FLARE: Verifying MILP Reformulations with LLM-Based Theorem Proving
AbstractMixed-Integer Linear Programming (MILP) is a fundamental tool for combinatorial optimization with extensive real-world applications. A central challenge is designing efficient MILP formulations. Large Language Models (LLMs) offer new opportunities to automate the modeling process, from deriving formulations to strengthening them.
- ▪AbstractMixed-Integer Linear Programming (MILP) is a fundamental tool for combinatorial optimization with extensive real-world applications.
- ▪A central challenge is designing efficient MILP formulations.
- ▪Large Language Models (LLMs) offer new opportunities to automate the modeling process, from deriving formulations to strengthening them.
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| Original publisher | Henryrobbins |
| Canonical URL | https://flare.henryrobbins.com/ |
| Publication time | Sun, 13 Sep 2026 17:34:53 +0000 |
| Retrieval time | 2026-09-13T17:46:50.681Z |
| Last seen | 2026-09-13T17:46:50.681Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | QcgVd0sXx2-- · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
AbstractMixed-Integer Linear Programming (MILP) is a fundamental tool for combinatorial optimization with extensive real-world applications. A central challenge is designing efficient MILP formulations. Large Language Models (LLMs) offer new opportunities to automate the modeling process, from deriving formulations to strengthening them. To ensure correctness, we need robust methods to compare formulations. However, existing approaches evaluate formulations numerically and fail to reason about general problem instances. We resolve this limitation by introducing a constructive notion of MILP reformulation that can be formalized in Lean and machine-checked.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Henryrobbins.
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