IR-Agent: Expert-Inspired LLM Agents for Structure Elucidation from Infrared Spectra
Authors: Heewoong Noh
Namkyeong Lee
Gyoung S. Na
Kibum Kim
Chanyoung Park
Venue: ICLR 2026 Poster (2026)
Keywords: LLM Agent
Infrared Spectroscopy
Structure Elucidation
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

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.

Published as a conference paper at ICLR 2026
IR-AGENT: EXPERT-INSPIRED LLM AGENTS FOR STRUCTURE ELUCIDATION FROM INFRARED SPECTRA
Heewoong Noh
15 Namkyeong Lee
Gyoung S. Na 1 Kibum Kim
Chanyoung Park
KAIST, KKRICT
ABSTRACT
Spectral analysis provides crucial clues for the elucidation of unknown materi- als. 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 reflect expert analytical processes and lack flex- ibility in incorporating diverse types of chemical knowledge, which is essential 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 inter- pretation, and their complementary roles enable integrated reasoning, thereby improving the overall accuracy of structure elucidation. Through extensive ex- periments, we demonstrate that IR-Agent not only improves baseline perfor- mance on experimental IR spectra but also shows strong adaptability to vari- ous forms of chemical information. The source code for iR-Agent is available at htttps:////i com/ http///eewoooohhh/eeoo/oo RR
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 (Grittiths) 2006), MS offers molecular weight and fragmented molecular structures (Lee) 1998), and NMR provides in-depth structural information, including stereochemistry (Li & 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 its low cost, speed, and high accessibility in laboratory settings (Coates et al. 2000; Mistek & Lednev 2018). Despite the case and affordability of acquiring IR spectra, their interpretation remains challenging and time-consuming task that requires extensive domain knowledge and expert experience /Varmuza et al. 1999; Jung et al. 20233).
To automate IR spectra analysis, various machine learning (ML)-based approaches have been explored. Early ML approaches for spectra analysis primarily focus on identifying functional groups, achieving high predictive accuracy. Specifically, convolutional neural network (CNN) architectures are employed classify functional groups from IR spectra (Jung al. [023]] |Wang al.) (202)). 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 material discovery identification of unknown material, underscoring the necessity of complete molecular structure elucidation.
More specifically, molecular structure whucidation-whiich aims generate the full SMILES represen- tation of an unknown molecule- requiuee-requires more comprehensive molecular information such as atomic composition, bonding information, and the connectivity of substructures, making it substantially
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more complex task compared to functional group classification (Xue al. 2023) As 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) 2024a Wu et al. 2025), exploring reinforcement learning with IR spectra alone (Ellis et al. 2023). and extending such methods to integrate both IR and NMR spectra (Devata et al. (2024). Despite these early advances, these methods generally rely on fixed and predefned input formats, which restricts their flexibility in accommodating diverse types of chemical information.
et al.
In real-world analytical scenarios, IR spectra are often accompanied by diverse chemical information, such as atom types inferred from synthesis pathways, the number of carbon atoms, or molecular scaffolds (i.e., the molecular skeleton structure). However, existing methods struggle to flexibly incorporate such information, since accommodating new types of inputs typically requires redesigning and retraining the model (Alberts al. 2024a; Jung al. 2023 Devata et al. [024). This highlights the need for a framework that can seamlessly integrate a wide range of chemical inputs.
Recently, by representing chemical information in natural language. Large Language Models (LLMs) have been effectively utilized in the field of biochemistry(L.a. et al. 2024h. For instance, M.LMs have been used to generate molecular structures in the string representation of molecules (i.e., SMILES) from text-based descriptions (Edwards et al. (022) and even modify molecular structures based on
specified conditions (Li et al.] 2024 Liu et al. (024a). These successes highlight that an LLM-based
framework is well-suited for building 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 infer local substructures and bonding patterns from peak positions (Socrates 2004 Larkin (017), and retrieve structurally similar molecules from spectral databases to provide global contextual clues (Moldoveanu & Rapson 1987). Similar to expert workflows, successful system should accurately perform each of these tasks extracting critical information from multiple sources and ultimately integrate the results in coherent reasoning process to predict the molccular structure. However, it is widely known that relying on a single LLM to perform all sub-tasks simultancously can result in suboptimal information extraction and may be inadequate for handling complex reasoning tasks (Chen et al. 2024 (un et al., 2023).
To this end, we propose IR-Agent, novel LLM- 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 effective 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 '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 specific aspect of IR spectrum analysis, their complementary roles enable an integrative analysis process, ultimately 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.
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To the best of our knowledge, this is the first work to leverage the LLM agents framework for molecular structure elucidation from IR spectra.
RELATED WORKS
2.1 MACIINE 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 are often costly and time-consuming Lee et al. (2023a b). In particular, ML approaches for IR spectra have demonstrated effectiveness in functional group identification, structural feature extraction, and molecular structure elucidation. CNNs have been applied to functional group classification (Jung et al. 2023 Wang et al. 2023). while GNNs have been used on spectrum graphs derived from the M-order Markov property for material classification and functional group detection (Na & Rho 2024p. Beyond functional groups, ML has also been extended to full molecular structure eluctdation. Transformer- based models are widely used: (Abeers al. (2024a) convert downsampled IR spectra into text, and Wu et al. (2025) apply patch-based sell-attention with data augmentation. Unlike these approaches, which assume access ground-truth chemical formulas, our setting relies solely on the IR spectrum. Additionally, reinforcement learning has been applied to structure elucidation using IR spectra (Ellis al.] (022)) with subsequent work extending this approach to incorporate both IR and NMR spectra (Devata et al. (202)). More recently, large language models have been explored for structure elucidation tasks with multi-modal spectral inputs, including IR, MS, and NMR spectra (Guo et al. 2024). Unlike prior work, IR-Agent aims to incorporate expert analytical processes and adopts 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, ChemCrow(Bran et al., (023) employs an LLM agent to autonomously perform tasks typically conducted by chemists, using a range of external tools. Similarly, Doscientis(Booike 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 science(Zhang et al. 2024) and biomedical domain, including applications in drug discovery(Inouut et al. 2024) and the design of biological experinentssRoohani Roohani et al.] 2024). In addition, recent work has explored the use of multi-agent frameworks effectively tackle drug discovery tasks(Lee et al. 2025 Liu et al. 2024b), highlighting the growing interest in collaborative LL.M-hased systems for complex scientific workflows. While LLM agents have yet been applied 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 viable solution for spectral analysis.
PRELIMINARIES
3.1 PROBLEM SETUP
Task Description. Given an IR spectrum X' E RIxL of molecule as input, where L denotes the number of absorbance values corresponding
wavenumber positions, IR-Agent predicts the raw SMILES representation of the molccule, a process known as molecular structure elucidation. While some studies (Wu et al. 2025 Alberts et al. (024a) assume that the ground-truth chemical formula is always available along with the IR spectrum, our setting considers only the IR spectrum. In practice, obtaining the exact formula of an unknown material is often unrealistic. Although mass spectrometry (MS) commonly employed, is costly and cons-onnu (Vas & Vekey 2004), difficult to interpret (Rolland & Prell 2021). and still leaves the derivation of an exact formula as highly non-trivial challenge (Böcker Dührkop 2016 Goldman al., 2023). Thus, we adopt a setting where the chemical formula is not used, employing a translator that directly predicts SMILES from IR spectra. Since our framework can also incorporate supplementary chemical information-such as atom types, carbon counts, or scaffold structures- -e further explore its applicability to more informed settings (Section 4.4). More details about analysis settings are provided in the Appendix A.2
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(a) Overall Framework
TI Expert
IR Spectra Tromitatrr
SE Expert
0
Based on Deperte Generate Top-10 SMIILES
Table Ausigner
Absorption
Spo
IR Spectra
Ret Expert
Output SMILLE
COc1ccc(Fccc /
Spectrra /Bsteever//ttbbb
10. OCOcled(0OOC
(b) TI Expert
Absorption Table
Translator Output(() & Table Interpretation
(3250) 3200;
asccool (0-4),
Identify substructures that are present both in the Interpretation and the corresponding
(3500) 3400)
(3400, 3300) "aliphakk primary anine (N-H)
"primary aniine (N HJ:
0.0
Translator Output
645)
Structural information from the IR Absorption Table
(c) Expert
Similarity Search
Retrieved Cosine
Analyyze the SMILES candidate spectra, structural Information
0.0
Structural information from similar spectra
Figure 1: Overview of IR-Agent. (a) Overall framework. Given an unknown IR spectrum, IR- Agent first utilizes the IR Spectra Translator generate candidate structures in SMILES format. The Table Interpretation (TT) 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 database. The Structure Elucidation (SE) Expert integrates analyses from both experts to produce the final predicted molecular structures. (b) Detailed view of the Table Interpretation (TI) Expert. (c) Detailed view of the Retriever (Ret) Expert.
Tools. this paper, we specifically design tools 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 similar to 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 Databuse contains a variety of spectra along with their corresponding molecules in SMILES format.
Additional details the tools and external knowledge are provided in the Appendix A
3.2 IR SPECTRA TRANSLATOR
To begin with, we introduce an IR Spectra Translator, a Transformer- based model (Vaswani et al.) 2017) that proposes initial pool of SMILES candidates from a target IR spectrum. Specifically, given the target IR spectrum X, we obtain a set SMILES candidates C as follows:
C -ss1..., sk] Transformer(A').
where denotes the number of SMILES candidates generated by the Translator using beam search decoding. Since deriving reliable SMILES directly from thousands of real-valued IR absorbance measurements is challenging for L.LMs, this module seeds the downstream reasoning process with plausible starting structures. which are subsequently expanded and revised. Additional details on the IR Spectra Translator are provided in Appendix A.2
4 PROPOSED METHOD: IR-AGENT
In this section, we introduce IR-Agent, a multi-agent framework for molecular structure elucidation from IR spectra that mimies expert analytical processes through specialized expert agents. Our
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framework combines off-the-shelf LLMs with analytical tools support integrative analysis process. It is composed of the following expert agents: (1) the Table Interpretation (TT) Expert. which employs an IR absorption table to identify substructures from SMILES sequences (Section[4.) (2) the Retriever (Ret) Expert, which extracts structural information from retrieved speetra (Section 4.2): and (3) the Structure Elucidation (SE) Expert, which provides ranked list of SMIL ES based on the outputs from both the Retriever Experts and Table Interpretation Experts (Section
The
overall framework is presented in Figure
4.1 TABLE INTERPRETATION (TT) 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 cap- tures fine-grained localized structural features, such as substitution patterns, cis/trans isomerism, and conjugation, which makes it 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: (I) 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 nigh-dimensional numerical absorbance data, which typically consists of thousands of values.
To address this limitation, the Expert agent employs the Peak Table Assigner tool, which extracts peaks from the spectrum by simply comparing the absorbance of neighboring wavenumbers and then assigns corresponding substructures to each peak based on its wavenumber range, by referring to the IR ahsorption table. An example output of the IR Peak Table Assigner is: "Peaks observed between 1200 and 1000 em are typically associated with fluoro compounds (C-F)," Specifically, given the task-specific prompt PTI Expert the IR Absorption Table (T), and IR Peak Table Assigner, the agent is defined as follows:
ATI Expert TI Expert PT IR Peak Table Assigner(XX C),C),
where C denotes the SMILES candidates generated by the IR spectra Translator. An Expert includes the potential substructures that can be included in the SMILES string.
Despite its utility, table-based interpretation has inherent limitations: 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 prompt Pr Expen that guides the agent to compare the output of the IR Peak Table Assigner with the SMILES candidates C, identify shared substructures, and generate 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 Absorption Table, IR Peak Table Assigner, the textual prompt are provided in Appendix A.3 and respectively.
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 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 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 identify spectra that are similar to the target IR spectrum. The IR Spectra Retriever adopts a simple yet effective approach: (I) 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(X() is defined as follows:
(candi sim candix simN)
IR Spectra Retriever(A!))
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where candi; denotes the SMILES corresponding to the i-the retrieved spectrum, and sim, denotes its cosine similarity to the the target spectrum. Given task-specific prompt Ret Expent and the retrieval output, the agent is defined as follows:
AReE Export Ret Expert (Reet Expert, IR Spectra Retriever(x)))
(4)
The output of the Ret Expert, i.e., Are 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
4.3 STRUCTURE ELUCIDATION (SE) EXPERT
Finally, the Structure Elucidation (SE) Expert conducts integrative structure reasoning based on both ATI Expert and ARet Expert follows:
ASE Expert SE Expert Exports ATI
Axpertt Expert ARee Export C),
(5)
where ASE Expert includes 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 MI. approachss and can flexibly incorporate various chemical information 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 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
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 single LLM to manage the entire reasoning pipeline, IR-Agent distributes the workload across specialized agents with each agent focusing 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-substructture 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 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 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, presented in Section 5.2

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5 EXPERIMENTS
5.1 EXPERIMENTAL SETUP
Datasets. In this study, we primarily use 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 al. 2023). The use of experimental spectra is particularly important, 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 realistic and practical basis for evaluating its effectiveness in real- world analytical workflows. Moreover, ensure dataset diversity, we include spectra from all phases (solid, liquid, and gas), do not exclude compounds with stereochemistry or ionic features, and impose no restrictions on heavy-atom count the presence of mixtures. Additional dataset details provided in Appendix [B] and the performance of IR-Agent on both single compounds and mixtures reported in Appendix C.6
External Knowledge. We use the IR Absorption Table available onlind" and employ the training set as 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 addi- tional 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 03-mini. We also consider setting where 03-mini is used to directly generate a ranked list of 10 SMILES structures, relying solely on the input candidate set 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 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 InChl representation (Heller et al. 2013). We report the average performance across three independent experiments.
5.2 RESULTS OF STRUCTURE ELUCIDATION
Effectiveness of IR-Agent. As shown in Table we ob- serve 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 (K 3) generated by the IR Spectra Translator, IR-Agent achieves higher Top- K ac- curacy compared the standalone Transformer model, which functions as the IR Spectra Translator in our sys- tem. This improvement is attributed to the complementary insights provided by both experts. Their collaborative anal- yses enable the Structure Flucidation (SH) 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
Table 1: Overall model performance for structure elucidation from IR spectra.
Top-K Accuracy
Method
Agent
Top-1
Top-3
Top-5
Top-10
Transformer
0.898
0.176
(2000)
(0200)
(003)
(3.00)
IR-Agent (GPT-4e miii)
single
0.072
(2:00
0.118
0000)
0.133
1063)
0.157
1300)
muhii
0.093
0.152
0.167
0.176
(2000)
0000)
1065)
1300)
IR-Agent (GPT-4c)
single
0.083
12094
0.135
0000)
0.165
1007)
1336:
mulii
0.093
0.155
0.177
(2.07)
0008)
IR-Agent (00-mani)
single
0.057
0.153
0008)
0.179
mulii
(2/00)
.00m)
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-40)
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Table
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Table 2: Overall model performance with various chemical information.
IR-Agent (single)
IR-Agent (malti)
03-mini
Chemical Information
(o3-mini)
(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:
(0.01))
(0.01))
(0.00))
(00J8)
(0213)
(0031)
(0.05)
(5003)
(0007)
(0.04)
(0,00)
Scaffold
0.096
0.160
0.177
0.198
0.112
0.195
0,208
0.228
0.118
0,208
0.258
01140)
01000)
01200)
(3100)
(6003)
06069)
0L0005
[1008]
(1010
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
01:00:
01.14)
01211)
(3103))
(0003)
00289)
01008:
011000)
00009)
0160m:
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
achieves comparable or even superior accuracy compared to the single-agent system built 03-mini, despite the former relying on a simpler model. This trend is also observed when comparing the multi-agent system (GPT-40-mini) with the single-agent version (GPT-4o). This result underscores the effectiveness of the multi-agent framework in handling the molecular structure elucidation, demon- strating its strength in performing integrative analysis beyond what a single-agent can achieve. We further provide comparisons with other baselines in the Appendix C.2
Structure Elucidation with Chemical Information. It is worth noting that IR-Agent is primarily developed with the assumption that the IR spectrum is the only available information; however, in practice, supplementary analyses often provide additional chemical data that can be leveraged 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 textual sentence to the prompt of the corresponding expert agent as described in Section 4.4] From Table 2
we make the following observations: (1) Even brief textual prompt containing chemical information can enhance the model's ability to predict accurate molecular structures. Without requiring any architecture modifications retraining, IR-Agent is able to successfully incorporate additional information into each expert's 's reasoning process through prompt-basee interaction, leveraging the inherent observe reasoning capabilities of LLMs. (2) Among the various types of chemical information, 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. Further experiments evaluating the robustness of IR-Agent to ambiguous chemical information are provided in Appendix C.4
5.3 IN-DEPTH ANALYSIS
Ablation Studies. To assess the contribution of each ex- pert agent, we conduct ablation studies by selectively moving them from the system. As shown in Table 3 rely- ing solely on the IR Spectra Translator without any expert assistance (No Experts) results in a significant drop in per- formance. Furthermore, using only one expert (i.c., 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-gre -eeggriiedd local information from the IR Absorption Table. Nevertheless, the Ret Ex-
Table 3: Ablation study of IR-Agent (o3- mini).
Top-K Accuracy
Expert
Top-1
Top-3
Top-5
Top-10
No Expert
0.073
0.131
0.157
0.185
TI Expert only
0.089
0.154
0.171
0.190
(0054)
(5/02)
(6/02)
Expert
0.098
0.169
0.188
0.211
00000)
(8005)
(0000)
(0/05)
IR-Agent (II+
0.103
0.178
0.199
0.216
(0005)
(0/03))
(0003)
(6/00)
pert 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 es- sential for providing complementary structural insights, enabling effective integrative analysis for structure elucidation.
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Number of SMILES candidates C. Moreover, we investigate how vary. ing the number of SMILES candidates C affects performance. As shown in Figure 2 (a), the performance of IR-Agent improves C increase up to 3 or 5, but tends to decline be- yond 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 manu- ally align information from the IR absorption table with an increasing number of candidates, which raises the risk of incorporating irrelevant or misleading structural features. These results suggest that selecting an appropriate number of SMILES can- didates crucial for effective expert reasoning.
Sensitivity Analysis:
Ro 7pp
Pretrined is Speera Pandana M-Agert
10-10
TopcKAccuarc
0.200
Performance of IR-Agent using the Pretrained
0.35
0.150
IR Spectra Translator. As our framework is com- patible with various IR spectra translators for gen- erating initial SMILES candidates, we replace original translator with a pretrained IR spectra trans- lator trained large-scale simulated data(Alberts) et al., (024a)). This translator adopts transformer architecture but differs from our original translator in its input representation: each IR spectrum downsampled to 400 points, and the intensities are discretized into the range 0-99, enabling text-based tokenization of spectral values. Although simulated and experimental spectra differ in nature, the pretrained translator captures rich spectral patterns from large-scale simulated data, which remain beneficial when adapted to experimental data through fine-tuning, Figure 2 (b) shows that the
0.15
10
1aa
kpp11 Sig
(a) Number of Translator Output[[]
(b) Pretrained Translator
Figure 2: In-depth Analysis results.
pretrained translator achieves strong standalone performance, while its integration into our framework yields additional improvements, highlighting the robustness of our method across different spectra translator choices.
Translator Output[[]
NcteccCCi//FFccc1/ "Nete=<@ricCCCF)FFFF NNtec=NeeCCF/FRi
Expert
C-F group > High confidence +> All three SMILEE include trifluoromethyl (CFx) substituent, which corressonds peaks between 1200-1000 cm typically
C-F bonds.
Halogen substitution (Br) > Low confidence >> SMILES 1 and 2 show bromine on the aromatic ring, but the IR halogens, match imprecise.
interpretation specifies C-CI; while
Ret Expert
aramatic bearing trifuuoroettyll group (-IIF](FIF), suggesting that the compaund likely features benzene ring substituted
candidate SMILES
target spectrum's molecule is an aromatic system with electron-donating and/or electron withdrawing substituents that can influence IR absorptions (e.g. CFS stretches near 1200 cmt", possible N-1t stretches for amines, and CaN stretches). The similarties the candidates' SMILES imply that target likely shares these structural motifs with variations the nature position of these substituents.
group. pattern indicates
SE Expert
2. Netecc(/1k<CCFFFFI CFFFiiie
Ncdccc(C)ccC(FI1FFs... 10. NedestOO)(((FFFFicc
Figure 3: Outputs of expert agents in IR-Agent during the structure elucidation process.
Case Study: How IR-Agent performs the structure elucidation. In Figure 3 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 Br is inferred with low confidence, since the table interpretation refers to C-CI 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 benzene ring substituted with a CF2 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.
Case Study: Structured reasoning by Table Interpretation(TT) Expert. In Figure 4 we demon- strate the structured reasoning process by which the TI expert identifies relevant local substructures from IR absorption table interpretations, rather than simply adopting table-based mappings. Although the two detected peak wavenumbers overlap with multiple wavenumber regions and initially cor- respond to six candidate substructures, the TI expert cross-validates these assignments against the translator outputs. As a result, only the isothiocyanate group associated with the 21 2140-1999 cm-

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Peaks wavenumber
[1350.265. 2060.1487]
Table interpretation mapping from the absorption table
(1372, cm associated with nitro compound (N-O).
Peak near (1420, 1390) cm associated with alcohol (O-H) cm is Peak near (1372, 1335) cm-a is associated with sulfonate (S=O).
(1390,
with phenol (O-H).
(13770, 1335) cm-
(5=O).
Peak near (2140, 1990) cm- is associated with isothiocyanate (N=C=S).
Translator Output(()
CC(C)tocc(CN))ccl 'CCC@etax(CN=C=sseett, C1=CCC 'C1=CCC1'
TI Expert Reasoning
Isothiocyanate high confidence "CCCCTTcc(CN===SScc exhibits N=C=S group,
matching the IR peak between 2140-1990 cm"), (No IR-assigned substructures are found in "CC(C)ccco(CHHNecT "C1=CCC1" based the provided mapping.)
Cclcce(CN-CC-Sjct
Figure 4: Example of structured analytical reasoning by the Table Interpretation (TI) expert.
region is retained, while the remaining substructures are excluded because they do not appear in the translator outputs. This conclusion consistent with the ground-truth SMILES, which also contains an isothiocyanate group.
CONCLUSION
In this paper, we propose IR-Agent, 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: Table Interpretation (TI) Expert that extracts local substructures from the IR absorption table; Retriever (Ret) Expert that provides global structural cues from retrieved candidates; and 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 lightweight and concise manner, which highlights its practicality in real-world ana- lytical 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 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-RR088758 and 35-2022-00033598) and by the Institute of Information & Communications Technology Planning & Evaluation (IITP), funded by the Korea government (MSIT) (RS- TRS-2025-02304967, Al Star Fellowship (KAIST)).
ETHICS STATEMENT
In this work, we present IR-Agent, a multi-agent system that automates molecular structure elucida- tion from spectra by emulating expert analytical processes. The reasoning capabilities of individual agents, each equipped with distinct knowledge, contribute to 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 adaptable a wide range of analytical scenarios. While our approach demonstrates promis- ing performance in structure elucidation, relies on large language model (LLM) agents, which may occasionally produce hallucinated outputs misinterpret information. Therefore, it essential that this framework be used under the supervision of domain experts when applied to real-world IR spectral analysis.
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REPRODUCIBILITY STATEMENT
To facilitate reproduction of the experimental results, we include all details in the main paper and appendix, describe the computational resources in Appendix A and provide the accompanying code at /tttts/://itttb.ccmomm/e/ wttpt//www....eeeee//o/oooooat/a AReet

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Supplementary Material
-IR-Agent: Expert-Inspine LLM Agents for Structure Elucidation from Infrared Spectra-
Implementation Details
16
A.I
IR Spectra Preprocessing
16
A.2
IR Spectra Translator
16
A.3
IR Absorption Table
17
A.4
IR Peak Table Assigner
19
A.5
LLM Agents
19
Dataset
20
Further Analysis
20
Performance Comparison Across Different Beam Widths
20
Performance Comparison with other baselines
20
Robustness of LLMs to Prompt Variations
21
Robustness of LLMs to ambiguous chemical information
22
C.5
Deterministic Workflow of IR-Agent vs. the ReAct Framework
22
C.6
The scope of IR-Agent
23
Additional Case Studies
23
L
Limitations Future Work
24
Prompt Templates for Expert Agents
24
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A
IMPLEMENTATION DETAILS
Implementation details of IR-Agent are presented in this section.
A. IR SPECTRA PREPROCESSING
We convert spectra from transmittance to absorbance using the standard formula:
A 1o
(6)
where T denotes the transmittance and denotes the absorbance. To avoid mathematical errors during the logarithmic transformation and ensure numerical stability, any zero-valued entries replaced with small positive constant, 10-10
A.2 IR SPECTRA TRANSLATOR
Analysis Setting Details. While some studies (Alberts al. 2024a Wu al. 2025) assume that the ground-truth chemical formula is always provided together with the IR spectrum, our framework relies solely the IR spectrum. This assumption holds only if mass spectrometry (MS), performed alongside IR spectroscopy, can provide an accurate chemical formula. In reality, however, obtaining a ground-truth chemical formula is rarely straightforward, owing several obstacles:
Cost and Time: Generating high-quality MS data demands labor-intensive, time-consuming, and often expensive sample preparation (Vas & Vekey 2004).
Interpretation Complexity: MS spectra are notoriously challenging interpret because of complex fragmentation patterns, overlapping peaks, and intrinsic resolution limits of the instrument [Rolland & Prell 2021).
Mon-Trivial Formula Derivation: Even with high-quality spectra, determining the exact formula remains difficult. For example, studies on benchmark datasets such as NPLIB exxpliiilll designed for this task task-report Top-1 accuracies of only 48% (Böcker & Dührkop 2016) and 71 1% (Goldman et al. 2023). underscoring the difficulty of precise formula determination.
Given these challenges, it is impractical to assume that the ground-truth chemical formula is always available for an unknown material. Accordingly, we adopt a setting where the chemical formula used by default (Ellis et al. 2023). and instead employ a SMILES translator that predicts molecular structures directly from IR spectra without formula information.
Model Implementation Details. We represent the IR spectrum as a ID sequence X E R1XL where L is the number of absorbance values aligned with wavenumber positions. This input is then passed through learnable linear transformation to produce a higher-dimensional feature sequence Rlxd To inject positional information, PE E L.xd where each row P, corresponds to the positional embedding at the i-th wavenumber. The input to the Transformer encoder is computed as:
define learnable positional embedding matrix
2; = x, + Pi, for = 11,...,..4
The resulting spectrum representations 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:
{z, | Lxd are first fed into the Transformer
LCB = T
log Pg (m) x()))
(8)
where N is the number of training examples, T, is the length of the target sequence for the n-th example, (() denotes the /-th token in the ground-truth molecular SMILES for the with example, and po denotes the model's predicted probability distribution parameterized by where 0 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).
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Training Details. The Translator is implemented in Python 3.11.10 and PyTorch 2.5.1. We the Adam optimizer for model training. The model 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 48GB NVIDIA RTX A6000.
Hlppepaarametes. For the Translator, we use batch size of 16, hidden dimension d = 128, and learning rate 0.001 with a linear scheduler and 8,000 warm-up steps. The model consists of encoder layers and 2 decoder layers, and the number of retrieved spectra (N) is set to 10. A beam width of is chosen to reflect practical decoding setting with moderate computational cost. A comparison of performance across larger beam widths is provided in Section C.I Note that when the model fails generate the desired number of outputs, we apply greedy decoding supplement the remaining outputs and guarantee a fixed output size. We perform a grid search over learning rates (0.0001, 0.0005, 0.001), 1, 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 4 shows the IR absorption table used in this paper, which is available online P For wavenumber entries specified as single points rather than ranges, we convert them into ranges by applying a +5 cm window around each point.
Table 4: Wavenumber Range and Substructure Assignments
Wavenumber (em-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)
3000-280
amine salt (N-H)
3333-3267
alkyne (C-H)
3100-3000
alkene (C-H)
3080-2840
alkane (C-H)
2830-2699
aldehyde (C-H)
1600-2550
thiol (S-H)
2354-2344
carbon dioxide (O=C=0)
2285-2250
isocyanate (N=C=O)
2260 2260-2222
nitrile (C=N) disubstituted alkyne (C=C)
2260-2190
2175-2140
thiocyanate (S (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) aromatic compound (C-H)
2000-1650
1818-1750
anhydride (C-0)
1815-1785
acid halide (C=0)
1800-1770
conjugated acid halide (C=0)
Continued on next page
htttees//deee..ibeettssss//oo/ http:///we...aaaaaaaaaa/aaaaaa/e Tables/ Tables/Spectronn hptp:/sscctttccccccccco Reference Tables/Intaaatte htw.:rrrrrrrrppppppoooooooooooo Absorption.
Spect
Table
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Table 4: Wavenumber Range and Substructure Assignments
Wavenumber (em
Substructure
1780-1770
conjugated anhydride (C=O)
1770-1780
vinyl/phenyl ester (C=0)
1765-1755
carboxylic acid (C=0)
1750-1735
esters (C=O)
1750-1740
cyclopentanone (C=O)
1740-1720
aldehyde (C=O)
1730-1715
a.B unsaturated ester (C=0)
1725-1715
conjugated anhydride (C=0)
1725-1705
aliphatic ketone (C=0)
1720-1706
carboxylic acid (C=0)
1710-1685
conjugated aldehyde (C=0)
1710-1680
conjugated acid (C=O)
1695-1685
primary amide (C=0)
1690-1640
imine/oxime (C=N)
1685-1675
tertiary amide (C=0)
1685-1666
conjugated ketone (C=0)
1678-1668
trans crans-disubstiuted alkene (C=C)
1675-1665
tetrasubstituted alkene (C=C)
1662-1626
cis assddiubbstiuted alkene (C=C)
1658-1600
alkene (vinylidene) (C=C)
1655-1645
8 l-lact (C=O)
1650-1600
conjugated alkene (C=C)
1650-1580
amine (N-H) cyclic alkene (C=C)
1650-1566
1648-1638
monosubstituted alkene (C=C) a.B c.B-uunsturated ketone (C=C)
1620-1610
1550-1500
nitro compound (N-O) alkane (methylene group) (C-H)
1470-1460
1455-1445
alkane (methyl group) (C-H)
1440-1395
carboxylic acid (O-H)
1420-1333
alcohol (O-H)
1415-1380
sulfate (S=O)
1410-1380
sulfonyl chloride (S=0)
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=0)
1350-1342
sulfonic acid (S=0)
1350-1300
sulfone (S=0) aromatic amine (C-N)
1342-1266
1310-1250
aromatic ester (C-O)
1300-122
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) tertiary alcohol (C-O)
1205-1124
1204-1177
sulfonyl chloride (S=O)
1200-1188
sulfate (S=O)
1200-1000
fluoro compound (C-F)
Continued on next page
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Table 4: Wavenumber Range and Substructure Assignments
Wavenumber (em
Substructure
1195-1168
sulfonate (S=0)
1170-1155
sulfonamide (S=0)
1165-1150
sulfonic acid (S=0)
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- diuussiiuusttttuted alkene (C=C)
895-885
alkene (vinylidene) (C-C)
840-790
trisubstituted alkene (C=C)
760-540
halo compound (C-CI)
730-665
cis cis-disubstiuuted alkene (C=C)
690-515
halo compound (C-Br)
600-500
halo compound (C-I)
700-700
monosubstituted benzene derivative monosubstituted benzene derivative
710 690
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 To extract peaks, we use the find __eaks function from SciPy setting the hyperparameters to he height=1 and didtincees atae=500. This allows us 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 the IR absorption table. and generate textual interpretations. For example, if peak found within the range of (1200, 1000) cm - the interpretation might be: "Peaks observed between 1200 and 1000 cm 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. langcha i in-openai 0.2.11. and langgrap raph 0.2.59. We utilize three L.LMs: GPT-40-min model with also with temperature with default settings and medium reasoning
GPT-40 and 03-min f0 For GPT-4o-mini, we use the 4pt-440-mi11-2224-07 a temperature setting 0.8. For GPT-4o, we use 4pt-44-2022--8 of 0.8. For 03-mini, we use o3-minn-2022-0111 mode.
Computational Cost Analysis. In Table 5 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 o3-mini. which is designed as a reasoning model, tends to explicitly reflected in
generate higher number of output tokens due reasoning tokens that 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. Additionally, find that LLMs with more intricate reasoning processes
find peaks.html http://eeeeeeiiiiooo//eeeeeeeeee httpaa//peeaa..ooo////////llllls
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Table 5: Computational cost analysis comparison across different LLMs.
Computational cost & Tokens
GPT--4-mini
GPT-4o
03-mini
Input cost (per 1M token)
$0.15
$2.50
$1.10
Output cost (per IM token)
S0.6
$10.00
$4.40
Average input token Average output token
1500
1500
1400
600
600
4400
Avg. cost per call (IR-Agent)
$0.0066
$0.0097
$0.0209
TI Expert (sec)
3.6
5.6
16.2
Ret Expert (sec)
6.2
5.5
8.2
SE Expert (sec)
3.2
2.7
18
IR-Agent(single, sec)
3.9
2.8
13.5
generally exhibit longer average runtimes per sample. Since the TI and Ret Experts can be operated in parallel, the total runtime for IR-Agent is determined by whichever of these two slower, along with the time required for the SE Expert. The IR-Agent (single), which performs all tasks at once, is much faster than the multi-agent approach. However, its performance lower compared to the multi-agent setup, indicating a trade-off between speed and accuracy. The complete process-which involves interpreting the IR absorption table, extracting key features from retrieved SMILES, and leveraging this information to generate the final SMILES candidates naturall requires a considerable amount of time. While the current LLM inference speed is not sufficient for high-throughpur applications, IR-Agent is still faster than manual expert analysis. Thus, it can serve as an effective tool for offering structural suggestions before human expert undertakes detailed spectrum interpretation.
large-scale
B DATASET
Preprocessing Details. Unlike prior work (Alberts et al. 2024a Wu al. 2025). we do not exclude compounds with stereochemistry or ionic states, nor do we restrict the heavy atom count to between 6 and 13 or limit elemental composition to C, H. N. O, S, P. and halogens. Instead, the NIST dataset we use contains 9,052 spectra with diverse phase compositions- solid-capturing broader chemical diversity. The heavy atom count ranges from 3 to 68 (mean: 13.4, median: 12.0), which substantially higher and more variable than in previous datasets. No filtering is imposed based on stereochemistry. charge state, or elemental composition; all spectra are retained. Consequently, our dataset exhibits higher SMILES token diversity and better reflects real-world experimental conditions. Following Na (2024,, we apply polynomial interpolation over wavenumbers ranging from 500-4000 em obtain structured format, addressing the inconsistent number of absorbance points across samples. For spectra recorded in transmittance mode, intensity values are converted to absorbance using standard conversion A.
gas, 20% liquid, and 24%
C FURTHER ANALYSIS
C.I PERFORMANCE COMPARISON ACROSS DIFFERENT BEAM WIDTHI
We validate the effectiveness of IR-Agent when using the IR Spectra Translator with larger beam widths. As shown Figure increasing the beam width beyond the default setting of 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.
C.2 PERFORMANCE COMPARISON WITH OTHER BASELINES
Table 6: Comparison with Additional Baselines
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IR Spectra Translator
IR Agent
0.30
Top-K Accuracy
0.25
0.20
0.15
0.10
Top-1
Top-3
Top-5
Top-10
Top-1
Top-3
Top-5
Top-10
Figure 5: Performance of IR-Agent using Translator across different beam widths
Top-K Accuracy
Tanimoto Similarity
Model
Top-1
Top-3
Top-5
Top-10
MACCS
RDKit
Morgan
1LaMA-3.1-8B Grattafiori et al. 2024
Patch-Base Self-Attention Transfomer Wu al. 2025
0.129
0.160
0,170
0.181
0.721
0.547
0.518
(0,00)
(0000)
(0000)
(0008)
(0.00)
(0,005)
(0.077)
0.103
0.178
0.199
0.216
0.770
0,596
0.549
IR-Agent
(0.002)
(00005)
We conduct additional experiments with two baselines: (1) Llama- 3-B Grattafioni et al. 2024).
and (2) the of thousands floating-point intensity values, we downsample each spectrum to 400 points and normalize the intensities to the range 0-99 make tokenization feasible. Using this representation, we evaluate Llama-3-8B under two settings: (1) zero-shot inference with the pretrained model and (2) QLoRA-basee fine-tuning.
Sell-Attention Transformer Wu al., (025). Since IR spectra consist
In both settings, the model fails to generate valid SMILES strings and often produced severely corrupted or nonsensical outputs. For the zero-shot case, this outcome is expected because the model has never been exposed any form of mapping between IR values and molecular structures. For the fine-tuning case, the difficulty appears to stem from the relatively modest dataset size and the challenge of learning meaningful spectral patterns directly from numeric sequences. The downsampling step may also have introduced information loss, further degrading model performance. An example of the data used in the Llama experiments is provided below.
('role': 'system', "content': 'You are an expert for IR spectroscopy, especially for molecular structure elucidation', 'role"; 'user', "content': "Given IR absorbance: IR 0 2 5 8 11 12 14 15 17 18 20 21 23 24 26 27 29 30 32 33... 46, generate the corresponding SMILES."]
In addition to the Ilaaa experiments, we also compare IR-Agent with another Transformer architec- ture featuring an advanced attention mechanism. The Patch-Based Self-Attention Transformer et al.) 2025) represents the -fate-of-the-ar non-agentic method for IR-based structure elucidation, incorporating a sophisticated patch-level attention design along with two data-augmentation strategies. To ensure a fair comparison, we used the official GitHub implementation and evaluated the model on the same test dataset IR-Agent. Our results show that although the Patch-Bass Based Self-Attention model attains higher Top-1 accuracy, IR-Agent outperforms it across all other exact-match metrics well as all Tanimoto similarity measures. This indicates that approach produces structurally more meaningful and chemically relevant predictions.
C.3 ROBUSTNESS LLMS TO PROMPT VARIATIONS
Table 7: Robustness of IR-Agent (o3-mini) prompt variations.
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Top-K Accuracy
Prompt
Top-1
Top-3
Top-5
Top-10 10
0.110
0.168
0.194
0.214
Prompt
(0.003)
(0.005)
(0.007)
(0.005)
0.100
0.182
0.201
0.222
Prompt 2
(0.006)
(0.004)
(0.011)
(0.006)
0.103
0.178
0.199
0.216
IR-Agent
(0.005)
(0.007)
(0.004)
(0.001)
To evaluate the robustness of IR-Agent across different prompts, we curated new prompt variations for each agent by rephrasing the originals. Specifically, we asked GPT-4o to "rephrase the given prompt with the same semantics, but in a different structure," and used the resulting two paraphrased versions for additional experiments with IR-Agent. Notably, IR-Agent demonstrated robust performance across these entirely paraphrased prompts, with results showing little deviation from the standard deviation observed with the original prompts.
C.4 ROBUSTNESS OF LLMS TO AMBIGUOUS CHEMICAL INFORMATION
Table 8: Robustness of IR-Agent (o3-mini) to ambiguous chemical information.
Top-K Accuracy
Chemical Information
Top-1
Top-3
Top-5
Top-10
0.103
0.178
0.199
0.216
No Knowledge (table
(0005)
(0.007)
(0004)
(0000)
Ambiguous Carbon Num
0.091
0.188
0.213
0,249
(0/03)
(0.03))
(0.06)
(0005)
0.123
0.190
0.215
0.252
Exact Carbon Num (table 2
(0.003)
(0.005)
(0.009)
(0.00)
In practical experimental settings, chemical information is often ambiguous or incomplete. To reflect this, we also consider an "ambiguous carbon number" scenario, where the number of carbons is provided as a range (i.e., exact carbon number +1) rather than an exact value. We evaluate the performance of IR-Agent under this setting with incomplete information. Experimental results show that, as expected, the performance of IR-Agent drops when given an ambiguous carbon number compared the exact value. However, except for the Top-1 metric, the decrease in performance is not significant, indicating that IR-Agent can still accurately predict the correct SMILES even when only incomplete carbon information is available.
C.5 DETERMINISTIC WORKFLOW OF IR-AGENT VS. THE REACT FRAMEWORK
Table 9: Comparison of -Rent (GPT-4o) with ReAcT framework
Top-K Accuracy
Workllow
Top-1
Top-3
Top-5
Top-10
ReAcT Framework
0.083
0.148
0.151
0.158
(6:04)
(0.00))
(0.08))
(0005)
IR-Agent
0.093
0.153
0.177
0.204
(8.007;
(0.005)
(0.055)
(0.05)
IR-Agent follows a largely deterministic and fixed pattern, lacking the dynamic Decisiionmaaing characteristic of agent systems. Since experts necessarily refer to both IR absorption tables and spectral databases when analyzing IR spectra, they rely on the combination of these two sources of information for interpretation. Unlike common tasks such as question answering, structure elucidation from IR spectra is highly specific and challenging, where faithfully emulating the expert reasoning process is essential. Therefore, we aim to design a system that closely mirrors the analytical workflow of domain experts. We conduct a comparative experiment using a ReAct agent framework, where the LLM autonomously selects its actions. Specifically, we employ GPT-4o as the base LLM and provided a set of SMILES candidates generated by the IR Spectra Translator. The available tools include the IR Peak Table Assigner, IR Spectra Retriever, and an additional finish tool that enables the agent terminate tool selection and output the final SMILES. The maximum number of tool calls is limited to five. In line with the ReAct framework, the agent iteratively goes through thought, action (tool selection), and observation steps.
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From our experimental results, observe that IR-Agent demonstrates superior performance compared to the ReAct framework. In the ReAct setting, the agent often successfully calls both the IR Peak Table Assigner and the IR Spectra Retriever. However, we also found two common failure modes: (1) the agent selects only the IR Peak Table Assigner, limiting itself to a narrow set of information, and (2) the agent repeatedly selects the same tool (e-g., retriever, then table, then table again), resulting final SMILES predictions that are biased toward the information provided by that tool alone. These observations suggest that, for the task of structure elucidation from IR spectra, which requires substantial domain knowledge and careful emulation of expert reasoning. a deterministic approach to tool selection-ai aas implemented in IR-Agent-is necessary. Such deterministic agent process is effective in emulating the expert analysis workflow and highlights the importance of guided, expert-like decision-making in this context
C.6
THE SCOPE OF IR-AGENT
Table 10: Performance of IR-Agent on Single Compounds and Mixtures
Type
Test
Top-1
Top-3
Top-5
Top-10
MACCS
RDK
Morgan
Single
886
0.105:0005)
0.183.0008)
0.204x0.05) 0.000
(.221 0.000
0.775
(03000))))
0.556000y
Mixture
20
0.000
0.000
0.567
0.310
0.277
Unlike previous Studies(Alberts et al. 2024a; Wu et al. 2025 J Jung et al. 2023 that typically restrict realistic
their scope single gas-phase materials, aimed to design model under and unconstrained setting using the NIST dataset. Consequently, our dataset (total: 9,052 spectra) includes both single compounds (8,810) and mixtures (242). However, peak overlap in mixtures poses a significant challenge for accurate prediction. Our empirical results show that although IR- Agent performs exceptionally well on single compounds, it struggles with mixtures due to signal complexity and limited training examples. Therefore, our IR-Agent focuses on handling single molecular components, and we consider mixture analysis an important but challenging direction for future work further enhance the agent's s capabilities.
C.7 ADDITIONAL CASE STUDIES
Translator Output(C]
FclocctC(idBdeet Telccc///llI "FcoooiCCsccB/
TI Expert
For Fcloct)))(BBccc
-C- > High confidence The "Fe" fragment indicates a fluorine directly bonded the aromaaic ring, matching the 1200-1000 cm" fluoro absorption.
-c- High confidence +) The presence of chlorine substituent the is consistent with the halo (C-CI) absorption at 760-540 cmr!.
Fcloc[B/)(CCCcc
-C- > High confidence >> The fluorine substituent ("Fc"l on the aromatic ring aligns with the 1200-1000 cmr" peak for C-F,
-C-0 confidence + The chlore substituent present and explains the band 760-540 cm" halo (C-CI) compounds.
- C- + High confidence + The aromatic fluorine ("Fc") confirms the IR feature at 1200-1000 cm" attributed to C-F bonds. benzene ring supports halo absorption
-C-0 confidence chlorine
760-540 cm"!
Ret Expert
(primarily functional groups like -5 (thiol/ thioether) or -CHH.. For example, the top candidate (Fclec:[/ee(C]cc1] features beroene ring with fluorine, chlorine, and sulfur subsituent. Other SMILES reinforce this pattern having dichlore-subsiitttinn le.g. COclocs(Cle(()cc) sulfur linker
Most candidate SMILES an aromatic ring decorated electronegative
Br) additional
Thus, it a likely that the target structure also consists a substituted aromatic core bearing one or more halogen atoms (notably CI) along with methoxy groups. These features high cosine similaribes observed.
heteroatom substituents such
vibrational modes found in spectrum and justify
SE Expert
1. Fctuo:CCha(rrcc,, 2. Fedecttrie((led...... 7. SCcleadClleeiccd 8. Felиt@CCHCCiel,
9. Fc1cce[C[)s(CIcc1,
10. Fcie|O)ku(rrccl
Figure 6: Additional Case Study: Outputs of expert agents in IR-Agent.
In Figure 6 we present an additional case study for IR-Agent. The TI Expert infers the presence of halogen atoms such as F and CI with high confidence, based on the consistency between the translator output and the table interpretation (e.g., C-F, C-CI bonds). Additionally, the Ret Expert identifies a structural pattern consisting of an aromatic ring substituted with one more halogen atoms. Based on these integrative analyses, the SE Expert accurately infers the complete molecular structure of the target spectrum.
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D LIMITATIONS & FUTURE WORK
We focus on extracting local structural information based on interpretations from the IR absorption table. However, accurate interpretation requires considering only the peak positions, but also the peak shapes and intensities. Since our framework refines and regenerates SMILES based on the candidates provided by the IR Spectra Translator, its overall performance is naturally influenced by the Translator. At the same time, the framework is designed to flexibly incorporate diverse types of chemical information without requiring retraining or architectural modifications. Nevertheless, when adapting to new spectral datasets, the Spectra Translator itself still needs to be retrained, as case with prior approaches. An alternative approach would be to directly input the IR spectrum as an image along with its raw spectral values into 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 generate candidate SMILES without requiring retraining when adapting to new spectral datasets, effectively functioning as a translator.
E PROMPT TEMPLATES FOR EXPERT AGENTS
In this section, we provide the prompt templates used for each agent described in Section 4 In addition to the default setting without chemical information, present modified prompt templates that incorporate additional chemical information in three scenarios: atom types, scaffold, and carbon count. For each case, single sentence describing the given chemical information is appended to the original prompt.
Table 11: Prompt for Table Interpretation (TI) Expert (Section 4.1
System Prompt:
You are expert organic chemist with specialized knowledge in analyzing infrared (IR) spectra.
Prompt:
You have an IR absorption interpretation that suggests certain substructures (e.g. nitrile, carbonyl, etc.), but this table-based mapping can be imprecise.
Given SMILES: (SMILES Candidates) IR interpretation: (Table Interpretation)
Your task is to:
For each SMILES in the given SMILES list, identify substructures that are present both in the IR interpretation and that SMILES.
Return a bulleted list in the format: substructure - confidence brief rationale
KEEP THE RESPONSE UNDER 300 TOKENS.
ONLY RETURN:
A bulleted list of (substructure confidence brief rationale).
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Table 12: Prompt for Table Interpretation (TI) Expert with Chemical InformntioonSecction 4.4
System Prompt:
You are expert organic chemist with specialized knowledge in analyzing infrared (IR) spectra.
You have an IR absorption interpretation that suggests certain substructures (e.g. nitrile, carbonyl, etc.), but this table-based mapping can be imprecise.
Prompt:
Given SMILES: (SMILES Candidates]
IR interpretation: Taable Interpretation]
The molecule corresponding to the target spectrum is known to include the following atom types: (Atom Types).
The molecule corresponding to the target spectrum is known to include the following scaffold: (Scaffold).
The molecule corresponding to the target spectrum is known to include exactly [Carbon Count) carbon atoms.
Your task is to: For each SMILES the given SMILES list, identify substructures that are present both the IR interpretation and in that SMILES.
Return a bulleted list in the format: substructure - confidence - brief rationale
KEEP THE RESPONSE UNDER 300 TOKENS.
ONLY RETURN: bulleted list (substructure -t confidence - brief rationale).
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Table 13: Prompt for Retriever (Ret) Expert (Section 4.2)
System Prompt:
You are an expert organic chemist with specialized knowledge in analyzing infrared (IR) spectra.
Prompt:
Your task is to analyze the SMILES of the candidate spectra, whose cosine similarity to the target spectrum is high.
If the target spectrum and candidate spectra exhibit high similarity, the SMILES the target spectrum may have similar structural characteristies to the SMILES of the candidate spectrum.
SMILES of candidate spectra and their cosine similarities to the target spectrum: [Output of IR Spectra Retriever)
Based on the SMILES list, extract the structural information to complement the SMILES of the target spectrum.
Provide reasoning to support your analysis.
Let's think step-by-step.
KEEP THE RESPONSE UNDER 300 TOKENS.
ONLY THE REQUESTED CONTENT SHOULD BE INCLUDED IN YOUR RESPONSE.
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Table 14: Prompt for Retriever (Ret) Expert with Chemical Information (Section 4.4
System Prompt:
You are expert organic chemist with specialized knowledge in analyzing infrared (IR) spectra.
Prompt:
Your task is to analyze the SMILES of the candidate spectra, whose cosine similarity to the target spectrum is high.
If the target spectrum and candidate spectra exhibit high similarity, the SMILES the target spectrum may have similar structural characteristics to the SMILES of the candidate spectrum.
SMILES of candidate spectra and their cosine similarities to the target spectrum: [Output of IR Spectra Retriever]
The molecule corresponding the target spectrum known to include the following atom types: {Atom Types).
The molecule corresponding to the target spectrum is known include the following scaffold: (Scaffold).
The molecule corresponding the target spectrum is known to include exactly
(Carbon Count) carbon atoms.
Based on the SMILES list, extract the structural information to complement the SMILES of the target spectrum.
Provide reasoning to support your analysis.
Let's think step-by-step.
KEEP THE RESPONSE UNDER 300 TOKENS.
ONLY THE REQUESTED CONTENT SHOULD BE INCLUDED IN YOUR RESPONSE.
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Table 15: Prompt for Structure Elucidation (SE) Expert (Section 4.3
System Prompt: You are an expert organic chemist with specialized knowledge in analyzing infrared (IR) spectra.
Prompt: Your task is to refine the given SMILES list and generate a N candidate list that aligns well with the IR spectrum while preserving structural diversity and plausibility.
The IR Absorption Table Agent provides potentially useful insights by interpreting the IR spectrum and suggesting possible substructures based on known absorption patterns.
IR Spectrum Retriever Agent examines the structural features of candidate SMILES that exhibit high cosine similarity to the target spectrum.
IR Absorption Table Agent Output: {AATI Expert
IR Spectrum Retriever Agent Output (high-similarity spectra & analysis): [AAeet Expent
I) Identify the substructures that are common to both the IR table interpretation and at least one SMILES in the list.
2) From the retriever agent output, extract structural information (e.g., recurring motifs / scaffolds) suggested by high-similarity candidates.
3) Guided by the structural insights from steps and 2, produce refined Top-N list of SMILES candidates.
4) Ensure the final list is chemically diverse and plausible-do not overfit to any single interpretation.
Based these analyses, regenerate list of Top-N SMILES by refining the target smiles: (SMILES Candidates).
Let's think step-by-step.
ONLY THE REQUESTED CONTENT SHOULD BE INCLUDED IN YOUR RESPONSE.
YOUR ANSWER FORMAT MUST BE AS FOLLOWS ONLY CONTAINING THE SMILES:
1. SMILES 1, 2. SMILES 2, SMILES 3, .... N. SMILES N
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Table 16: Prompt for Structure Elucidation (SE) Expert with Chemical Information (Section 4.4
You are an expert organic chemist with specialized knowledge in analyzing infrared (IR) spectra.
System Prompt:
Prompt:
Your task is to refine the given SMILES list and generate a N candidate list that aligns well with the IR spectrum while preserving structural diversity and plausibility.
The IR Absorption Table Agent provides potentially useful insights by interpreting the IR spectrum and suggesting possible substructures based on known absorption patterns.
IR Spectrum Retriever Agent examines the structural features of candidate SMILES that exhibit high cosine similarity to the target spectrum.
IR Absorption Table Agent Output: (AATI Expert
IR Spectrum Retriever Agent Output (high-similarity spectra & analysis): [AARet Export
The final predicted molecular structures are constrained to contain only the following atom (Ate: Types).
The final predicted molecular structures must incorporate the specified scaffold (Scaffold). The final predicted molecular structures required contain exactly (Carbon Count) carbon atoms.
1) Identify the substructures that are common to both the IR table interpretation and at least one SMILES in the list.
2) From the retriever agent output, extract structural information (c.g., recurring motifs scaffolds) suggested by high-similarity candidates.
3) Guided by the structural insights from steps 1,2, and [[Atom Types), (Scaffold), (Carbon Count]] constraint, produce refined Top-N list of SMILES candidates.
4) Ensure the final list is chemically diverse and plausible do not overfit to any single interpretation.
Based on these analyses, regenerate a list of Top-N SMILES by refining the target smiles: (SMILES Candidates).
Let's think step-by-step.
ONLY THE REQUESTED CONTENT SHOULD BE INCLUDED IN YOUR RESPONSE.
YOUR ANSWER FORMAT MUST BE AS FOLLOWS ONLY CONTAINING THE SMILES:
1. SMILES 1, 2. SMILES 2, 3. SMILES 3, N. SMILES_N
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Reviews:
[{"review_id": "t6QV9o9kIy", "reviewer": "Reviewer_byX6", "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.", "questions": "Data Scale and Generalization Capability\nThis 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.\n\nComprehensiveness of Baseline Comparisons\nAlthough 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?\n\nEvaluation Metrics Beyond Accuracy\nRelying 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.\n\nMechanism Analysis of Multi-Agent Advantages\nThe 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.\n\nSensitivity Analysis of the Initial Translator\nThe 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.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 3, "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.", "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.\n\nInsufficient 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.\n\nLimited 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.\n\nLack 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.\n\nTranslator 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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "AwDEd9hLfi", "reviewer": "Reviewer_u4dq", "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.", "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?\n+ 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.\n+ 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.\n+ 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? \n+ 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.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "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.", "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**.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "vhuAMSiSId", "reviewer": "Reviewer_MPaz", "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.", "questions": "Did the authors use canonical SMILES when computing top-k accuracy?\nWhat happens when the molecule is not included in the expert knowledge base or training dataset?\nCould the authors include Tanimoto similarity, token-level, or functional-group metrics to quantify the chemical diversity of predicted candidates?\nCan the authors provide examples of inter-agent reasoning beyond simple peak annotation (e.g., how agents collaborate to ensure structural consistency)?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 1, "strengths": "The paper tackles an important and challenging problem: automated molecular structure elucidation from spectroscopy data.\nThe proposed multi-agent architecture is conceptually interesting, providing a modular framework that could, in principle, improve interpretability and extensibility.\nThe experiments are clearly reported, including dataset details, model parameters, and top-k metrics.\nThe paper makes a valid attempt to align model design with expert workflows in chemistry.", "weaknesses": "Scientific limitations of the problem setup:\nIR 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.\n\nShallow “reasoning”:\nWhile 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.”\nFor 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).\nThus, the claimed interpretability remains superficial and non-scientific, weakening the paper’s argument that the agents emulate expert analysis.\n\nEvaluation metrics are insufficient:\nOnly 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.\n\nGiven that IR spectra alone cannot determine molecular structure unambiguously, the framework’s current performance is insufficient for realistic applications.\nWhile 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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "3uFr0WOIoq", "reviewer": "Reviewer_hhfV", "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.", "questions": "1) What is the SOTA non-agentic method to solve the structure elucidation problem in IR?\n2) Is there a feedback mechanism in IR-Agents where the Experts can improve themselves in the process?\n3) Is there a critique mechanism to monitor the performance of agents?\n4) 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?\n5) Are there innovations in terms of agentic system design?\n6) Is there a reason why IR-Agent with GPT-4o and 4o-mini does not outperform the Transformer? Is that expected?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1) Application of agents to an IR problem seems novel\n2) An ablation study is done on TI and Ret experts, which supports one of the claims.", "weaknesses": "1) Lack of iterative correction or self-feedback mechanisms to improve the performance of agents\n2) Performance improvements in Table 1, Table 2, and Fig. 2, and 3 are not significant\n3) Lack of novelty in agentic system design.\n4) No limitations of the method are explained.\n\nMinor issues:\n1) Figure 3 appears before Figure 2.\n2)  Captions are not descriptive enough to fully grasp the results.\n3) What are the numbers in the parentheses in the Figures and Tables?\n4) The formal objective of molecular structure elucidation can be explained earlier in the intro.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)