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LLM Agents Perform Controlled Experiments Using Simulation Models — arXiv:2608.23622

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.23622
License (as archived): CC0-1.0
Archived: 2026-10-18T06:13:51.848Z
SHA-256 of the archived text: d45165721a0d59e3bdf3b2d090077afe255a8f4c1f9c4f799e066b7b2fa8b79e
Stable URL of this page: https://metraipolis.com/documento/dc_4e16ce437259

How to cite

LLM Agents Perform Controlled Experiments Using Simulation Models — arXiv:2608.23622 — arXiv (en), https://arxiv.org/abs/2608.23622 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4e16ce437259 (Metraipolis archive dc_4e16ce437259, sha256 d45165721a0d59e3bdf3b2d090077afe255a8f4c1f9c4f799e066b7b2fa8b79e)

BibTeX

@misc{dc4e16ce437259,
  title = {LLM Agents Perform Controlled Experiments Using Simulation Models — arXiv:2608.23622},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.23622},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4e16ce437259; sha256 d45165721a0d59e3bdf3b2d090077afe255a8f4c1f9c4f799e066b7b2fa8b79e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.23622. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Large language models (LLMs) have shown strong capabilities in reasoning, planning, and tool use, but many scientific and engineering tasks require more than plausible text and code generation. They require understanding how a system responds to intervention, which in practice depends on controlled experimentation. In this work, we propose a multi-agent framework that enables LLM agents to conduct controlled experiments with scientific simulation models for pharmaceutical process design. Given a user query and a baseline configuration, the system constructs a structured task representation, designs experiments, executes comparative simulation, interprets the resulting outcomes, and synthesizes evidence-based recommendations for process parameter optimization. By coupling language models with high-fidelity simulation models in an interactive agent framework, the proposed system supports reasoning through intervention, comparison, and observation. As a result, it produces more specific and actionable outputs than language-only reasoning. In an industrial application setting, this advantage is reflected in higher output specificity as well as improved user-rated correctness and helpfulness. Ablation studies and visualized case analyses further demonstrate the effectiveness and practical utility of simulation-integrated experimental reasoning.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-26 in cs.MA).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL); Multiagent Systems (cs.MA); Software Engineering (cs.SE).
Pagina del lavoro: https://arxiv.org/abs/2608.23622 — 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 2026-10-18T06:07:44.723Z dalla pagina https://arxiv.org/catchup/cs.MA/2026-08-26?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).