TL;DR
IR-Agent is a multi-agent framework that emulates expert IR spectral analysis, enhancing molecular structure elucidation accuracy and adaptability by integrating specialized reasoning agents.
Contribution
The paper introduces IR-Agent, a novel multi-agent system that mimics expert analysis procedures for IR spectra, improving flexibility and accuracy in structure elucidation.
Findings
IR-Agent outperforms baseline methods on experimental IR spectra.
The framework demonstrates strong adaptability to diverse chemical information.
Extensive experiments validate the effectiveness of the multi-agent approach.
Abstract
Spectral analysis provides crucial clues for the elucidation of unknown materials. Among various techniques, infrared spectroscopy (IR) plays an important role in laboratory settings due to its high accessibility and low cost. However, existing approaches often fail to reflect expert analytical processes and lack flexibility in incorporating diverse types of chemical knowledge, which is essential in real-world analytical scenarios. In this paper, we propose IR-Agent, a novel multi-agent framework for molecular structure elucidation from IR spectra. The framework is designed to emulate expert-driven IR analysis procedures and is inherently extensible. Each agent specializes in a specific aspect of IR interpretation, and their complementary roles enable integrated reasoning, thereby improving the overall accuracy of structure elucidation. Through extensive experiments, we demonstrate that…
Peer Reviews
Decision·ICLR 2026 Poster
The modeling section of this paper demonstrates a innovative and ingeniously engineered system. It successfully integrates the capabilities of LLMs with domain-specific tools, offering a novel solution pathway for scientific computing problems. Its most significant advantage lies in utilizing multi-agent collaboration to mitigate erroneous judgments that may arise from single-agent systems.
Data Limitations: With only 9,052 experimental data points, the dataset appears insufficient to adequately demonstrate model generalizability, particularly for large language models requiring substantial training data. Insufficient Baseline Comparisons: The baseline evaluation lacks comprehensive comparisons with contemporary large models (e.g., Llama3, Claude-3-opus) that have been successfully applied in spectral interpretation tasks. Furthermore, the study fails to compare against state-of-t
The reasoning strategy of the proposed **IR-Agent** is both reasonable and consistent with that of human experts. Experimental validation is comprehensive, providing evidence of its superiority and the soundness of its architecture.
The framework was evaluated on a real experimental dataset; however, the overall accuracy of structure inference remains low, even when additional chemical information is provided, **making it far from suitable for practical applications**.
The paper tackles an important and challenging problem: automated molecular structure elucidation from spectroscopy data. The proposed multi-agent architecture is conceptually interesting, providing a modular framework that could, in principle, improve interpretability and extensibility. The experiments are clearly reported, including dataset details, model parameters, and top-k metrics. The paper makes a valid attempt to align model design with expert workflows in chemistry.
Scientific limitations of the problem setup: IR spectroscopy alone cannot uniquely determine molecular connectivity or size. As a result, the reported top-k accuracies (<20\%) confirm the fundamental ambiguity of the inverse problem. Even with added information (atom types, scaffolds, carbon counts), improvements are marginal (~3\%), limiting the real-world utility of the method. Shallow “reasoning”: While the framework uses large language models (e.g., GPT-o-mini) to emulate reasoning, the qua
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