The paper introduces an automated method that employs Large Language Models in a check–fix–verify loop to generate executable Python scripts for extracting problem-specific features from constraint satisfaction problems. Given a MiniZinc model and instance, the LLM agent produces code that builds a typed graph representation and computes structural properties such as graph density, variable clustering, and constraint tightness. Evaluated on vehicle routing, car sequencing, and fixed‑length error‑correcting codes, the synthesized extractors enable algorithm selectors that outperform expert‑curated mzn2feat features and transformer‑based trans2feat variants, while remaining interpretable.
arXiv:2608. 08127v1 Announce Type: new Abstract: The runtime of Constraint Programming (CP) solvers is highly sensitive to modeling choices, such as symmetry breaking, implied constraints, global constraints, constraint reformulation, and variable representation.
arXiv:2606. 04816v1 Announce Type: new Abstract: Large language models (LLMs) increasingly translate natural-language optimization problems into executable solver code.
arXiv:2512. 13374v2 Announce Type: replace Abstract: Recent advances in Large Language Models (LLMs) open new perspectives for automation in optimization, yet little is known about whether their internal representations capture problem structure or algorithmic behavior.
arXiv:2609.37849v1 Announce Type: cross Abstract: Scientific software is increasingly required to process larger datasets while maintaining acceptable execution times. Software optimization tradition...
arXiv:2608. 13333v1 Announce Type: new Abstract: Large neighborhood search normally selects a random subset of decision variables for iterative optimization.
Mixed-integer programming (MIP) lies at the core of operations research and industrial optimization. While large language models (LLMs) have recently shown promise in automated MIP modeling from natur...
OptSkills is an archetype‑centric agent that learns and reasons about optimization problems using large language models. It clusters problems by underlying archetypes, explores diverse modeling and solver configurations within each cluster, and distills successful trajectories into reusable workflow‑level skills. The system achieves state‑of‑the‑art accuracy on multiple datasets, outperforming prior methods on challenging benchmarks such as MIPLIB‑NL and OOD NLCO.
arXiv:2608. 02641v1 Announce Type: cross Abstract: Large language models (LLMs) can translate natural-language optimization problems into solver-ready formulations, but direct code generation is brittle: schema, indexing, and semantic errors can cause compilation failures, infeasible models, or incorrect objectives, while iterative repair, search, and multi-agent workflows increase inference cost.
arXiv:2608.23353v1 Announce Type: new Abstract: Mixed-integer programming (MIP) lies at the core of operations research and industrial optimization. While large language models (LLMs) have recently s...
arXiv:2503. 03137v3 Announce Type: replace Abstract: Constructive neural combinatorial optimization (NCO) offers a promising paradigm for solving vehicle routing problems (VRPs) by directly learning to construct approximate optimal solutions, thereby reducing reliance on expert knowledge for algorithm design.
arXiv:2601. 09097v3 Announce Type: replace Abstract: Multi-constraint planning involves identifying, evaluating, and refining candidate plans while satisfying multiple, potentially conflicting constraints.
arXiv:2608. 01633v1 Announce Type: new Abstract: Large language models (LLMs) enable neural architecture search (NAS) directly over executable neural network programs.