LLM4MIP says it closed 34 open optimisation problems — MIPLIB lists 29 as incumbents

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LLM4MIP says it closed 34 open optimisation problems — MIPLIB lists 29 as incumbents

A research site says an LLM workflow closed 34 open optimisation problems. The benchmark that would have to agree lists 29 entries from the same team — and none of them as proven optimal. The gap is the story.

An LLM-assisted workflow now claims 34 of 132 open MIPLIB instances "resolved" — but MIPLIB's own log carries 29 of those submissions, all as incumbent improvements, not proofs. The work is LLM4MIP, published as a research website on 21 September by Yicheng Huang and Wenzhi Gao as co-lead authors with Dongdong Ge, Madeleine Udell and Yinyu Ye — a group with real optimisation pedigree, not a weekend prompt. Their claim splits into 32 instances they say they solved to optimality and two they say they proved infeasible, drawn from a working set of 132 instances chosen out of 217 still open in MIPLIB, the standard public library for mixed-integer programming.

The independent check is more modest, and it is public. MIPLIB's news log records a release on 24 September with 51 better incumbents, two instances moved to "easy", three moved from optimal to hard, and one updated to infeasible. LLM4MIP's solutions appear in the best-known-solution records for 29 instances — 25 improved incumbents plus four instances given a first feasible solution for the first time. That is a genuine contribution to a library where progress is measured in single instances per release, and it is also not 34 resolved problems: an accepted incumbent is a better answer, not a proof of the best answer.

There is no preprint and no peer review — the authors say a technical report is forthcoming — and no third-party replication has surfaced. That matters less than it sounds, because the verifiable layer already exists: MIPLIB's maintainers at Zuse Institute Berlin ran their own feasibility checks before listing the entries, and independent records show no constraint or objective violations. What a reader should take from this is the shape of the result, not the count. Solvers plus a model reasoning about structure is an effective workflow; "LLMs are doing mathematics" is the version that will not survive review.


miHoYo's president says the Genshin Impact maker will be a heavyweight in Chinese large models within two to three years — and it has quietly started building a coding model. Liu Wei made the claim at a Shanghai Jiao Tong University recruiting talk on 23 September, telling students he is "very confident" and inviting them to hold him to it; the quote surfaced widely on 28 September as a 澎湃新闻 exclusive, though QbitAI and GameLook had it days earlier. The substance underneath is what makes it worth noting: miHoYo has run an AI unit called 逆熵 since 2018, filed a generative-AI service named Glossa with Shanghai's cyberspace authority in September 2024, and Liu has put a ceiling of RMB 100 billion over three years on the company's AI spending.


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What to watch: whether the LLM4MIP authors publish the technical report with the two optimality claims their site still carries, and whether MIPLIB upgrades any of those 29 instances from incumbent to solved.

Which number should a benchmark believe — the submitters' count or the maintainers' log? Tell us in the comments.

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