OR-Space is a benchmark for LLM agents performing full-lifecycle optimization tasks across Build, Revise, and Explain modes in executable multi-artifact workspaces.
arXiv preprint arXiv:2509.22979 (2025)
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A code-graph and correction-based LLM search framework outperforms full-algorithm generation at equal token budgets on three combinatorial optimization problems.
Constraint injection forms a dual verifier with differential testing to improve LLM translation of natural-language VRPs into Gurobi code, yielding VRPCoder at 93% average Pass@1 across benchmarks.
LLM agent translates user prompts into model patches and selects primal-aware re-optimization techniques for large-scale dynamic problems, shown on supply-chain and exam-scheduling cases.
Graph-grounded optimization sources problem elements from knowledge graphs and shows Rao-family metaheuristics plus OR-tools perform differently across seven real-world KG-backed problems while surfacing data issues.
Opt-Verifier adds structure-side and solution-side verification to LLM-generated optimization models and reports over 20% accuracy gains on standard benchmarks.
Regression models fit observed LP solver runtimes well within instance classes, but asymptotic growth rates differ substantially across simplex, interior-point, and PDHG methods.
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Budget-Efficient Automatic Algorithm Design via Code Graph
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