An algorithm generates a portfolio of LLM-produced optimization models with guarantees that high-quality candidates are included if either the generator or evaluator aligns with human preferences.
arXiv preprint arXiv:2508.10047 , year=
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LLMs hit an effective binding limit in text-to-optimization as data complexity grows, but externalizing numeric data via BIND and binding-focused finetuning raises accuracy from 59% to 82%+ and lets small specialists match larger end-to-end models.
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.
Agora-Opt uses decentralized debate among LLM agent teams plus a read-write memory bank to produce more accurate optimization models from text than prior LLM methods.
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.
citing papers explorer
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Generating Robust Portfolios of Optimization Models using Large Language Models
An algorithm generates a portfolio of LLM-produced optimization models with guarantees that high-quality candidates are included if either the generator or evaluator aligns with human preferences.
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Models Can Model, But Can't Bind: Structured Grounding in Text-to-Optimization
LLMs hit an effective binding limit in text-to-optimization as data complexity grows, but externalizing numeric data via BIND and binding-focused finetuning raises accuracy from 59% to 82%+ and lets small specialists match larger end-to-end models.
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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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From Soliloquy to Agora: Memory-Enhanced LLM Agents with Decentralized Debate for Optimization Modeling
Agora-Opt uses decentralized debate among LLM agent teams plus a read-write memory bank to produce more accurate optimization models from text than prior LLM methods.
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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.