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SimpleEvol: An Agent-Loop Framework for LLM-Driven Automated Heuristic Design with Minimal Human Priors — arXiv:2609.37172

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.37172
License (as archived): CC0-1.0
Archived: 2026-10-07T16:23:35.403Z
SHA-256 of the archived text: 1271493cb9d5485b4f84a221052da3b141f8d9d07b8eecb8dbbfd1434824b7f7
Stable URL of this page: https://metraipolis.com/documento/dc_eb5fb03bee76

How to cite

SimpleEvol: An Agent-Loop Framework for LLM-Driven Automated Heuristic Design with Minimal Human Priors — arXiv:2609.37172 — arXiv (en), https://arxiv.org/abs/2609.37172 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_eb5fb03bee76 (Metraipolis archive dc_eb5fb03bee76, sha256 1271493cb9d5485b4f84a221052da3b141f8d9d07b8eecb8dbbfd1434824b7f7)

BibTeX

@misc{dceb5fb03bee76,
  title = {SimpleEvol: An Agent-Loop Framework for LLM-Driven Automated Heuristic Design with Minimal Human Priors — arXiv:2609.37172},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.37172},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_eb5fb03bee76; sha256 1271493cb9d5485b4f84a221052da3b141f8d9d07b8eecb8dbbfd1434824b7f7},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.37172. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Large language models (LLMs) have emerged as powerful tools for automated heuristic design (AHD), enabling iterative generation and refinement of heuristics. However, the dominant paradigm embeds LLMs as narrow, fixed components, such as crossover or mutation, within heavily hand-engineered evolutionary frameworks. We argue this misapprehends LLMs. It treats them as specialized tools rather than general reasoners, constrains them to low-level operations, and underutilizes their autonomy. Moreover, the extensive human priors in these frameworks violate the bitter lesson principle that general methods scaling with computation surpass hand-crafted solutions. This raises a key question: which AHD framework designs best convert stronger LLM capabilities into better heuristics? To address this, we propose metrics for LLM-driven AHD framework handcraftedness (AHI) and intelligence conversion efficiency (ICE). Evaluating ten LLMs across three challenging combinatorial optimization problems, we obtain a notable finding that frameworks with fewer human priors consistently yield higher ICE. Based on this finding, we propose SimpleEvol, an agent-loop framework for AHD which removes nearly all human priors and allows the LLM to operate autonomously. SimpleEvol consistently achieves the highest ICE, often by a large margin. Our results challenge the trend toward complex AHD pipelines and point to a lighter and more model-centric alternative, suggesting that reducing human priors is a more effective strategy to scale up with model intelligence. The source code is available at this https URL .

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/2609.37172 — 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 2026-10-07T15:54:53.597Z 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).