OR-Space is a benchmark for LLM agents performing full-lifecycle optimization tasks across Build, Revise, and Explain modes in executable multi-artifact workspaces.
Orlm: A customizable framework in training large models for automated optimization model- ing.Operations Research, 73(6):2986–3009, November 2025
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A source-extended Lie-group algorithm derives Ward identities for elastic Burgulence, showing viscoelastic turbulence has weaker symmetry constraints than Navier-Stokes turbulence.
AutoOR uses synthetic data generation and RL post-training with solver feedback to enable 8B LLMs to autoformalize linear, mixed-integer, and non-linear OR problems, matching larger models on benchmarks.
ORLA combines LLM-generated MIP formulations with solver verification and learning-based selection to improve multi-warehouse inventory allocation accuracy by 4.5 percentage points on 29 JD.com production batches.
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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Functional Renormalization for Elastic Burgulence
A source-extended Lie-group algorithm derives Ward identities for elastic Burgulence, showing viscoelastic turbulence has weaker symmetry constraints than Navier-Stokes turbulence.
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AutoOR: Scalably Post-training LLMs to Autoformalize Operations Research Problems
AutoOR uses synthetic data generation and RL post-training with solver feedback to enable 8B LLMs to autoformalize linear, mixed-integer, and non-linear OR problems, matching larger models on benchmarks.
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Solver-Verified Formulation Generation and Selection for Multi-Warehouse Inventory Allocation Using Large Language Models
ORLA combines LLM-generated MIP formulations with solver verification and learning-based selection to improve multi-warehouse inventory allocation accuracy by 4.5 percentage points on 29 JD.com production batches.