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DOI: 10.4230/LIPIcs.CP.2026.18

Neurosymbolic Large Neighbourhood Search

Authors: Arnaud Delage-Reid, Gilles Pesant, and Amal Zouaq

Published in: LIPIcs, Volume 379, 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026)

Abstract

Recent advances in ai have spurred interest in NeSy architectures that integrate neural and symbolic methods. In particular, combining a Constraint Programming (cp) model with a language model for constrained sequence generation tasks allows the neural component to capture domain knowledge while cp enforces structural constraints. In this paper we propose combining cp with a Masked Language Model (mlm) to perform Large Neighbourhood Search (lns). Unlike conventional left-to-right Large Language Models, mlm s can complete sequences with gaps in arbitrary positions, making them well-suited for this task. Meanwhile, lns provides a cp-based iterative framework to explore constrained subspaces whenever searching the whole space would be intractable. We evaluate NeSylns on tasks in constrained text generation and molecule discovery. Our experiments show that it can quickly generate many high-quality sentences and molecules, even for highly-constrained tasks.

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Arnaud Delage-Reid, Gilles Pesant, and Amal Zouaq. Neurosymbolic Large Neighbourhood Search. In 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 379, pp. 18:1-18:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{delagereid_et_al:LIPIcs.CP.2026.18,
  author =	{Delage-Reid, Arnaud and Pesant, Gilles and Zouaq, Amal},
  title =	{{Neurosymbolic Large Neighbourhood Search}},
  booktitle =	{32nd International Conference on Principles and Practice of Constraint Programming (CP 2026)},
  pages =	{18:1--18:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-432-1},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{379},
  editor =	{Beldiceanu, Nicolas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2026.18},
  URN =		{urn:nbn:de:0030-drops-266514},
  doi =		{10.4230/LIPIcs.CP.2026.18},
  annote =	{Keywords: Large Neighbourhood Search, Neurosymbolic AI, Constraint Programming, Masked Language Model, CPBP Solver}
}

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Neurosymbolic Large Neighbourhood Search

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