[CFP] 2nd Workshop on LLMs meet Constraint Solving (LLM-Solve 2026)

[Serdar Kadioglu]

LLMs meet Constraint Solving (LLM-Solve) at FLoC'26

The second edition of LLMs meet Constraint Solving (LLM-Solve) will take place on July 19, 2026, at the University Institute of Lisbon (ISCTE) Campus in the city center of Lisbon, Portugal as part of the Federated Logic Conference (FLoC'26) and Constraint Programming (CP'26).

• LLM-Solve workshop: https://sites.google.com/view/llm-solve-2026
• The Federated Logic Conference (FLoC): https://www.floc26.org/venue
• Constraint Programming (CP): https://cp2026.a4cp.org/
• Submissions: https://submissions.floc26.org/llm-solve/

IMPORTANT DATES

• Submission deadline: May 15, 2026
• Notification: May 25, 2026
• Workshop date: July 19, 2026

ABOUT THE WORKSHOP

Large Language Models (LLMs) have rapidly evolved from purely natural-language tools into powerful agentic systems capable of orchestrating complex tasks, interacting with external tools, and executing multi-step reasoning workflows. This new generation of models promises transformative applications across scientific discovery, automation, and decision support. Yet despite their impressive versatility, LLMs still struggle with consistency, reliability, and formal correctness—limitations that become critical when deploying autonomous agents in domains requiring structured reasoning, constraint satisfaction, or guaranteed solution quality.

On the other hand, constraint-solving technologies such as CP/SAT/OR provide mature frameworks for modeling and solving combinatorial problems with provable guarantees on correctness, optimality, and explainability. However, the need for expert knowledge in formalizing problems, selecting appropriate modeling abstractions, and choosing suitable solving techniques continues to limit the broader adoption of these powerful methods.

Bringing LLMs and constraint solving together opens new opportunities on both sides: LLMs can support non-experts in model formulation, constraint acquisition, and interactive problem solving, while constraint solvers can serve as execution engines, verification modules, and structured reasoning components for LLM-driven agents. Recent research has shown promising results in both directions—from LLM-assisted constraint modeling and hybrid search to solver-backed LLM validation, correction, and planning.

The LLM-Solve 2026 workshop aims to unite researchers working at this rapidly evolving frontier to discuss challenges, synergies, and future research directions. The workshop seeks to shape the emerging landscape of LLM-powered constraint solving and constraint-driven LLM architectures.

AIMS AND SCOPE

The LLM-Solve 2026 workshop aims to bring together researchers exploring the intersection of Large Language Models (LLMs) and Constraint Solving (CP, SAT, SMT, MIP, and related paradigms). This workshop provides a platform to discuss recent advances, challenges, and opportunities in combining LLMs and constraint solving.

The workshop covers both directions of this interaction:

1. LLMs for Constraint Solving: Investigating how LLMs and agentic systems can be used to tackle challenges in constraint solving research, i.e., assist in constraint modeling, solving, and explanations; including automated constraint acquisition, solver configuration and selection, solver heuristics, automated solver code generation and natural-language based solving and solution refinement.
2. Constraint Solving for LLMs: Exploring how constraint-solving techniques can improve LLM reasoning and verification, LLM safety, and applications in structure reasoning, formal verification, and more.

The topics of interest include (but are not restricted to):

• LLMs and agentic systems for constraint modeling and acquisition
• LLM-guided solver heuristics and search strategies
• Hybrid approaches combining LLMs and constraint solvers
• Constraint solvers for improving LLM reasoning and verification
• SAT/CP-based methods for controlling or guiding LLM outputs
• LLM-driven explanations and interactive constraint solving
• Benchmarks, datasets and evaluation methodologies for LLM-Constraint integration
• Real-world applications involving the use of constraint-solving technologies and LLMs.

This workshop welcomes contributions from both theoretical and applied perspectives, fostering discussions between researchers in CP, SAT, AI, OR, and NLP who are interested in bridging the gap between constraint solving and natural language processing with LLMs.

SUBMISSIONS

Authors are invited to send contributions in the form of extended abstracts (up to 2 pages, without counting references). The authors can optionally add up to 10 pages of technical report after the extended abstract. Submissions can be published journal/conference papers, original work, work in progress with preliminary results, or position papers.

Contributions should be submitted in the form of a PDF file, following LIPIcs guidelines: https://submission.dagstuhl.de/series/details/5#author

The workshop co-chairs will select the papers to be presented at the workshop according to their suitability to the aims. All presenters and attendees are expected to register for the CP/SAT workshop day.

Submission page: https://submissions.floc26.org/llm-solve/

ORGANIZATION

The workshop co-chairs are:

• Tias Guns (KU Leuven, Belgium), https://people.cs.kuleuven.be/~tias.guns/
• Serdar Kadıoglu (Brown University, USA), https://skadio.github.io/
• Stefan Szeider (TU Wien, Austria), https://www.ac.tuwien.ac.at/people/szeider/
• Dimos Tsouros (UOWM, Greece), https://dimostsouros.github.io/