SAILOR: Solver-Assisted Interactive LLM-based Optimization Recovery
Natural-language descriptions of optimization problems may be incomplete or vague about numerical information that a solver requires, including costs, capacities, demands, bounds, and penalties. A language model can translate the description into code, but when a required value is absent it must either stop or guess. We present SAILOR, a proof-of-concept system that detects such unsupported numerical choices, asks the user targeted follow-up questions, and updates the optimization model before returning a solution. Questions are prioritized using uncertainty and solver-derived estimates of how strongly each missing value affects the current model. We evaluate the pipeline on 1,723 instanc...
| Authors | Shaghayegh Sadeghi, Stephen L. Smith, David C. Del Rey Fern'andez |
|---|---|
| Primary category | cs.AI |
| Published | 12 September 2026 |
| Last updated at source | 12 September 2026 |
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