Full-Stack Domain Enhancement for Combustion LLMs: Construction and Optimization
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915876184260608 |
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| author | Xiao, Quanjia Ouyang, Weimin Yang, Zonglin Wu, Tianhao Zhou, Qingguo Mao, Runze Chen, Zhi X. |
| author_facet | Xiao, Quanjia Ouyang, Weimin Yang, Zonglin Wu, Tianhao Zhou, Qingguo Mao, Runze Chen, Zhi X. |
| contents | Large language models (LLMs) in the direction of task adaptation and capability enhancement for professional fields demonstrate significant application potential. Nevertheless, for complex physical systems such as combustion science, general-purpose LLMs often generate severe hallucinations due to insufficient domain knowledge and the inability to adhere to physical conservation laws. To address this issue, we propose the first full-stack domain-enhanced LLM workflow tailored for the field of combustion science, which integrates automated domain corpus construction, incremental pre-training, instruction fine-tuning, and verifiable reward-based reinforcement learning. This workflow ensures that the model truly internalizes physical laws rather than merely learning textual statistical patterns. We also release FlameBench, a standardized evaluation benchmark specifically designed for complex reasoning tasks in combustion science. Experimental results demonstrate that the model developed in this work significantly outperforms state-of-the-art general-purpose closed-source models and traditional retrieval-augmented generation methods on combustion science reasoning tasks. This work lays a solid technical and resource foundation for the subsequent development of domain-specific scientific research agents with reliable scientific reasoning capabilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_19268 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Full-Stack Domain Enhancement for Combustion LLMs: Construction and Optimization Xiao, Quanjia Ouyang, Weimin Yang, Zonglin Wu, Tianhao Zhou, Qingguo Mao, Runze Chen, Zhi X. Computation and Language Artificial Intelligence Large language models (LLMs) in the direction of task adaptation and capability enhancement for professional fields demonstrate significant application potential. Nevertheless, for complex physical systems such as combustion science, general-purpose LLMs often generate severe hallucinations due to insufficient domain knowledge and the inability to adhere to physical conservation laws. To address this issue, we propose the first full-stack domain-enhanced LLM workflow tailored for the field of combustion science, which integrates automated domain corpus construction, incremental pre-training, instruction fine-tuning, and verifiable reward-based reinforcement learning. This workflow ensures that the model truly internalizes physical laws rather than merely learning textual statistical patterns. We also release FlameBench, a standardized evaluation benchmark specifically designed for complex reasoning tasks in combustion science. Experimental results demonstrate that the model developed in this work significantly outperforms state-of-the-art general-purpose closed-source models and traditional retrieval-augmented generation methods on combustion science reasoning tasks. This work lays a solid technical and resource foundation for the subsequent development of domain-specific scientific research agents with reliable scientific reasoning capabilities. |
| title | Full-Stack Domain Enhancement for Combustion LLMs: Construction and Optimization |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2603.19268 |