# IR-Agent: Expert-Inspired LLM Agents for Structure Elucidation from Infrared Spectra

arXiv: https://arxiv.org/abs/2508.16112 · HTML: https://mlsanity.com/paper/2508.16112/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 45 references that are in the map.
- First posted on arXiv 22 Aug 2025.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Translation between Molecules and Natural Language](https://mlsanity.com/paper/2204.11817/) (2022)
- [Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A ChatGPT Perspective](https://mlsanity.com/paper/2306.06615/) (2023)
- [Corex: Pushing the Boundaries of Complex Reasoning through Multi-Model Collaboration](https://mlsanity.com/paper/2310.00280/) (2023)
- [BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments](https://mlsanity.com/paper/2405.17631/) (2024)
- [HoneyComb: A Flexible LLM-Based Agent System for Materials Science](https://mlsanity.com/paper/2409.00135/) (2024)
- [DrugAgent: Automating AI-aided Drug Discovery Programming through LLM Multi-Agent Collaboration](https://mlsanity.com/paper/2411.15692/) (2024)
- [CoMM: Collaborative Multi-Agent, Multi-Reasoning-Path Prompting for Complex Problem Solving](https://mlsanity.com/paper/2404.17729/) (2024)
- [Unraveling Molecular Structure: A Multimodal Spectroscopic Dataset for Chemistry](https://mlsanity.com/paper/2407.17492/) (2024)
- [Conditional Graph Information Bottleneck for Molecular Relational Learning](https://mlsanity.com/paper/2305.01520/) (2023)
- [RAG-Enhanced Collaborative LLM Agents for Drug Discovery](https://mlsanity.com/paper/2502.17506/) (2025)
- [Shift-Robust Molecular Relational Learning with Causal Substructure](https://mlsanity.com/paper/2305.18451/) (2023)
- [Fragmentation trees reloaded](https://mlsanity.com/paper/1412.1929/) (2014)
- [Density of States Prediction of Crystalline Materials via Prompt-guided Multi-Modal Transformer](https://mlsanity.com/paper/2311.12856/) (2023)
- [Vision Language Model is NOT All You Need: Augmentation Strategies for Molecule Language Models](https://mlsanity.com/paper/2407.09043/) (2024)

## Built on by (most cited first, 5 of 5)

- [S1-NexusAgent: a Self-Evolving Agent Framework for Multidisciplinary Scientific Research](https://mlsanity.com/paper/2602.01550/) (2026)
- [NMR Elucidation as an Agentic Search Problem, Not a Modeling Problem](https://mlsanity.com/paper/2607.19406/) (2026)
- [Data Fusion and Contrastive Alignment for Unconstrained IR Molecular Structure Elucidation](https://mlsanity.com/paper/2607.26164/) (2026)
- [S3C-LLM: Skill-Code Guided Agentic Language Models for Spectrum-to-Structure Elucidation](https://mlsanity.com/paper/2608.30910/) (2026)
- [MolP2SBench: Do LLMs Really Reason from Molecular Properties to Structure?](https://mlsanity.com/paper/2610.12522/) (2026)

## Abstract (arXiv)

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 IR-Agent not only improves baseline performance on experimental IR spectra but also shows strong adaptability to various forms of chemical information.