.

By Alessandro Abate, Giuseppe De Giacomo, Mathias Jackermeier, Jan Kret\'insk\'y, Maximilian Prokop, Christoph Weinhuber

rag agents reinforcement-learning benchmarks

arXiv Machine Learning

Jul 21

MTSSL: Meta-Thresholding Semi-Supervised Learning

arXiv:2607. 16363v1 Announce Type: cross Abstract: A large body of Semi-supervised Learning~(SSL) algorithms encounter the threshold $\tau$ to select pseudo-labels.

By Shuyang Liu, Ziang Zeng, Ruiqiu Zheng, Jiazheng Wang, Zechen Liu, Wenxi Li, Zhou Yu

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