Incomplete natural-language briefs often omit facts that change the structure of an optimization model (objectives, constraints, business rules). This work reframes pre-formulation clarification as a focused recovery problem: identify formulation-critical gaps that alter model structure, recover them with bounded interaction, and decide when the specification is model-ready. The core insight is to separate persistent gap diagnosis from question-selection so the agent asks only what matters and stops when enough structure is recovered.
Key Findings
- OR-Clarify benchmark: provides paired public briefs and hidden, source-backed formulation-critical slots plus simulated-user responses, enabling controlled evaluation of slot recovery, stopping behavior, silent assumptions, and interaction cost. So what: gives a reproducible way to measure whether agents know when to ask and what to ask before modeling.
- InterOPT framework: two-stage design (Dynamic Gap Search to track unresolved gaps; Gap-Guided Action Search to decide questions vs. stop). So what: separating gap tracking from action selection improves targeted recovery and reduces unnecessary queries.
- Empirical results: InterOPT substantially improves exact slot recovery in choice-based (structured) interactions and is competitive in open/free-form settings; diagnostics highlight tradeoffs between question precision and interaction burden. So what: structured gap-guidance yields clear gains when options are available, while free-form gains are more mixed.
Who It's For & Tradeoffs
Great fit if you build LLM-driven tools that auto-formulate optimization models or assist OR practitioners: the benchmark and framework help evaluate and improve whether an assistant asks the right clarifying questions and avoids silent assumptions. Look elsewhere if your pipeline already enforces complete, machine-readable specifications (no need for clarification) or if realistic users cannot tolerate any interactive delay — this work assumes bounded interactive clarification and simulated-user responses rather than noisy human-in-the-loop variability.
Where It Fits
This paper sits at the intersection of operations research and LLM-based modeling: it targets pre-formulation reliability rather than solver performance or post-hoc debugging. It complements solver-in-the-loop modeling systems by front-loading requirement recovery and provides a standardized evaluation to compare clarification strategies.