Towards a Unified Textual Graph Framework for Spectral Reasoning via Physical and Chemical Information Fusion

• URL: http://arxiv.org/abs/2506.17761v1
• Date: Sat, 21 Jun 2025 16:58:30 GMT
• Title: Towards a Unified Textual Graph Framework for Spectral Reasoning via Physical and Chemical Information Fusion
• Authors: Jiheng Liang, Ziru Yu, Zujie Xie, Yuchen Guo, Yulan Guo, Xiangyang Yu,
• Abstract summary: We propose a novel multi-modal spectral analysis framework that integrates prior knowledge graphs with Large Language Models.<n>Our method bridges physical spectral measurements and chemical structural semantics by representing them in a unified Textual Graph format.<n>Our framework achieves consistently high performance across multiple spectral analysis tasks, including node-level, edge-level, and graph-level classification.
• Score: 33.53441350137292
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Motivated by the limitations of current spectral analysis methods-such as reliance on single-modality data, limited generalizability, and poor interpretability-we propose a novel multi-modal spectral analysis framework that integrates prior knowledge graphs with Large Language Models. Our method explicitly bridges physical spectral measurements and chemical structural semantics by representing them in a unified Textual Graph format, enabling flexible, interpretable, and generalizable spectral understanding. Raw spectra are first transformed into TAGs, where nodes and edges are enriched with textual attributes describing both spectral properties and chemical context. These are then merged with relevant prior knowledge-including functional groups and molecular graphs-to form a Task Graph that incorporates "Prompt Nodes" supporting LLM-based contextual reasoning. A Graph Neural Network further processes this structure to complete downstream tasks. This unified design enables seamless multi-modal integration and automated feature decoding with minimal manual annotation. Our framework achieves consistently high performance across multiple spectral analysis tasks, including node-level, edge-level, and graph-level classification. It demonstrates robust generalization in both zero-shot and few-shot settings, highlighting its effectiveness in learning from limited data and supporting in-context reasoning. This work establishes a scalable and interpretable foundation for LLM-driven spectral analysis, unifying physical and chemical modalities for scientific applications.

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