ModelEquivBench: Certifying Multi-Relational Evaluation of LLM-Generated Optimization Models
Penglin Zhu, Jungang Xu
Why It Matters
What makes this one worth your time
This work addresses the limitations of existing evaluation methods by providing a more granular and certifiable approach, which is crucial for improving the reliability of LLM-generated models in practical applications.
ModelEquivBench offers a nuanced evaluation framework for LLM-generated optimization models.
Summary
The paper introduces ModelEquivBench, a multi-relational evaluation system for assessing optimization models generated by large language models, providing detailed semantic profiles and independently verifiable evidence for various equivalence relations.
Key contributions
- Development of a semantic profile system (E0-E6) for evaluating optimization models.
- Provision of independently verifiable evidence for each equivalence relation.
- Identification of distinct failure modes across different LLM snapshots.
Notable insights
- The introduction of a multi-relational evaluation system allows for a more comprehensive understanding of model equivalence beyond binary classifications.
- The use of independently checkable evidence for each equivalence relation enhances the credibility of the evaluation process.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2607.29431v1 Announce Type: new Abstract: Large language models increasingly generate optimization models from natural language, but existing evaluation often reduces a generated model and its ground truth to a single equivalent/not-equivalent verdict or an execution-success rate--labels that are neither independently checkable nor faithful to the multiple distinct senses in which two formulations can agree. We present ModelEquivBench, a certifying, multi-relational evaluation system that reports a per-pair semantic profile E0--E6: model construction and exact ingestion (E0), verified representation alignment (E1), same-space and projected feasible-set relations (E2, E3), objective-order equivalence (E4), optimal-value equality (E5), and optimizer-set equivalence (E6). Each decided entry carries relation-appropriate, independently re-checkable evidence: replayable traces or explicit maps for E0--E1, exact-rational certificates for positive E2--E6 conclusions, and explicit witnesses for supported negatives. Incomplete mapping search, unsupported structure, and resource limits produce typed UNKNOWN or N/A outcomes rather than guesses, while unmet prerequisites are reported as ABSENT. Using ModelEquivBench to evaluate three model snapshots--GPT-5.4, Claude Sonnet 4.6, and Qwen3.5-397B-A17B--on the same frozen cohort of 173 base problems (346 cells per model) under a no-repair protocol, the resulting profiles expose distinctions that coarse baselines do not represent: 49, 35, and 25 cells contain executable candidates that are nevertheless certified negative on at least one supported relation, and 25, 8, and 18 structural rejections occur on pairs for which E2 certifies mapped feasible-set equality under a verified map. The three model snapshots fail at different stages of the profile and therefore cannot be meaningfully reduced to a single accuracy score.