SOURCE-LINKED INTELLIGENCE
FLARE: Verifying MILP Reformulations with LLM-Based Theorem Proving
Mixed-Integer Linear Programming (MILP) is a fundamental tool for combinatorial optimization with extensive real-world applications. A central challenge is designing computationally efficient MILP formulations. Large Language Models (LLMs) offer new opportunities to automate the modeling process, from deriving formulations to strengthening them. Reliable automation requires robust methods for verifying that proposed formulations preserve the underlying optimization problem. However, existing approaches evaluate formulations numerically and fail to reason about general problem instances. We res
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-25T23:19:41.000Z
First collected: 2026-09-21T09:42:05.193Z. This is not the publication date.