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[2602.22465] ConstraintBench: Benchmarking LLM Constraint Reasoning on Direct Optimization
Summary
The paper introduces ConstraintBench, a benchmark designed to evaluate large language models (LLMs) on direct constrained optimization tasks across various domains, highlighting their limitations in producing optimal solutions.
Why It Matters
As LLMs are increasingly utilized in decision-making processes involving optimization, understanding their capabilities and limitations is crucial. ConstraintBench provides a structured way to assess how well these models can handle complex optimization problems without relying on external solvers, which is vital for their practical application in operational settings.
Key Takeaways
- ConstraintBench evaluates LLMs on their ability to solve constrained optimization problems directly.
- The best-performing model achieved only 65% constraint satisfaction, indicating significant room for improvement.
- Feasibility is a major challenge, with models often misunderstanding constraints or hallucinating entities.
- Performance varies widely across different domains, highlighting the need for tailored approaches.
- The benchmark and evaluation tools will be publicly available, promoting further research and development.
Computer Science > Artificial Intelligence arXiv:2602.22465 (cs) [Submitted on 25 Feb 2026] Title:ConstraintBench: Benchmarking LLM Constraint Reasoning on Direct Optimization Authors:Joseph Tso, Preston Schmittou, Quan Huynh, Jibran Hutchins View a PDF of the paper titled ConstraintBench: Benchmarking LLM Constraint Reasoning on Direct Optimization, by Joseph Tso and 3 other authors View PDF HTML (experimental) Abstract:Large language models are increasingly applied to operational decision-making where the underlying structure is constrained optimization. Existing benchmarks evaluate whether LLMs can formulate optimization problems as solver code, but leave open a complementary question. Can LLMs directly produce correct solutions to fully specified constrained optimization problems without access to a solver? We introduce ConstraintBench, a benchmark for evaluating LLMs on direct constrained optimization across 10 operations research domains, with all ground-truth solutions verified by the Gurobi solver. Each task presents a natural-language scenario with entities, constraints, and an optimization objective; the model must return a structured solution that a deterministic verifier checks against every constraint and the solver-proven optimum. We evaluate six frontier models on 200 tasks and find that feasibility, not optimality, is the primary bottleneck. The best model achieves only 65.0% constraint satisfaction, yet feasible solutions average 89 to 96% of the Gurobi-op...
