IR2Solve: New AI Pipeline for Cost-Efficient Optimization Autoformulation
A recent study presents IR2Solve, a pipeline that prioritizes intermediate representation to convert natural-language optimization challenges into formats suitable for solvers. This method employs a single call to a semantic LLM to generate a ModelIR constrained by a schema, followed by two deterministic phases: verification and compilation from IR to solver. ModelIR clearly delineates sets, parameters, variables, objectives, and constraints through restricted Python-like expression strings. A specific scalar-constraint convention treats finite per-index constraint families as distinct entries, minimizing errors related to free indices and implicit quantification, thereby streamlining verification and compilation. The paper, identified as 2608.02641, is accessible on arXiv and highlights issues with direct code generation in LLMs, which can lead to compilation failures and increased inference costs.
Key facts
- IR2Solve is an intermediate-representation-first autoformulation pipeline.
- It uses a single semantic LLM call to produce a schema-constrained ModelIR.
- The pipeline includes two deterministic stages: verification and IR-to-solver compilation.
- ModelIR explicitly represents sets, parameters, variables, objectives, and constraints using restricted Python-like expression strings.
- A concrete scalar-constraint convention represents finite per-index constraint families as individual entries.
- The approach reduces free-index and implicit-quantification errors.
- The paper is available on arXiv with identifier 2608.02641.
- The paper was announced as a cross-type submission.
Entities
Institutions
- arXiv