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.
A survey compiling roles, applications, benchmarks, challenges, and future directions for large language models in operations research.
citing papers explorer
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OR-Space: A Full-Lifecycle Workspace Benchmark for Industrial Optimization Agents
OR-Space is a benchmark for LLM agents performing full-lifecycle optimization tasks across Build, Revise, and Explain modes in executable multi-artifact workspaces.
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Budget-Efficient Automatic Algorithm Design via Code Graph
A code-graph and correction-based LLM search framework outperforms full-algorithm generation at equal token budgets on three combinatorial optimization problems.
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Beyond Objective Equivalence: Constraint Injection for LLM-Based Optimization Modeling on Vehicle Routing 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.
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Democratizing Large-Scale Re-Optimization with LLM-Guided Model Patches
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.
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Graph-Grounded Optimization: Rao-Family Metaheuristics, Classical OR, and SLM-Driven Formulation over Knowledge Graphs
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.
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Opt-Verifier: Unleashing the Power of LLMs for Optimization Modeling via Dual-Side Verification
Opt-Verifier adds structure-side and solution-side verification to LLM-generated optimization models and reports over 20% accuracy gains on standard benchmarks.
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Empirical Asymptotic Runtime Analysis of Linear Programming Algorithms
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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Large Language Models for Operations Research: A Comprehensive Survey
A survey compiling roles, applications, benchmarks, challenges, and future directions for large language models in operations research.