← 研踪 PaperTrailarXiv:2610.29391 · v2
Nominate-then-Adjudicate: LLM-Assisted One-Pass and Per-Instance MIP Solver Tuning
首次提交 2026-10-21 16:19
版本更新 2026-11-09 11:33
雷达观测 2026-11-10 13:18
摘要
Parameter configurations control presolving, cut generation and branching strategies in mixed-integer programming (MIP) solvers, directly affecting solving efficiency. Effective configurations vary across instances, while searching for them online requires additional solver runs. We propose Nominate-then-Adjudicate: an empirical performance model (EPM) shortlists candidates from a fixed portfolio, and one large language model (LLM) call selects using static features, compressed formulation text and memory distilled from offline solver measurements. Text exposes distinctions omitted by the chosen aggregate features; selection requires no online candidate solves or foundation-model weight updates. Across sixteen synthetic families, adjudication improves on the EPM's top-ranked candidate (top-1), raising mean normalized performance score from 0.377 to 0.426 and the win rate against SCIP's default from 84.9% to 90.2%; all ten sequential batches show positive mean-score gains. On 400 Ecole instances, it outperforms the per-instance baseline ISAC+ in mean runtime-based score, with gains concentrated in facility location. Total solver time falls by 69.6% versus default, excluding LLM latency. Ablations support text and memory; a second LLM improves over top-1.The code is available at [this URL](https://github.com/cyberaz/nominate-then-adjudicate)
作者备注：10 pages, 7 figures, 6 tables. Accepted as a short paper at IEEE BigData 2026
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v3 · 2026-11-10 16:58 · 最新观测版本
Nominate-then-Adjudicate: LLM-Assisted One-Pass and Per-Instance MIP Solver Tuning
Parameter configurations control presolving, cut generation and branching strategies in mixed-integer programming (MIP) solvers, directly affecting solving efficiency. Effective configurations vary across instances, while searching for them online requires additional solver runs. We propose Nominate-then-Adjudicate: an empirical performance model (EPM) shortlists candidates from a fixed portfolio, and one large language model (LLM) call selects using static features, compressed formulation text and memory distilled from offline solver measurements. Text exposes distinctions omitted by the chosen aggregate features; selection requires no online candidate solves or foundation-model weight updates. Across sixteen synthetic families, adjudication improves on the EPM's top-ranked candidate (top-1), raising mean normalized performance score from 0.377 to 0.426 and the win rate against SCIP's default from 84.9% to 90.2%; all ten sequential batches show positive mean-score gains. On 400 Ecole instances, it outperforms the per-instance baseline ISAC+ in mean runtime-based score, with gains concentrated in facility location. Total solver time falls by 69.6% versus default, excluding LLM latency. Ablations support text and memory; a second LLM improves over top-1.
查看此版本解读 →查看此版本原文 ↗v2 · 2026-11-09 11:33
Nominate-then-Adjudicate: LLM-Assisted One-Pass and Per-Instance MIP Solver Tuning
Parameter configurations control presolving, cut generation and branching strategies in mixed-integer programming (MIP) solvers, directly affecting solving efficiency. Effective configurations vary across instances, while searching for them online requires additional solver runs. We propose Nominate-then-Adjudicate: an empirical performance model (EPM) shortlists candidates from a fixed portfolio, and one large language model (LLM) call selects using static features, compressed formulation text and memory distilled from offline solver measurements. Text exposes distinctions omitted by the chosen aggregate features; selection requires no online candidate solves or foundation-model weight updates. Across sixteen synthetic families, adjudication improves on the EPM's top-ranked candidate (top-1), raising mean normalized performance score from 0.377 to 0.426 and the win rate against SCIP's default from 84.9% to 90.2%; all ten sequential batches show positive mean-score gains. On 400 Ecole instances, it outperforms the per-instance baseline ISAC+ in mean runtime-based score, with gains concentrated in facility location. Total solver time falls by 69.6% versus default, excluding LLM latency. Ablations support text and memory; a second LLM improves over top-1.The code is available at [this URL](https://github.com/cyberaz/nominate-then-adjudicate)
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