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

2025-07-28

AI Summary

To address the low accuracy of automated infrared (IR) spectral interpretation under data-scarce conditions, this paper proposes the first large language model (LLM)-driven agent framework tailored for IR spectroscopy analysis. The framework integrates automated spectral preprocessing, literature-based knowledge retrieval, few-shot prompting, and a multi-round closed-loop reasoning mechanism to enable end-to-end multi-task analysis—including functional group identification, material classification, and compositional inference. Its key contributions are: (1) the first application of LLM-based agent systems to IR spectral analysis; (2) a dynamic feedback-enabled multi-round reasoning strategy that substantially improves few-shot generalization; and (3) cross-material applicability—demonstrated on diverse domains such as seal ink, traditional Chinese medicine, and Pu’er tea. Experiments show that, in low-data regimes, our method matches or surpasses conventional machine learning and deep learning baselines, with multi-round reasoning yielding significant performance gains over single-round inference.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Agent Communication
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Automating infrared spectral interpretation using LLMs
• Overcoming high-dimensional overlapping spectral bands challenge
• Enhancing accuracy in low-data conditions via multi-task reasoning

Innovation

• LLM-driven framework for spectral analysis
• Multi-task reasoning with few-shot prompts
• Closed-loop protocol for dynamic refinement