An LLM Driven Agent Framework for Automated Infrared Spectral Multi Task Reasoning

• URL: http://arxiv.org/abs/2507.21471v1
• Date: Tue, 29 Jul 2025 03:20:51 GMT
• Title: An LLM Driven Agent Framework for Automated Infrared Spectral Multi Task Reasoning
• Authors: Zujie Xie, Zixuan Chen, Jiheng Liang, Xiangyang Yu, Ziru Yu,
• Abstract summary: Large language models (LLMs) offer promising potential for complex scientific reasoning.<n>This study addresses the challenge of achieving accurate, automated infrared spectral interpretation under low-data conditions.<n>We introduce an end-to-end, large language model driven agent framework that integrates a structured literature knowledge base, automated spectral preprocessing, and multi task reasoning.
• Score: 4.934622388454071
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Infrared spectroscopy offers rapid, non destructive measurement of chemical and material properties but suffers from high dimensional, overlapping spectral bands that challenge conventional chemometric approaches. Emerging large language models (LLMs), with their capacity for generalization and reasoning, offer promising potential for automating complex scientific workflows. Despite this promise, their application in IR spectral analysis remains largely unexplored. This study addresses the critical challenge of achieving accurate, automated infrared spectral interpretation under low-data conditions using an LLM-driven framework. We introduce an end-to-end, large language model driven agent framework that integrates a structured literature knowledge base, automated spectral preprocessing, feature extraction, and multi task reasoning in a unified pipeline. By querying a curated corpus of peer reviewed IR publications, the agent selects scientifically validated routines. The selected methods transform each spectrum into low dimensional feature sets, which are fed into few shot prompt templates for classification, regression, and anomaly detection. A closed loop, multi turn protocol iteratively appends mispredicted samples to the prompt, enabling dynamic refinement of predictions. Across diverse materials: stamp pad ink, Chinese medicine, Pu'er tea, Citri Reticulatae Pericarpium and waste water COD datasets, the multi turn LLM consistently outperforms single turn inference, rivaling or exceeding machine learning and deep learning models under low data regimes.

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