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DASyR-LLM: Domain-Aware Symbolic Regression with LLMs for Kinetic Model Discovery — arXiv:2608.05120

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.05120
License (as archived): CC0-1.0
Archived: 2026-10-31T11:35:34.556Z
SHA-256 of the archived text: 872d1723428d06675534e6a02ecc483a04de05634a5d49d59fa71a6b40e05696
Stable URL of this page: https://metraipolis.com/documento/dc_23c0c021dd4a

How to cite

DASyR-LLM: Domain-Aware Symbolic Regression with LLMs for Kinetic Model Discovery — arXiv:2608.05120 — arXiv (en), https://arxiv.org/abs/2608.05120 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_23c0c021dd4a (Metraipolis archive dc_23c0c021dd4a, sha256 872d1723428d06675534e6a02ecc483a04de05634a5d49d59fa71a6b40e05696)

BibTeX

@misc{dc23c0c021dd4a,
  title = {DASyR-LLM: Domain-Aware Symbolic Regression with LLMs for Kinetic Model Discovery — arXiv:2608.05120},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.05120},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_23c0c021dd4a; sha256 872d1723428d06675534e6a02ecc483a04de05634a5d49d59fa71a6b40e05696},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.05120. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Kinetic model discovery is a central challenge in chemical engineering, as accurate rate expressions are essential for understanding and controlling chemical and biological processes. Symbolic regression (SR) has emerged as a powerful data-driven approach for identifying interpretable kinetic models, but usually operates without domain knowledge, often exploring physicochemically implausible models. Large language models (LLMs) offer a promising avenue for injecting domain expertise into this search. Here, we introduce an LLM-guided SR framework, embedding an LLM module within an iterative SR algorithm for automated kinetic model discovery. The LLM performs two roles at each iteration: (1) a qualitative physicochemical critique of the best SR candidates, and (2) the proposal of new candidate rate expressions guided by the SR-generated models and embedded chemical knowledge. Our framework is evaluated on four in silico case studies of increasing complexity, spanning heterogeneous catalysis and bioprocess systems. Results show the LLM-guided framework reduces iterations to identify the ground-truth model by $41.7-79.3\%$ versus a state-of-the-art SR framework, with the LLM directly proposing the correct model structure in over half of the guided runs. In practical settings, where each iteration typically requires a new wet-lab experiment, this translates into a substantial reduction in experimental effort. Predictive performance on an independent validation set is equivalent between both approaches, with $R^2>0.98$ in all case studies. Ablation studies indicate that both the SR component and the LLM scale contribute to this performance, with a reduced-size LLM largely retaining discovery efficiency. These findings demonstrate that LLMs can effectively inject domain knowledge into scientific model discovery, paving the way toward fully automated, domain-aware kinetic modelling pipelines.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-06 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG) ; Computational Engineering, Finance, and Science (cs.CE); Symbolic Computation (cs.SC).
Pagina del lavoro: https://arxiv.org/abs/2608.05120 — 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 2026-10-31T11:24:48.812Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-08-06?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).