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Structure-Aware Skill Retrieval and Arbitration for Optimization Modeling — arXiv:2610.41909

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41909
License (as archived): CC0-1.0
Archived: 2026-11-02T07:19:14.790Z
SHA-256 of the archived text: 9e24327bbd569e54fdcbefbdc957c029db664ac52b92a05633d675738a344f28
Stable URL of this page: https://metraipolis.com/documento/dc_bf8a63d3f360

How to cite

Structure-Aware Skill Retrieval and Arbitration for Optimization Modeling — arXiv:2610.41909 — arXiv (en), https://arxiv.org/abs/2610.41909 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bf8a63d3f360 (Metraipolis archive dc_bf8a63d3f360, sha256 9e24327bbd569e54fdcbefbdc957c029db664ac52b92a05633d675738a344f28)

BibTeX

@misc{dcbf8a63d3f360,
  title = {Structure-Aware Skill Retrieval and Arbitration for Optimization Modeling — arXiv:2610.41909},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41909},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bf8a63d3f360; sha256 9e24327bbd569e54fdcbefbdc957c029db664ac52b92a05633d675738a344f28},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41909. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Large Language Models (LLMs) can turn natural-language optimization tasks into solver-checked answers, but current agents remain brittle under narrative rewrites and often store reusable experience at the instance level. We introduce \textsc{ArchOR}, an archetype-centric skill agent that builds on \textsc{OptSkills} by adding two retrieval-focused modules. \emph{Centroid-Aware Representation Learning} (CARL) refines the ordering of cached skill representations through a low-rank prototype residual. \emph{Signature-Guided Retrieval} (S\textsuperscript{3}R) attaches open-dimensional structural signatures to skills, prioritizes candidates by weighted Jaccard overlap, and bounds semantic re-ranking to a top-$K$ set. A baseline-preserving arbitration rule determines when the structural route should replace the original selector. With the same DeepSeek-V4.1-Flash backbone and released skill library, \textsc{ArchOR} reaches 65.00\% Micro-Avg.\ Pass@1 across five benchmarks, improving over \textsc{OptSkills} by 1.64~pp Micro-Avg.\ and 2.68~pp Macro-Avg.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.41909 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-02T07:16:57.200Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).