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

## Abstract

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 IR-Agent not only improves baseline performance on experimental IR spectra but also shows strong adaptability to various forms of chemical information.
The source code for IR-Agent is available at https://github.com/HeewoongNoh/IR-Agent .

## 1 Introduction

Spectral analysis provides critical clues for the elucidation of unknown materials Huang et al. (2021); Li & Kang (2020). In particular, spectroscopic techniques such as Infrared Spectroscopy (IR), Mass Spectrometry (MS), and Nuclear Magnetic Resonance Spectroscopy (NMR) are widely used for elucidating molecular structures Li & Kang (2020). For example, IR reveals details about chemical bonds and substructures Griffiths (2006), MS offers molecular weight and fragmented molecular structures Lee (1998), and NMR provides in-depth structural information, including stereochemistry Li & Kang (2020); Klein (2013). Although IR spectroscopy lacks comprehensive chemical information such as molecular weight, stoichiometry, and stereochemical details compared to MS and NMR, it is frequently utilized in the initial phase of analysis due to its low cost, speed, and high accessibility in laboratory settings Coates et al. (2000); Mistek & Lednev (2018).
Despite the ease and affordability of acquiring IR spectra, their interpretation remains a challenging and time-consuming task that requires extensive domain knowledge and expert experience Varmuza et al. (1999); Jung et al. (2023).

To automate IR spectra analysis, various machine learning (ML)-based approaches have been explored. Early ML approaches for IR spectra analysis primarily focus on identifying functional groups, achieving high predictive accuracy. Specifically, convolutional neural network (CNN) architectures are employed to classify functional groups from IR spectra Jung et al. (2023); Wang et al. (2023), and the M-order Markov property is utilized to construct IR spectrum graphs for tasks such as material class classification and functional group detection Na & Rho (2024).
While functional group classification enables rapid and simple characterization of compounds, it remains insufficient for tasks such as material discovery or identification of unknown material, thereby underscoring the necessity of complete molecular structure elucidation.

More specifically, molecular structure elucidation requires more comprehensive molecular information such as atomic composition, bonding information, and the connectivity of substructures, making it a substantially more complex task compared to functional group classificationXue et al. (2023).
As a result, only a few recent studies have explored early approaches, including predicting molecular structures as SMILES sequences by leveraging Transformer models with chemical formula information Alberts et al. (2024a); Wu et al. (2025),
and exploring reinforcement learning methods that integrate both IR and NMR spectra Devata et al. (2024).
Despite these early advances, these methods generally rely on fixed and predefined input formats, which restricts their flexibility in accommodating diverse types of chemical information.

However, in real-world analytical scenarios, IR spectra are often accompanied by various types of chemical information, necessitating approaches that are capable of integrating this additional information.
For instance, information such as atom types inferred from synthesis pathways, the number of carbon atoms, or scaffolds (i.e., the molecular skeleton structure) may be provided.
Therefore, previous methods pose a limitation in using real-world IR spectra analysis due to the limited flexibility, i.e., accommodating new types of chemical information typically requires redesigning and retraining the model Alberts et al. (2024a); Jung et al. (2023); Devata et al. (2024).
To overcome this limitation, a framework capable of flexibly accepting various types of input is required.

Recently, by representing chemical information in a human language, Large Language Models (LLMs) have been effectively utilized in the field of biochemistry Lee et al. (2024).
For instance, LLMs have been used to generate molecular structures in the string representation of molecules (i.e., SMILES) from text-based descriptions Edwards et al. (2022) and even modify molecular structures based on specified conditions Li et al. (2024); Liu et al. (2024b).
These successes highlight that an LLM-based framework is well-suited for building a more flexible and extensible IR spectrum-based structure elucidation system.

On the other hand, beyond the various types of chemical information, IR spectra analysis involves comprehensively integrating knowledge from diverse sources.
Specifically, during the process, experts interpret IR absorption tables to infer local substructures and bonding patterns from peak positions Socrates (2004); Larkin (2017), and retrieve structurally similar molecules from spectral databases to provide global contextual clues Moldoveanu & Rapson (1987).
Similar to expert workflows, a successful system should accurately perform each of these tasks—extracting critical information from multiple sources—and ultimately integrate the results in a coherent reasoning process to predict the molecular structure.
However, it is widely known that relying on a single LLM to perform all sub-tasks simultaneously can result in suboptimal information extraction and may be inadequate for handling complex reasoning tasks Chen et al. (2024); Sun et al. (2023).

To this end, we propose IR-Agent, a novel LLM-based multi-agent framework specifically designed to emulate expert analytical processes and seamlessly incorporate various types of knowledge into the structure elucidation workflow based on IR spectra.
Rather than relying on a single LLM to process all types of knowledge at once, our framework adopts modeling with specialized sub-agents tailored to each type of knowledge.
More specifically, we design a multi-agent framework composed of the following: (1) a Table Interpretation Expert that performs table-guided absorption analysis to extract local structural information from the target IR spectrum; (2) a Retriever Expert that identifies similar spectra from spectra databases to provide global contextual structural information; and (3) a Structure Elucidation Expert that produces the final structure prediction by integrating the analyses from both expert agents, each contributing complementary information for complete structure elucidation.
This integrative analysis within a multi-agent framework enables efficient molecular structure elucidation by extracting relevant information from each knowledge source and performing collaborative reasoning.
A further appeal of IR-Agent is its flexibility: when new knowledge becomes available, the system does not need a complete redesign or retraining.
Instead, it can be easily extended by incorporating the additional information through updated prompts to guide the agent’s reasoning process.
Our main contributions in this study are as follows:

- •
We introduce IR-Agent, a novel multi-agent framework for molecular structure elucidation from infrared (IR) spectra. This framework models expert-driven IR spectrum analysis processes and is designed to be highly extensible.
- •
While each agent specializes in a specific aspect of IR spectrum analysis, their complementary roles enable an integrated analysis process, thereby improving molecular structure elucidation from IR spectrum.
- •
Through extensive experimentation, we show that our proposed framework not only improves baseline performance on experimental IR spectra but also exhibits strong adaptability to diverse types of chemical information.

To the best of our knowledge, this is the first work to leverage the LLM agents framework for molecular structure elucidation from IR spectra.

## 2 Related Works

### 2.1 Machine Learning for IR Spectra Analysis

Recently, ML approaches have become game changers in the field of molecular science, where traditional research has heavily relied on theory, experimentation, and computer simulation, which is costly Lee et al. (2023a, c, b).
Among the approaches, analyzing IR spectra have demonstrated their capability to identify functional groups, extract structural features, and assist in molecular structure elucidation.
Inspired by CNN architectures specifically designed to capture local features, CNNs have been successfully applied to functional group classification directly from IR spectra Jung et al. (2023); Wang et al. (2023).
In parallel, GNNs have been employed to construct IR spectrum graphs based on the M-order Markov property, enabling tasks such as material classification and functional group detection. Beyond functional group identification, recent efforts have extended ML approaches to full molecular structure elucidation from IR spectra.
Among these, Transformer-based architectures are the most widely adopted, with prior work Alberts et al. (2024a) transforming downsampled IR spectra into text format for use with standard Transformer models, and other studies Wu et al. (2025) adopting patch-based self-attention mechanisms to better capture spectral patterns and local dependencies. Additionally, recent work explores reinforcement learning methods that leverage both IR and NMR spectra for structure elucidation Devata et al. (2024), and more recent studies examines the use of LLMs with multi-modal spectral inputs, such as IR, MS, and NMR spectra, to perform structure elucidation tasks Guo et al. (2024).
Unlike prior work, IR-Agent aims to incorporate expert analytical processes and adopts a multi-agent framework, which offers high architectural flexibility.

### 2.2 LLM Agents for Science

LLM agents have demonstrated strong capabilities across various scientific domains. For example, ChemCrowBran et al. (2023) employs an LLM agent to autonomously perform tasks typically conducted by chemists, using a range of external tools. Similarly, CoscientistBoiko et al. (2023) autonomously handles experimental design, planning, and execution of complex experiments by integrating internet search, code execution, and laboratory automation.
Moreover, LLM agents have been applied to diverse fields such as materials scienceZhang et al. (2024) and biomedical domain, including applications in drug discoveryInoue et al. (2024) and the design of biological experimentsRoohani et al. (2024). In addition, recent work has explored the use of multi-agent frameworks to effectively tackle drug discovery tasksLee et al. (2025); Liu et al. (2024a), highlighting the growing interest in collaborative LLM-based systems for complex scientific workflows. While LLM agents have not yet been applied to spectra-related tasks, their integration with external tools presents high potential for extensibility and effectiveness, as demonstrated in other scientific domains. This work aims to initiate exploration in this direction by positioning LLM agents as a viable solution for spectral analysis.

## 3 Preliminaries

### 3.1 Problem setup

Task Description.
Given an IR spectrum $\mathcal{X}\in\mathbb{R}^{1\times L}$ as input, where $L$ denotes the number of absorbance values corresponding to wavenumber positions (i.e., the length of the spectrum), IR-Agent predicts the raw SMILES representation of the corresponding molecule, a process known as molecular structure elucidation.
We primarily assume an analysis setting in which only the IR spectrum is available for an unknown material, as this reflects a more practical and common scenario in early-stage compound elucidation, where additional information is often limited or unavailable.
Since our framework is designed to flexibly accommodate scenarios where supplementary chemical information—such as atom types, the number of carbon atoms, or scaffold structures—is available, we also explore its applicability to more informed analytical settings (Section [4.4](https://arxiv.org/html/2508.16112v1#S4.SS4)).

Tools.
In this paper, we specifically design tools to support task-specific analysis as follows:

- •
IR Peak Table Assigner extracts the peaks from the spectrum and finds relevant substructures from the IR absorption table.
- •
IR Spectra Retriever retrieves IR spectra from the IR Spectra Database that are similar to a given input spectrum.

External Knowledge.
We also use external knowledge to support task-specific analysis as follows:

- •
IR Absorption Table summarizes the characteristic absorption frequencies associated with different molecular functional groups.
- •
The IR Spectra Database contains a variety of IR spectra along with their corresponding molecules in SMILES format.

Additional details on the tools and external knowledge are provided in the Appendix [A](https://arxiv.org/html/2508.16112v1#A1).

### 3.2 IR Spectra Translator

To begin with, we introduce IR Spectra Translator, a Transformer-based model Vaswani et al. (2017) that predicts a set of initial molecular structures in SMILES format from a target IR spectrum. In line with previous studies that employ Transformer architectures for IR spectra modeling Alberts et al. (2024a); Wu et al. (2025), we adopt this architecture for our IR Spectra Translator.
Specifically, given the target IR spectrum $\mathcal{X}$, we obtain a set of SMILES candidates $\mathcal{C}$ as follows:

$$ $\mathcal{C}=\left\{\mathbf{s}_{1},\dots,\mathbf{s}_{\mathbf{K}}\right\}=\mathrm{Transformer}(\mathcal{X}),$ (1) $$

where $\mathbf{K}$ denotes the number of SMILES candidates generated by the Translator using beam search decoding. Additional details on the IR Spectra Translator are provided in Appendix [A](https://arxiv.org/html/2508.16112v1#A1).

## 4 Proposed Method: IR-Agent

Figure: Figure 1: Overview of IR-Agent. (a) Overall framework. Given an unknown IR spectrum, IR-Agent first utilizes the IR Spectra Translator to generate candidate structures in SMILES format. The Table Interpretation (TI) Expert then extracts local structural information by referencing the IR absorption table through the IR Peak Table Assigner. In parallel, the Retriever (Ret) Expert obtains global structural features from similar spectra retrieved by the IR Spectra Retriever from a database. The Structure Elucidation (SE) Expert integrates the analyses from both experts to produce the final ranked list of predicted molecular structures. (b) Detailed view of the Table Interpretation (TI) Expert. (c) Detailed view of the Retriever (Ret) Expert.
Refer to caption: x1.png

In this section, we introduce IR-Agent, a multi-agent framework for molecular structure elucidation from IR spectra that mimics expert analytical processes through specialized expert agents. Our framework combines off-the-shelf LLMs with analytical tools to support an integrative analysis process.
It is composed of the following expert agents: (1) the Table Interpretation (TI) Expert, which employs IR absorption table to identify substructures (Section [4.1](https://arxiv.org/html/2508.16112v1#S4.SS1)); (2) the Retriever (Ret) Expert, which extracts structural information from retrieved spectra (Section [4.2](https://arxiv.org/html/2508.16112v1#S4.SS2)); and (3) the Structure Elucidation (SE) Expert, which provides a ranked list of SMILES based on the outputs from both the Retriever Experts and Table Interpretation Experts (Section [4.3](https://arxiv.org/html/2508.16112v1#S4.SS3)). The overall framework is presented in Figure [1](https://arxiv.org/html/2508.16112v1#S4.F1).

### 4.1 Table Interpretation (TI) Expert

The use of IR absorption tables is grounded in decades of experimental validation and theoretical development, offering reliable and interpretable structural insights.
Importantly, this approach captures fine-grained localized structural features, such as substitution patterns, cis/trans isomerism, and conjugation, which makes it a crucial component in structure elucidation.
However, effective utilization of these tables requires accurate identification of spectral peaks, which is challenging to an LLM agent because: (1) it may infer peak positions from spectral images, but only approximately and often without sufficient precision; and (2) it struggles to detect peaks directly from high-dimensional numerical absorbance data, which typically consists of thousands of values.

To address this limitation, the TI Expert agent employs the IR Peak Table Assigner tool, which extracts peaks from the spectrum by simply comparing the absorbance of neighboring wavenumbers.
Then, TI Expert agent assigns corresponding substructures to each peak based on its wavenumber range, by referring to the IR absorption table.
Specifically, given the task-specific prompt $\mathbf{P}_{\text{TI Expert}}$, the IR Absorption Table (T), and IR Peak Table Assigner, the agent is defined as follows:

$$ $\mathcal{A}_{\text{TI Expert}}=\text{TI Expert}\left(\mathbf{P}_{\text{TI Expert}},\text{IR Peak Table Assigner}(\mathcal{X},\mathbf{T}),\mathcal{C}\right),$ (2) $$

where $\mathcal{C}$ denotes the SMILES candidates generated by the IR spectra Translator.
$\mathcal{A}_{\text{TI Expert}}$ includes the potential substructures that can be included in the SMILES string.

Despite its utility, table-based interpretation has inherent limitations, as IR spectra often contain noise and multiple substructures may exhibit absorption within the same wavenumber region, leading to ambiguity in peak assignment.
To mitigate the possible misinterpretation, we design a prompt $\mathbf{P}_{\text{TI Expert}}$ that guides the agent to compare the output of the IR Peak Table Assigner with the SMILES candidates $\mathcal{C}$, identify shared substructures, and generate a confidence level along with a brief rationale for each identified substructure (e.g., substructure → confidence → brief rationale).
By doing so, the agent enhances the reliability of the table-based interpretation for target spectra. Details of IR Peak Table Assigner, IR absorption table, the textual prompt are provided in Appendix [E](https://arxiv.org/html/2508.16112v1#A5).

### 4.2 Retriever (Ret) Expert

Although the local structural information provided by the TI Expert is valuable, it is often insufficient to uniquely determine the complete molecular structure.
This limitation arises because IR spectra offer vibrational information localized to specific functional groups or substructures, rather than providing a direct mapping to the full molecular structure Coates et al. (2000); Griffiths (2006).
To overcome this limitation, we draw inspiration from the typical reasoning process of human experts, who frequently consult spectral databases to identify structurally similar reference compounds when analyzing unknown spectra Moldoveanu & Rapson (1987). Accordingly, we propose the Retriever (Ret) Expert agent, which leverages known molecular structures associated with similar IR spectra to provide global structural context, effectively linking local substructures to a more complete molecular structure.

Specifically, the Ret Expert agent utilizes the IR Spectra Retriever tool to identify spectra that are similar to the target IR spectrum. The IR Spectra Retriever adopts a simple yet effective approach: (1) it computes the cosine similarity between the target spectrum and all spectra in the database; (2) it then retrieves the top-$N$ most similar spectra, each associated with its corresponding SMILES structure.
The output of IR Spectra Retriever for the target spectrum($\mathcal{X}$) is defined as follows:

$$ $\left\{\text{candi}_{1}:\text{sim}_{1},\dots,\text{candi}_{N}:\text{sim}_{N}\right\}=\text{IR Spectra Retriever}(\mathcal{X}),$ (3) $$

where $\text{candi}_{i}$ denotes the SMILES corresponding to the $i$-the retrieved spectrum, and $\text{sim}_{i}$ denotes its cosine similarity to the the target spectrum.
Given task-specific prompt $\mathbf{P}_{\text{Ret Expert}}$ and the retrieval output, the agent is defined as follows:

$$ $\mathcal{A}_{\text{Ret Expert}}=\text{Ret Expert}\left(\mathbf{P}_{\text{Ret Expert}},\text{IR Spectra Retriever}(\mathcal{X})\right).$ (4) $$

The output of the Ret Expert, i.e., $\mathcal{A}_{\text{Ret Expert}}$, includes shared structural features among the top-$N$ retrieved SMILES.
Given the SMILES and the cosine similarity between the target spectrum and the retrieved spectrum, the Ret Expert automatically identifies common substructures while assigning higher weight to spectra with increased similarity.
These structural features provide global contextual clues that guide molecular structure reasoning.
The complete prompt is provided in Appendix [E](https://arxiv.org/html/2508.16112v1#A5).

### 4.3 Structure Elucidation (SE) Expert

Finally, the Structure Elucidation (SE) Expert conducts integrative structure reasoning based on both $\mathcal{A}_{\text{TI Expert}}$ and $\mathcal{A}_{\text{Ret Expert}}$, as follows:

$$ $\mathcal{A}_{\text{SE Expert}}=\text{SE Expert}\left(\mathbf{P}_{\text{SE Expert}},\mathcal{A}_{\text{TI Expert}},\mathcal{A}_{\text{Ret Expert}},\mathcal{C}\right),$ (5) $$

where $\mathcal{A}_{\text{SE Expert}}$ includes a final ranked list of the top-$K$ predicted molecular structures.
By utilizing the information provided by both agents, the SE expert agent is able to perform a comprehensive reasoning process that integrates both local and global molecular structures.
Moreover, structural features consistently identified by both agents can serve as reliable cues for the SE expert agent in molecular structure elucidation.

### 4.4 Incorporating Various Chemical Information into Agent Reasoning

In real-world analytical scenarios, IR spectra are often accompanied by various types of chemical information, necessitating approaches that are capable of integrating this additional information.
As the IR-Agent framework is based on LLM agents, it is not constrained by fixed input formats as in conventional ML approaches, and can flexibly incorporate various chemical information in textual form.
Specifically, rather than instantiating a separate agent for chemical information, we embed chemical information directly into the reasoning prompts of all the agents.
Moreover, to avoid the complexity of prompt engineering, we simply append a concise sentence containing the relevant chemical information to the original prompt.
This lightweight strategy reduces the cost of adding new agents and designing new prompts, while enabling each agent to perform its original task more effectively by leveraging chemical information during reasoning.
Therefore, IR-Agent is applicable not only in scenarios where only IR spectral data is available, but also in cases where additional chemical information is provided, thereby enhancing the flexibility and applicability of the framework without requiring additional training or architectural modifications.
We provide more details on the prompt that incorporates chemical information in the Appendix [E](https://arxiv.org/html/2508.16112v1#A5).

### 4.5 Discussion: Multi-Agent Framework for Structure Elucidation

Our IR-Agent employs a multi-agent framework in which each LLM agent is assigned a distinct sub-task within the overall structure elucidation process.
Rather than employing a single LLM to manage the entire reasoning pipeline, IR-Agent distributes the workload across specialized agents with each agent focusing on a specific type of analytical reasoning, and integrates their outputs to infer the final molecular structure.
Each sub-task poses unique reasoning challenges:
the TI Expert performs precise local pattern recognition and chemical knowledge grounding to interpret peak–substructure mappings;
the Ret Expert needs to reason over spectral similarity and extract structurally meaningful global patterns from retrieved candidates;
and the SE Expert is tasked with integrating these heterogeneous insights into a coherent molecular structure. When a single-agent model attempts to perform all these sub-tasks simultaneously, it often struggles to distinguish and prioritize relevant signals for each stage. For instance, local absorption features may be misinterpreted by global context, or retrieved candidates may not be properly utilized when misleading substructure signals are present. Additionally, the increased cognitive burden of handling diverse input information (e.g., tables interpretation by peak region, retrieved SMILES) within a single context window can result in incomplete reasoning, leading to degraded predictions. To evaluate the effectiveness of our multi-agent framework in addressing these issues, we conduct a comparative analysis against its single-agent counterpart, as presented in Section [5.2](https://arxiv.org/html/2508.16112v1#S5.SS2).

## 5 Experiments

### 5.1 Experimental Setup

Datasets.
In this study, we use a dataset of 9,052 experimental IR spectra from the NIST database, which has been widely adopted in prior IR spectra modeling studies Jung et al. (2023); Na (2024); Wang et al. (2023).
The use of experimental spectra is particularly important, as they reflect the noise, peak broadening, and variability inherent in real-world measurements, making them more representative of practical compound analysis scenarios.
These spectra also reflect the types of challenges typically encountered in laboratory settings, where structural elucidation requires interpreting imperfect signals through expert knowledge and heuristics.
Since our framework relies heavily on absorption table interpretation and human-like reasoning with retrieval-based search, experimental spectra provide a realistic and practical basis for evaluating its effectiveness in real-world analysis analytical workflows.
Following Na (2024), we apply polynomial interpolation over wavenumbers ranging from $500\text{ to }4000~\text{cm}^{-1}$ to obtain a structured format, addressing the inconsistent number of absorbance points across samples.
For spectra recorded in transmittance mode, intensity values are converted to absorbance using a standard conversion formula.

External Knowledge.
We use the IR absorption table available online(^1^11[https://chem.libretexts.org/Ancillary_Materials/Reference/Reference_Tables/Spectroscopic_Reference_Tables/Infrared_Spectroscopy_Absorption_Table](https://chem.libretexts.org/Ancillary_Materials/Reference/Reference_Tables/Spectroscopic_Reference_Tables/Infrared_Spectroscopy_Absorption_Table)) and employ the training set as an IR Spectra Database for retrieval.

Methods Compared.
To validate the effectiveness of IR-Agent, we compare it with the standalone Transformer model used as our IR Spectra Translator, showing that our framework provides additional gains in structure elucidation.
We further evaluate a single-agent variant of IR-Agent, where a single LLM agent simultaneously handles all sub-tasks.
To assess the impact of the underlying LLM, we vary the backbone model used in both the single-agent and multi-agent settings across GPT-4o-mini, GPT-4o, and o3-mini.
We also consider a setting where o3-mini is used to directly generate a ranked list of 10 SMILES structures, relying solely on the input candidate set $\mathcal{C}$.

Evaluation Protocol.
We randomly split the dataset into train/valid/test of 80/10/10%.
The IR Spectra Translator is trained on this split prior to applying IR-Agent.
We adopt Top-$K$ exact match accuracy as the evaluation metric to assess the effectiveness of the proposed method.
This metric checks whether the correct SMILES is included among the top $K$ generated candidates, comparing structures after conversion to the InChI representation Heller et al. (2015).
We report the average performance across three independent experiments.

### 5.2 Results of Structure Elucidation

Table: Table 1: Overall model performance for structure elucidation from IR spectra.

Effectiveness of IR-Agent.
As shown in Table [1](https://arxiv.org/html/2508.16112v1#S5.T1), we observe the following:
(1) Comprehensive reasoning based on the analyses from the TI Expert and the Ret Expert leads to more accurate molecular structure predictions.
Given the candidate set $\mathcal{C}\,(\mathbf{K}=3)$ generated by the IR Spectra Translator, IR-Agent achieves higher Top-$K$ accuracy compared to the standalone Transformer model, which functions as the IR Spectra Translator in our system.
This improvement is attributed to the complementary insights provided by both experts. Their collaborative analyses enable the Structure Elucidation (SE) Expert to refine the candidates and generate more accurate final structures.
(2) The multi-agent framework consistently outperforms the single-agent approach.
Compared to the single-agent version of IR-Agent, in which a single LLM handles all tasks simultaneously, the multi-agent version, where each expert agent is responsible for a specific sub-task, demonstrates more consistent and superior performance in the structure elucidation task.
This observation is further supported by the following finding: While using a more advanced LLM backbone generally leads to improved performance, the multi-agent version of IR-Agent (GPT-4o) achieves comparable or even superior accuracy compared to the single-agent system built on o3-mini, despite the former relying on a simpler model.
This trend is also observed when comparing the multi-agent system (GPT-4o-mini) with the single-agent version (GPT-4o).
This result underscores the effectiveness of the multi-agent framework in handling the molecular structure elucidation, demonstrating its strength in performing integrative analysis beyond what a single-agent can achieve.

**Table 2: Overall model performance with various chemical information.**
| Chemical Information |  | o3-mini |  | IR-Agent (single)<br>(o3-mini) |  | IR-Agent (multi)<br>(o3-mini) |  |  |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  | Top-1 | Top-3 | Top-5 | Top-10 |  | Top-1 | Top-3 | Top-5 | Top-10 |  | Top-1 | Top-3 | Top-5 | Top-10 |  |
| No Knowledge |  | 0.073 | 0.131 | 0.157 | 0.185 |  | 0.087 | 0.153 | 0.179 | 0.197 |  | 0.103 | 0.178 | 0.199 | 0.216 |
|  | (0.010) | (0.011) | (0.011) | (0.005) |  | (0.010) | (0.011) | (0.011) | (0.005) |  | (0.005) | (0.007) | (0.004) | (0.001) |  |
| Scaffold |  | 0.096 | 0.160 | 0.177 | 0.198 |  | 0.112 | 0.195 | 0.208 | 0.228 |  | 0.118 | 0.208 | 0.232 | 0.258 |
|  | (0.002) | (0.006) | (0.003) | (0.003) |  | (0.003) | (0.009) | (0.008) | (0.010) |  | (0.003) | (0.009) | (0.008) | (0.010) |  |
| Carbon Count |  | 0.105 | 0.158 | 0.186 | 0.214 |  | 0.121 | 0.177 | 0.194 | 0.219 |  | 0.123 | 0.190 | 0.215 | 0.252 |
|  | (0.009) | (0.014) | (0.014) | (0.013) |  | (0.003) | (0.008) | (0.010) | (0.009) |  | (0.003) | (0.0005) | (0.009) | (0.007) |  |
| Atom Types |  | 0.104 | 0.182 | 0.209 | 0.237 |  | 0.123 | 0.208 | 0.235 | 0.266 |  | 0.127 | 0.213 | 0.250 | 0.278 |
|  | (0.011 | (0.007) | (0.005) | (0.003) |  | (0.006) | (0.003) | (0.011) | (0.009) |  | (0.006) | (0.003) | (0.011) | (0.009) |  |

Structure Elucidation with Chemical Information. While our IR-Agent is primarily designed under the assumption that only the IR spectrum is available, in real-world analytical settings, it is common to incorporate additional chemical information to support structure elucidation Alberts et al. (2024b).
To reflect this practical scenario, we consider three types of chemical information: atom types, scaffold (i.e., molecular backbone), and carbon count.
In each case, the relevant chemical information is appended as a textual sentence to the prompt of the corresponding expert agent as described in Section [4.4](https://arxiv.org/html/2508.16112v1#S4.SS4).
From Table [2](https://arxiv.org/html/2508.16112v1#S5.T2), we make the following observations:
(1) Even a brief textual prompt containing chemical information can enhance the model’s ability to predict accurate molecular structures.
Without requiring any architecture modifications or retraining, IR-Agent  is able to successfully incorporate additional information into each expert’s reasoning process through prompt-based interaction, leveraging the inherent reasoning capabilities of LLMs.
(2) Among the various types of chemical information, we observe that incorporating Atom Types information enables the model to generate more accurate molecular structures compared to other types of chemical information. This is due to the inherent challenge of determining the exact set of constituent elements solely from an IR spectrum.
(3) However, incorporating any form of chemical information consistently improved performance over using only IR spectra (No Knowledge), highlighting the importance of a flexible framework capable of integrating various types of available chemical information. In conclusion, each expert in IR-Agent  plays a distinct and effective role in structure elucidation while flexibly integrating various forms of chemical information, demonstrating the potential extensibility of the multi-agent framework for spectral analysis tasks.

### 5.3 In-depth Analysis

Figure: Table 3: Ablation study of IR-Agent (o3-mini).

Ablation Studies.
To assess the contribution of each expert agent, we conduct ablation studies by selectively removing them from the system.
As shown in Table [5.3](https://arxiv.org/html/2508.16112v1#S5.SS3), relying solely on the IR Spectra Translator without any expert assistance (No Experts) results in a significant drop in performance. Furthermore, using only one expert (i.e., either the TI Expert only or Ret Expert only) underperforms compared to the case where both experts are employed. When only the TI Expert is used, the system struggles to capture global structural patterns, whereas the Ret Expert alone often fails to extract fine-grained local information from the IR Absorption Table. Nevertheless, the Ret Expert alone achieves slightly better performance than the TI Expert alone, as it can access a broader range of structural patterns by leveraging multiple retrieved SMILES candidates, resulting in richer overall structural information. These results highlight that utilizing both TI and Ret Experts is essential for providing complementary structural insights, enabling effective integrative analysis for structure elucidation.

Sensitivity Analysis: Number of SMILES candidates $\mathcal{C}$.
Moreover, we investigate how varying the number of SMILES candidates $\mathcal{C}$ affects performance.
As shown in Figure [2](https://arxiv.org/html/2508.16112v1#S5.F2) (a), the performance of IR-Agent  improves as $\mathcal{C}$ increase up to 3 or 5, but tends to decline beyond that point. This degradation may be attributed to the introduction of noisy candidates, which can hinder the experts’ reasoning. In particular, the TI Expert is required to manually align information from the IR absorption table with an increasing number of candidates,

Figure: Figure 2: In-depth Analysis results.
Refer to caption: x2.png

which raises the risk of incorporating irrelevant or misleading structural features.
These results suggest that selecting an appropriate number of SMILES candidates is crucial for effective expert reasoning.

Performance of IR-Agent using the Transferred IR Spectra Translator.
As our framework is compatible with various IR spectra translators for generating initial SMILES candidates, we replace our original translator with one that was pretrained on a large-scale simulated IR datasetAlberts et al. (2024b) (790k spectra) and subsequently finetuned on our experimental data.
Although simulated and experimental spectra differ in nature, the pretrained translator captures rich spectral patterns from large-scale simulated data, which remain beneficial when transferred and adapted to experimental data through fine-tuning.
Figure [2](https://arxiv.org/html/2508.16112v1#S5.F2) (b) shows that the transferred translator alone achieves strong performance.
When integrated into our framework, it leads to further improvements, highlighting the robustness of our method to different spectra translator choices.

Figure: Figure 3: Outputs of expert agents in IR-Agent during the structure elucidation process.
Refer to caption: x3.png

Case Study.
In Figure [3](https://arxiv.org/html/2508.16112v1#S5.F3), we present how IR-Agent performs the structure elucidation process.
The TI Expert identifies local substructures by comparing the IR absorption table interpretation with the output of the IR Spectra Translator.
For instance, the TI Expert infers the presence of a C–F group with high confidence, as both the Translator output and the table interpretation consistently point to C–F bonds.
In contrast, the presence of a halogen substitution such as Br is inferred with low confidence, since the table interpretation refers to C–Cl bonds, which are not found in the Translator output.
On the other hand, the Ret Expert extracts global structural patterns from the retrieved candidates, identifying a benzene ring substituted with a CF₃ group as a dominant motif, which serves as the broader structural context.
Based on the complementary analyses from both experts, the SE Expert successfully infers the complete molecular structure of the target spectrum.

## 6 Conclusion

In this paper, we propose IR-Agent , a novel multi-agent framework for structure elucidation from IR spectra that mimics the expert analytical process. To achieve this, we design a system composed of three specialized agents: a Table Interpretation (TI) Expert that extracts local substructures from the IR absorption table; a Retriever (Ret) Expert that provides global structural cues from retrieved candidates; and a Structure Elucidation (SE) Expert that integrates both sources of information to infer the final molecular structure. Furthermore, our framework supports the integration of chemical information in a lightweight and concise manner, which highlights its practicality in real-world analytical scenarios. Through extensive experiments including diverse chemical information conditions, we demonstrate both the effectiveness of IR-Agent  in structure elucidation and the flexibility of the multi-agent framework in adapting to various analytical scenarios.

## Acknowledgements

This work was supported by grants from the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (RS-2022-NR068758 and RS-2024-00335098), and by the Institute of Information & Communications Technology Planning & Evaluation (IITP), funded by the Korea government (MSIT) (RS-2025-02304967, AI Star Fellowship (KAIST)).

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## Appendix A Implementation Details

Implementation details of IR-Agent are presented in this section.

### A.1 IR Spectra Preprocessing

We convert spectra from transmittance to absorbance using the standard formula:

$$ $A=-\log_{10}(T)$ (6) $$

where $T$ denotes the transmittance and $A$ denotes the absorbance. To avoid mathematical errors during the logarithmic transformation and ensure numerical stability, any zero-valued entries are replaced with a small positive constant, $10^{-10}$.

### A.2 IR Spectra Translator

We represent the IR spectrum as a 1D sequence $\mathcal{X}\in\mathbb{R}^{1\times L}$, where $L$ is the number of absorbance values aligned with wavenumber positions. This input is then passed through a learnable linear transformation to produce a higher-dimensional feature sequence $\mathbf{x}\in\mathbb{R}^{L\times d}$. To inject positional information, we define a learnable positional embedding matrix $\mathbf{P}\in\mathbb{R}^{L\times d}$, where each row $\mathbf{P}_{i}$ corresponds to the positional embedding at the $i$-th wavenumber.
The input to the Transformer encoder is computed as:

$$ $\mathbf{z}_{i}=\mathbf{x}_{i}+\mathbf{P}_{i},\quad\text{for }i=1,\dots,L.$ (7) $$

The resulting spectrum representations $\mathbf{Z}=\{\mathbf{z}_{i}\}_{i=1}^{L}\in\mathbb{R}^{L\times d}$ are first fed into the Transformer encoder and subsequently into the Transformer decoder, which autoregressively generates the target molecular sequence. The model is trained to maximize the likelihood of the ground-truth output tokens given the input spectrum by minimizing the following cross-entropy (CE) loss:

$$ $\mathcal{L}_{\text{CE}}=-\frac{1}{N}\sum_{n=1}^{N}\sum_{t=1}^{T_{n}}\log p_{\theta}(y_{t}^{(n)}\mid y_{<t}^{(n)},\mathcal{X}^{(n)}),$ (8) $$

where $N$ is the number of training examples, $T_{n}$ is the length of the target sequence for the $n$-th example, $y_{t}^{(n)}$ denotes the $t$-th token in the ground-truth molecular SMILES for the $n$-th example, and $p_{\theta}$ denotes the model’s predicted probability distribution parameterized by $\theta$, where $\theta$ represents the learnable parameters of the translator. This objective corresponds to the standard next-token prediction loss widely used in training language models Vaswani et al. (2017).

Training Details. The Translator is implemented in Python 3.11.10 and PyTorch 2.5.1. We use the Adam optimizer for model training. The model is trained for up to 300 epochs, with early stopping applied if the best validation BLEU score does not improve for 25 consecutive epochs. All the experiments are conducted on a 48GB NVIDIA RTX A6000.

Hyperparameters. For the Translator, we use a batch size of 16, hidden dimension $d=128$, and a learning rate of 0.001 with a linear scheduler and 8,000 warm-up steps. The model consists of 2 encoder layers and 2 decoder layers, and the number of retrieved spectra ($N$) is set to 10. A beam width of 3 is chosen to reflect a practical decoding setting with moderate computational cost. A comparison of performance across larger beam widths is provided in Section [B.1](https://arxiv.org/html/2508.16112v1#A2.SS1). Note that when the model fails to generate the desired number of outputs, we apply greedy decoding to supplement the remaining outputs and guarantee a fixed output size.
We perform a grid search over learning rates {0.0001, 0.0005, 0.001}, batch sizes {16, 32, 64}, and hidden dimensions {64, 128}, and report test performance based on the best model selected according to validation set results.

### A.3 IR Absorption Table

Table LABEL:app:_absorption_table_1 shows the IR absorption table used in this paper, which is available online (^2^22[https://chem.libretexts.org/Ancillary_Materials/Reference/Reference_Tables/Spectroscopic_Reference_Tables/Infrared_Spectroscopy_Absorption_Table](https://chem.libretexts.org/Ancillary_Materials/Reference/Reference_Tables/Spectroscopic_Reference_Tables/Infrared_Spectroscopy_Absorption_Table)). For wavenumber entries specified as single points rather than ranges, we convert them into ranges by applying a $\pm 5\ \mathrm{cm}^{-1}$ window around each point.

**Table 4: Wavenumber Range and Substructure Assignments**
| Wavenumber (cm^-1) | Substructure |
| --- | --- |
| 3700–3584 | alcohol (O–H) |
| 3550–3200 | alcohol (O–H) |
| 3500–3400 | primary amine (N–H) |
| 3400–3300 | aliphatic primary amine (N–H) |
| 3330–3250 | aliphatic primary amine (N–H) |
| 3350–3310 | secondary amine (N–H) |
| 3100–2900 | carboxylic acid (O–H) |
| 3200–2700 | alcohol (O–H) |
| 3000–2800 | amine salt (N–H) |
| 3333–3267 | alkyne (C–H) |
| 3100–3000 | alkene (C–H) |
| 3080–2840 | alkane (C–H) |
| 2830–2695 | aldehyde (C–H) |
| 2600–2550 | thiol (S–H) |
| 2354–2344 | carbon dioxide (O=C=O) |
| 2285–2250 | isocyanate (N=C=O) |
| 2260–2222 | nitrile (C≡N) |
| 2260–2190 | disubstituted alkyne (C≡C) |
| 2175–2140 | thiocyanate (S–C=N) |
| 2160–2120 | azide (N=N=N) |
| 2155–2145 | ketene (C=C=O) |
| 2145–2120 | carbodiimide (N=C=N) |
| 2140–2100 | monosubstituted alkyne (C≡C) |
| 2140–1990 | isothiocyanate (N=C=S) |
| 2005–1995 | ketenimine (C=C=N) |
| 2000–1900 | allene (C=C=C) |
| 2000–1650 | aromatic compound (C–H) |
| 1818–1750 | anhydride (C=O) |
| 1815–1785 | acid halide (C=O) |
| 1800–1770 | conjugated acid halide (C=O) |
| 1780–1770 | conjugated anhydride (C=O) |
| 1770–1780 | vinyl/phenyl ester (C=O) |
| 1765–1755 | carboxylic acid (C=O) |
| 1750–1735 | esters (C=O) |
| 1750–1740 | cyclopentanone (C=O) |
| 1740–1720 | aldehyde (C=O) |
| 1730–1715 | α,β–unsaturated ester (C=O) |
| 1725–1715 | conjugated anhydride (C=O) |
| 1725–1705 | aliphatic ketone (C=O) |
| 1720–1706 | carboxylic acid (C=O) |
| 1710–1685 | conjugated aldehyde (C=O) |
| 1710–1680 | conjugated acid (C=O) |
| 1695–1685 | primary amide (C=O) |
| 1690–1640 | imine/oxime (C=N) |
| 1685–1675 | tertiary amide (C=O) |
| 1685–1666 | conjugated ketone (C=O) |
| 1678–1668 | trans–disubstituted alkene (C=C) |
| 1675–1665 | tetrasubstituted alkene (C=C) |
| 1662–1626 | cis–disubstituted alkene (C=C) |
| 1658–1600 | alkene (vinylidene) (C=C) |
| 1655–1645 | δ–lactam (C=O) |
| 1650–1600 | conjugated alkene (C=C) |
| 1650–1580 | amine (N–H) |
| 1650–1566 | cyclic alkene (C=C) |
| 1648–1638 | monosubstituted alkene (C=C) |
| 1620–1610 | α,β–unsaturated ketone (C=C) |
| 1550–1500 | nitro compound (N–O) |
| 1470–1460 | alkane (methylene group) (C–H) |
| 1455–1445 | alkane (methyl group) (C–H) |
| 1440–1395 | carboxylic acid (O–H) |
| 1420–1330 | alcohol (O–H) |
| 1415–1380 | sulfate (S=O) |
| 1410–1380 | sulfonyl chloride (S=O) |
| 1390–1380 | aldehyde (C–H) |
| 1390–1310 | phenol (O–H) |
| 1385–1380 | alkane (gem dimethyl) (C–H) |
| 1380–1370 | alkane (methyl group) (C–H) |
| 1372–1335 | sulfonate (S=O) |
| 1372–1290 | nitro compound (N–O) |
| 1370–1365 | alkane (gem dimethyl) (C–H) |
| 1370–1335 | sulfonamide (S=O) |
| 1350–1342 | sulfonic acid (S=O) |
| 1350–1300 | sulfone (S=O) |
| 1342–1266 | aromatic amine (C–N) |
| 1310–1250 | aromatic ester (C–O) |
| 1300–1250 | phosphorus oxide (P–O) |
| 1275–1200 | alkyl aryl ether (C–O) |
| 1250–1195 | phosphorus oxide (P–O) |
| 1250–1020 | amine (C–N) |
| 1225–1200 | vinyl ether (C–O) |
| 1210–1163 | ester (C–O) |
| 1205–1124 | tertiary alcohol (C–O) |
| 1204–1177 | sulfonyl chloride (S=O) |
| 1200–1185 | sulfate (S=O) |
| 1200–1000 | fluoro compound (C–F) |
| 1195–1168 | sulfonate (S=O) |
| 1170–1155 | sulfonamide (S=O) |
| 1165–1150 | sulfonic acid (S=O) |
| 1160–1120 | sulfone (S=O) |
| 1150–1085 | aliphatic ether (C–O) |
| 1124–1087 | secondary alcohol (C–O) |
| 1085–1050 | primary alcohol (C–O) |
| 1075–1020 | alkyl aryl ether (C–O) |
| 1075–1020 | vinyl ether (C–O) |
| 1070–1030 | sulfoxide (S=O) |
| 1050–1040 | anhydride (CO–O–CO) |
| 995–985 | monosubstituted alkene (C=C) |
| 915–905 | monosubstituted alkene (C=C) |
| 980–960 | trans–disubstituted alkene (C=C) |
| 895–885 | alkene (vinylidene) (C=C) |
| 840–790 | trisubstituted alkene (C=C) |
| 760–540 | halo compound (C–Cl) |
| 730–665 | cis–disubstituted alkene (C=C) |
| 690–515 | halo compound (C–Br) |
| 600–500 | halo compound (C–I) |
| 750–700 | monosubstituted benzene derivative |
| 710–690 | monosubstituted benzene derivative |

### A.4 IR Peak Table Assigner

The IR Peak Table Assigner consists of two main components: (1) extracting peaks from the input spectrum, and (2) identifying relevant substructures using the IR Absorption Table (Table LABEL:app:_absorption_table_1). To extract peaks, we use the find_peaks function from SciPy(^3^33[https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.find_peaks.html](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.find_peaks.html)), setting the hyperparameters to height=1 and distance=50. This allows us to identify wavenumber positions where the absorbance exhibits local maxima, which we refer to as peaks. After extracting the peaks, we assign the corresponding substructures based on the IR absorption table, and generate textual interpretations. For example, if a peak is found within the range of (1200, 1000) cm^-1, the interpretation might be: "Peaks observed between 1200 and 1000 cm^-1 are typically associated with fluoro compounds (C–F)."

### A.5 LLM Agents

System Setup. Our agent system is implemented using Python 3.11.10, with langchain 0.3.25, langchain-openai 0.2.11, and langgraph 0.2.59.
We utilize three LLMs: GPT-4o-mini(^4^44[https://openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence/](https://openai.com/index/gpt-4o-mini-advancing-cost-efficient-intelligence/)), GPT-4o(^5^55[https://openai.com/index/hello-gpt-4o/](https://openai.com/index/hello-gpt-4o/)), and o3-mini(^6^66[https://openai.com/index/openai-o3-mini/](https://openai.com/index/openai-o3-mini/)). For GPT-4o-mini, we use the gpt-4o-mini-2024-07-18 model with a temperature setting of 0.8. For GPT-4o, we use gpt-4o-2024-08-06, also with a temperature of 0.8. For o3-mini, we use o3-mini-2025-01-31 with default settings and medium reasoning mode.

Cost Analysis.
In Table [5](https://arxiv.org/html/2508.16112v1#A1.T5), we summarize the cost per LLM, along with input and output token counts. Although the output length for both the TI and Ret Experts is constrained to fewer than 300 tokens, we observe that o3-mini, which is designed as a reasoning model, tends to generate a higher number of output tokens due to reasoning tokens that are not explicitly reflected in the final output. The overall API cost for a single run of IR-Agent increases in the order of GPT-4o-mini, GPT-4o, and o3-mini.

**Table 5: Cost analysis comparison across different LLMs.**
| Cost & Tokens | GPT-4o-mini | GPT-4o | o3-mini |
| --- | --- | --- | --- |
| Input cost (per 1M token) | $0.15 | $ 2.50 | $1.10 |
| Output cost (per 1M token) | $0.6 | $10.00 | $4.40 |
| Average input token | 1500 | 1500 | 1400 |
| Average output token | 600 | 600 | 4400 |
| Avg. cost per call (IR-Agent) | $0.0006 | $0.0097 | $0.0209 |

## Appendix B Further Analysis

### B.1 Performance Comparison Across Different Beam Widths

We validate the effectiveness of IR-Agent when using the IR Spectra Translator with larger beam widths. As shown in Figure [4](https://arxiv.org/html/2508.16112v1#A2.F4), increasing the beam width beyond the default setting of 3 improves the performance of the Translator, thanks to the increased diversity in the decoding process. Moreover, when integrated into our framework, IR-Agent consistently yields additional performance gains by leveraging the SMILES candidates generated by the enhanced translator.

Figure: Figure 4: Performance of IR-Agent using Translator across different beam widths
Refer to caption: x4.png

### B.2 Additional Case Studies

In Figure [5](https://arxiv.org/html/2508.16112v1#A2.F5), we present an additional case study for IR-Agent. The TI Expert infers the presence of halogen atoms such as F and Cl with high confidence, based on the consistency between the translator output and the table interpretation (e.g., C–F, C–Cl bonds). Additionally, the Ret Expert identifies a structural pattern consisting of an aromatic ring substituted with one or more halogen atoms. Based on these integrative analyses, the SE Expert accurately infers the complete molecular structure of the target spectrum.

Figure: Figure 5: Additional Case Study: Outputs of expert agents in IR-Agent.
Refer to caption: x5.png

## Appendix C Limitations & Future Work

We focus on extracting local structural information based on interpretations from the IR absorption table. However, accurate interpretation requires considering not only the peak positions, but also the peak shapes and intensities. Our framework is designed to incorporate diverse types of chemical information without requiring retraining or architectural modifications. Nonetheless, the IR Spectra Translator still needs to be retrained when adapting to new spectral datasets, similar to prior approaches. An alternative approach would be to directly input the IR spectrum as an image along with its raw spectral values into an LLM. With an effective prompting strategy or collaboration with external tools, this would enable the model to capture peak shapes and intensities during the interpretation of the IR absorption table, and to generate candidate SMILES without requiring retraining when adapting to new spectral datasets, effectively functioning as a translator.

## Appendix D Broader Impacts

Potential Positive Scientific Impacts.
In this work, we present IR-Agent, a multi-agent system that automates molecular structure elucidation from IR spectra by emulating expert analytical processes. The reasoning capabilities of individual agents, each equipped with distinct knowledge, contribute to a more effective structure elucidation pipeline, with clearly defined and specialized reasoning steps. Unlike prior methods, the proposed multi-agent framework offers the flexibility to incorporate various types of chemical information, making it adaptable to a wide range of analytical scenarios.

Potential Negative Societal Impacts.
While our approach demonstrates promising performance in structure elucidation, it relies on large language model (LLM) agents, which may occasionally produce hallucinated outputs or misinterpret information. Therefore, it is essential that this framework be used under the supervision of domain experts when applied to real-world IR spectral analysis.

## Appendix E Prompt Templates for Expert Agents

In this section, we provide the prompt templates used for each agent described in Section [4](https://arxiv.org/html/2508.16112v1#S4). In addition to the default setting without chemical information, we present modified prompt templates that incorporate additional chemical information in three scenarios: atom types, scaffold, and carbon count. For each case, a single sentence describing the given chemical information is appended to the original prompt.

Table: Table 6: Prompt for Table Interpretation (TI) Expert (Section [4.1](https://arxiv.org/html/2508.16112v1#S4.SS1))

Table: Table 7: Prompt for Table Interpretation (TI) Expert with Chemical Information(Section [4.4](https://arxiv.org/html/2508.16112v1#S4.SS4))

Table: Table 8: Prompt for Retriever (Ret) Expert (Section [4.2](https://arxiv.org/html/2508.16112v1#S4.SS2))

Table: Table 9: Prompt for Retriever (Ret) Expert with Chemical Information (Section [4.4](https://arxiv.org/html/2508.16112v1#S4.SS4))

Table: Table 10: Prompt for Structure Elucidation (SE) Expert (Section [4.3](https://arxiv.org/html/2508.16112v1#S4.SS3))

Table: Table 11: Prompt for Structure Elucidation (SE) Expert with Chemical Information (Section [4.4](https://arxiv.org/html/2508.16112v1#S4.SS4))

## Peer Reviews

confidence: 5
contribution: 3
initial_rating: 4
presentation: 2
questions: Data Scale and Generalization Capability
This study utilizes 9,052 experimental spectra for validation. Given the complexity of chemical space and the data requirements of large language models, could the authors provide evidence or discussion on how the framework generalizes to broader molecular diversity? For instance, has testing been conducted on external large-scale datasets or structurally unique compounds? Cross-validation on specific compound categories is recommended to address this limitation.

Comprehensiveness of Baseline Comparisons
Although the paper compares single-agent variants and basic Transformer models, recent studies (e.g., Guo et al.) have employed larger-scale LLMs (such as Llama-3 and Claude-3) for molecular parsing tasks. Could the authors supplement comparisons with such models? Additionally, have advanced Transformer-based methods (e.g., those incorporating enhanced attention mechanisms) been evaluated to ensure performance improvements are not achievable through simple architectural modifications?

Evaluation Metrics Beyond Accuracy
Relying solely on Top-K accuracy may not fully capture the quality of structural predictions. Would the authors consider incorporating structural similarity metrics (e.g., fingerprint similarity calculated via RDKit) to assess whether "approximately correct" predictions retain chemical significance? This would strengthen the argument for the framework's practical utility.

Mechanism Analysis of Multi-Agent Advantages
The superiority of multi-agent systems is primarily attributed to accuracy improvements. Could the authors provide qualitative case studies or error analyses to specify how task division and collaboration avoid typical failure modes of single-agent systems (e.g., handling ambiguous peaks or reconciling conflicting evidence)? Such analysis would clarify the conceptual advantages of the framework.

Sensitivity Analysis of the Initial Translator
The framework's dependency on the IR spectral translator raises concerns about error propagation. Could the authors quantify robustness by testing performance degradation under noisy or low-quality candidate SMILES? For example, how does the system perform when the correct structure is absent from the initial candidate set? Ablation studies on translator quality could delineate the framework's operational boundaries.
soundness: 2
strengths: 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.
summary: This paper introduces IR-Agent, a novel LLM-based multi-agent framework for molecular structure elucidation from infrared spectra. The system employs three specialized agents that leverage external tools to emulate expert analytical reasoning. While demonstrating promising performance and extensibility through prompt-based integration of chemical knowledge, the approach exhibits notable limitations.
weaknesses: 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-the-art Transformer-based methods that might achieve comparable performance through architectural improvements.

Limited Evaluation Metrics: The sole reliance on accuracy as an evaluation metric is inadequate. The absence of structural similarity measures, such as the widely-recognized Fingerprint Tanimoto Similarity from the RDKit library, prevents comprehensive assessment of prediction quality.

Lack of Multi-agent Mechanism Analysis: The paper provides insufficient analysis explaining why the multi-agent approach outperforms single-agent systems. It fails to demonstrate specific scenarios where multi-agent collaboration prevents errors or enhances reasoning robustness beyond mere accuracy improvements.

Translator Dependency Unexplored: The study doesn't investigate how translation errors propagate through the system, leaving the model's sensitivity to the initial candidate SMILES quality unquantified.

---

confidence: 4
contribution: 2
initial_rating: 4
presentation: 2
questions: + In the **Task Description section**, the IR spectrum is represented as a one-dimensional array. However, different molecules possess distinct functional groups that are reflected at different wavenumbers on the x-axis, **often spanning different ranges**. How can a 1-D array adequately align these patterns and capture the variations in absorbance energy across such differing wavenumber ranges?
+ The **Table Interpretation Expert** is crucial because knowing the functional groups can almost directly determine the molecular structure. The current agent appears to make decisions based on relatively coarse wavenumber intervals, but is this level of accuracy sufficient? A comparison with traditional software such as **Omnic (Thermo Nicolet)** is important, as this is a highly complex problem involving baseline preprocessing, peak location detection, and other subtleties.
+ It is unclear whether the current IR-Agent is capable of handling samples with multiple molecular components. For mixtures, peak overlapping presents a major challenge that should be explicitly discussed. If the method is not designed for such cases, the applicable problem scope should be clearly defined in the paper.
+ In the **Experimental Setup section**, the training IR spectra are used as the database for the Retriever Expert. This raises an important concern about database coverage: does the retrieval database sufficiently include the molecular structures present in the test set? If the database does not fully cover the test molecules, retrieval in step 2 may frequently return incorrect candidates. The authors must therefore clarify whether and how such incorrect retrievals impact the downstream Structure Elucidation Expert (step 3). Conversely, if the database does fully cover the test structures and retrieval accuracy is high, it is important to quantify the incremental contribution of the Structure Elucidation Expert: to what extent does it improve final SMILES inference beyond what a strong Retriever alone (such as Omnic ) would achieve? 
+ While the paper reports good performance on real experimental patterns, one critical issue is the low overall accuracy. A survey of results from other published studies on the same or similar tasks would be helpful for context and reference for the reader.
soundness: 3
strengths: 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.
summary: This paper proposes **IR-Agent**, a multi-agent framework for molecular structure elucidation from IR spectra. It consists of three components: the **Table Interpretation Expert**, responsible for retrieving functional groups; the **Retriever Expert**, which retrieves potential molecules; and the **Structure Elucidation Expert**, which integrates all information to infer the corresponding SMILES. Experimental results show that the proposed framework outperforms existing methods, although several key limitations remain.
weaknesses: 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**.

---

confidence: 5
contribution: 1
initial_rating: 4
presentation: 3
questions: Did the authors use canonical SMILES when computing top-k accuracy?
What happens when the molecule is not included in the expert knowledge base or training dataset?
Could the authors include Tanimoto similarity, token-level, or functional-group metrics to quantify the chemical diversity of predicted candidates?
Can the authors provide examples of inter-agent reasoning beyond simple peak annotation (e.g., how agents collaborate to ensure structural consistency)?
soundness: 2
strengths: 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.
summary: The paper introduces IR-Agent, a multi-agent framework for molecular structure elucidation from infrared (IR) spectra. Each agent is designed to emulate a specific aspect of human expert analysis (e.g., identifying functional groups, interpreting peaks, or integrating contextual cues), and the system combines their outputs to predict the full molecular structure, expressed as a SMILES string. The authors claim that this architecture reflects expert-driven reasoning and offers flexibility to incorporate auxiliary chemical information. Experimental results show moderate performance on benchmark IR datasets and limited gains when additional descriptors (e.g., atom types, carbon count, or scaffolds) are provided.
weaknesses: 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 qualitative examples mostly show pattern annotations such as “the peak at 1700 cm⁻¹ indicates a C=O group.”
For chemists, this does not constitute genuine reasoning, which would involve cross-checking multiple spectral regions, verifying the chemical plausibility of substructures, and ensuring internal consistency (e.g., in anthracene C-H and C=C will be shifted due to the precess of the other rings).
Thus, the claimed interpretability remains superficial and non-scientific, weakening the paper’s argument that the agents emulate expert analysis.

Evaluation metrics are insufficient:
Only top-k accuracy is reported. This metric does not capture the chemical proximity or diversity of predicted molecules. Additional measures such as Tanimoto similarity, token-level accuracy, or functional-group recall would better reflect predictive utility in semi-automated settings.

Given that IR spectra alone cannot determine molecular structure unambiguously, the framework’s current performance is insufficient for realistic applications.
While the topic is relevant and the proposed architecture is creative, the results and qualitative analyses do not convincingly demonstrate meaningful chemical reasoning or practical utility. The low predictive accuracy, combined with the lack of interpretability beyond surface-level annotations, limits the paper’s contribution to the community.

---

confidence: 4
contribution: 2
initial_rating: 2
presentation: 2
questions: 1) What is the SOTA non-agentic method to solve the structure elucidation problem in IR?
2) Is there a feedback mechanism in IR-Agents where the Experts can improve themselves in the process?
3) Is there a critique mechanism to monitor the performance of agents?
4) Typically, multiple-agent systems are applied in settings where a design target or goal is imagined and the agents get feedback to take smarter action in the next round. The LLM used in this paper works on solving a one-time task. Is it fair to refer to this system as a multi-agent system? I understand there are two LLMs involved, but I am asking about the "agency". What is making the method an agentic work?
5) Are there innovations in terms of agentic system design?
6) Is there a reason why IR-Agent with GPT-4o and 4o-mini does not outperform the Transformer? Is that expected?
soundness: 2
strengths: 1) Application of agents to an IR problem seems novel
2) An ablation study is done on TI and Ret experts, which supports one of the claims.
summary: The paper proposed IR-Agent, a multi-agent AI system for structure elucidation from IR spectra. IR-Agent is composed of three specialized agents: Table Interpreation (TI) expert (extracts local substructures from the IR absorption), Retreiver (Ret) expert (provides global structural cues from retrieved candidates), and Structure Elucidation (SE) expert (integrates both sources of information to infer the final molecular structure). Experiments show improved performance on some experimental IR spectra.
weaknesses: 1) Lack of iterative correction or self-feedback mechanisms to improve the performance of agents
2) Performance improvements in Table 1, Table 2, and Fig. 2, and 3 are not significant
3) Lack of novelty in agentic system design.
4) No limitations of the method are explained.

Minor issues:
1) Figure 3 appears before Figure 2.
2)  Captions are not descriptive enough to fully grasp the results.
3) What are the numbers in the parentheses in the Figures and Tables?
4) The formal objective of molecular structure elucidation can be explained earlier in the intro.


Decision: Accept (Poster)