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OptiCom : A Unified Framework for State-Conditioned Composition in LLM-Driven Optimization — arXiv:2609.37221

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.37221
License (as archived): CC0-1.0
Archived: 2026-10-07T16:23:39.579Z
SHA-256 of the archived text: 2b5c3d49d1a4972e16b24e3575097e973052c07c3628941ea70dd1d5e49f5846
Stable URL of this page: https://metraipolis.com/documento/dc_5d3bbaf2eb53

How to cite

OptiCom : A Unified Framework for State-Conditioned Composition in LLM-Driven Optimization — arXiv:2609.37221 — arXiv (en), https://arxiv.org/abs/2609.37221 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_5d3bbaf2eb53 (Metraipolis archive dc_5d3bbaf2eb53, sha256 2b5c3d49d1a4972e16b24e3575097e973052c07c3628941ea70dd1d5e49f5846)

BibTeX

@misc{dc5d3bbaf2eb53,
  title = {OptiCom : A Unified Framework for State-Conditioned Composition in LLM-Driven Optimization — arXiv:2609.37221},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.37221},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_5d3bbaf2eb53; sha256 2b5c3d49d1a4972e16b24e3575097e973052c07c3628941ea70dd1d5e49f5846},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.37221. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Large language models (LLMs) are increasingly deployed to solve complex scientific and practical problems via iterative optimization. However, dynamically coordinating diverse search mechanisms as candidate quality, failure modes, and resource budgets evolve remains a critical open challenge. Targeted empirical diagnostics reveal that mechanism effectiveness is highly state-dependent. Motivated by this, we analyze how individual decisions drive final outcomes, decomposing the expected terminal improvement under a shared budget into cumulative decision opportunities minus cumulative selection losses. Guided by this opportunity-loss theoretical foundation, we propose OptiCom, a unified framework that represents LLM-driven optimizers within a shared configuration space: C=(A,Q,O,E,M,S), corresponding to artifact, query, operator, evaluation, memory, and strategy. Operating within this space, a fast LLM-based Optimization Controller dynamically composes immediate mechanisms through structured Action Packages, while a slower Strategy Adapter refines long-term selection preferences, operator weights, and templates based on accumulated trajectory feedback. Comprehensive evaluations across 32 benchmark groups demonstrate the superiority of framework: OptiCom achieves an average Max-score rank of 1.72 among 14 evaluated configurations, securing the top score in 23 groups. Ultimately, these results highlight the broad applicability and high extensibility of OptiCom as a general-purpose paradigm for robust LLM test-time scaling.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-30 in cs.AI).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.37221 — 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 2026-10-07T15:54:53.604Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-30?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).