arXiv:2602.10450v2 Announce Type: replace Abstract: Optimization modeling underpins decision-making in logistics, manufacturing, energy, and finance, yet translating natural-language requirements into correct optimization formulations and solver-executable code remains labor-intensive. Although large language models (LLMs) have been explored for this task, evaluation is still dominated by toy-sized or synthetic benchmarks, masking the difficulty of industrial problems with $10^{3}$--$10^{6}$ (or more) variables and constraints. A key bottleneck is the lack of benchmarks that align natural-lang
The rapid advancements in large language models (LLMs) have made their application to optimization attractive, but evaluation methods have lagged, necessitating more robust benchmarks.
This development addresses a critical bottleneck in deploying AI for complex industrial optimization, which can unlock significant efficiencies and economic value across multiple sectors.
The availability of industrial-scale benchmarks will accelerate the development and validate the effectiveness of LLMs in solving real-world optimization problems, moving beyond theoretical or toy examples.
- · AI developers
- · Logistics companies
- · Manufacturing sector
- · Consulting firms
- · Manual optimization experts (in the long term)
- · Companies relying on outdated optimization solutions
Improved performance and broader adoption of AI-driven optimization solutions in industry.
Increased demand for specialized AI talent capable of developing and deploying these advanced optimization systems.
Significant productivity gains and cost reductions across industries leveraging large-scale AI optimization, leading to competitive shifts.
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Read at arXiv cs.LG