Are LLMs Ready for Neural-integrated Mechanistic Modeling? A Benchmark and Agentic Framework
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arXiv
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| Format: | Preprint |
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2026
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| author | Guan, Zihan Datta, Rituparna Hu, Mengxuan Liu, Shunshun Zhang, Aiying Balachandran, Prasanna Li, Sheng Vullikanti, Anil |
| author_facet | Guan, Zihan Datta, Rituparna Hu, Mengxuan Liu, Shunshun Zhang, Aiying Balachandran, Prasanna Li, Sheng Vullikanti, Anil |
| contents | Large language models (LLMs) have shown promise in constructing mechanistic models from data. However, existing evaluations largely focus on simplified settings and fail to capture the complexity of real-world scientific modeling. In practice, such modeling often involves neural-integrated formulations, where a mechanistic model component and a neural network component are jointly constructed, leading to a significantly more complex search space. Motivated by this gap, we introduce the Neural-Integrated Mechanistic Modeling (NIMM) benchmark, which evaluates LLM-generated neural-integrated mechanistic models across three scientific domains. Experiments on NIMM reveal that existing LLM-based approaches struggle to effectively explore this complex space, resulting in limited search stability and solution quality. To address this challenge, we propose NIMMGen, a tree-guided agentic framework that enables diversified exploration via branch-level search and improves solutions through atomic model refinement. Extensive experiments demonstrate that NIMMGen achieves state-of-the-art performance on NIMM, significantly improving search stability and solution quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_18008 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Are LLMs Ready for Neural-integrated Mechanistic Modeling? A Benchmark and Agentic Framework Guan, Zihan Datta, Rituparna Hu, Mengxuan Liu, Shunshun Zhang, Aiying Balachandran, Prasanna Li, Sheng Vullikanti, Anil Machine Learning Artificial Intelligence Computation and Language Large language models (LLMs) have shown promise in constructing mechanistic models from data. However, existing evaluations largely focus on simplified settings and fail to capture the complexity of real-world scientific modeling. In practice, such modeling often involves neural-integrated formulations, where a mechanistic model component and a neural network component are jointly constructed, leading to a significantly more complex search space. Motivated by this gap, we introduce the Neural-Integrated Mechanistic Modeling (NIMM) benchmark, which evaluates LLM-generated neural-integrated mechanistic models across three scientific domains. Experiments on NIMM reveal that existing LLM-based approaches struggle to effectively explore this complex space, resulting in limited search stability and solution quality. To address this challenge, we propose NIMMGen, a tree-guided agentic framework that enables diversified exploration via branch-level search and improves solutions through atomic model refinement. Extensive experiments demonstrate that NIMMGen achieves state-of-the-art performance on NIMM, significantly improving search stability and solution quality. |
| title | Are LLMs Ready for Neural-integrated Mechanistic Modeling? A Benchmark and Agentic Framework |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2602.18008 |