Models Can Model, But Can't Bind: Structured Grounding in Text-to-Optimization
Text-to-optimization requires two separable capabilities: modeling -- choosing the right optimization structure -- and binding -- grounding every coefficient, index, and parameter in the concrete problem data. We study this via Text2Opt-Bench, a scalable benchmark of solver-verified optimization problems spanning 12 categories, from textbook linear programs to stochastic and multi-objective formulations with up to thousands of variables. Across 10+ models, we find that accuracy collapses as instance data grows, even when the formulation itself is simple. We call this the effective binding limit. We study it with a family of techniques, BIND, that externalize numeric data to structured fil...
| Authors | Zhiqi Gao, Albert Ge, Alexander Berenbeim et al. |
|---|---|
| Primary category | cs.LG |
| Published | 20 May 2026 |
| Last updated at source | 15 July 2026 |
Models and datasets referencing this paper
- ZhiqiGao/Text2Opt-BenchDataset
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