# Full-Stack Domain Enhancement for Combustion LLMs: Construction and Optimization

arXiv: https://arxiv.org/abs/2603.19268 · HTML: https://mlsanity.com/paper/2603.19268/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 29 references that are in the map.
- First posted on arXiv 27 Feb 2026.

## Builds on (most cited first, 20 of 20)

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- [Hypothesis Generation for Materials Discovery and Design Using Goal-Driven and Constraint-Guided LLM Agents](https://mlsanity.com/paper/2501.13299/) (2025)
- [Structured Chemistry Reasoning with Large Language Models](https://mlsanity.com/paper/2311.09656/) (2023)
- [Benchmarking Retrieval-Augmented Generation for Chemistry](https://mlsanity.com/paper/2505.07671/) (2025)
- [A Reliable Knowledge Processing Framework for Combustion Science using Foundation Models](https://mlsanity.com/paper/2401.00544/) (2023)
- [Guaranteeing Conservation Laws with Projection in Physics-Informed Neural Networks](https://mlsanity.com/paper/2410.17445/) (2024)

## Abstract (arXiv)

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.