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arxiv: 2605.07103 · v1 · submitted 2026-05-08 · 💻 cs.AI · cs.MA

ARMOR: An Agentic Framework for Reaction Feasibility Prediction via Adaptive Utility-aware Multi-tool Reasoning

Ye Liu , Botao Yu , Xinyi Ling , Daniel Adu-Ampratwum , Xia Ning This is my paper

Pith reviewed 2026-05-11 00:50 UTC · model grok-4.3

classification 💻 cs.AI cs.MA
keywords reaction feasibility predictionmulti-tool reasoningagentic frameworktool utility modelingconflict resolutioncomputational chemistry
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The pith

ARMOR uses an agentic setup to learn each tool's strengths and resolve conflicts for more accurate reaction feasibility predictions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces ARMOR as a way to combine multiple AI tools for deciding if a chemical reaction is feasible. Individual tools perform unevenly across different reactions, so simple averaging or picking one tool often falls short. ARMOR instead builds a hierarchy that favors stronger tools for each case, tracks their specific patterns, and uses memory-augmented reasoning to settle disagreements. Experiments on a public dataset show consistent gains over single-tool and aggregation baselines, with the largest improvements occurring precisely when the tools disagree. This approach matters because reliable feasibility checks can speed up exploration in drug design and materials discovery without needing to run every reaction in the lab.

Core claim

ARMOR organizes tools into a hierarchy that prioritizes top-performing tools for each reaction, characterizes their strengths through tool-specific patterns, and resolves conflicts via memory-augmented reasoning to produce the final feasibility prediction.

What carries the argument

An agentic framework that models tool-specific utilities, adaptively prioritizes tools in a hierarchy, and resolves conflicts through memory-augmented reasoning.

If this is right

  • Single-tool methods and simple aggregation approaches are outperformed on the evaluation dataset.
  • The accuracy gains are largest on reactions where the underlying tools produce conflicting feasibility signals.
  • Explicit utility modeling and memory-based conflict resolution allow complementary strengths across tools to be used systematically.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same adaptive hierarchy and conflict-resolution steps could be applied to other multi-tool decision tasks such as property prediction or synthesis planning.
  • If tool utilities prove stable across chemical domains, the framework might reduce the need to retrain or select tools for each new subfield.
  • Memory-augmented reasoning for conflicts may generalize to settings where tools provide probabilistic rather than binary outputs.

Load-bearing premise

The public dataset reflects the distribution of real-world reactions and the learned tool utilities and conflict patterns will hold for new reactions outside the test set.

What would settle it

Run ARMOR on a fresh collection of reactions drawn from a different source where tool predictions frequently conflict and check whether the accuracy advantage over baselines disappears.

Figures

Figures reproduced from arXiv: 2605.07103 by Botao Yu, Daniel Adu-Ampratwum, Xia Ning, Xinyi Ling, Ye Liu.

Figure 1
Figure 1. Figure 1: ARMOR framework. The robot icon indicates that the corresponding module is agentic. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Ablation study of ARMOR. In this subsection, we conduct ablation experiments to assess the effectiveness of different components in the ARMOR framework. We progressively remove the tool conflict resolution module (Section 3.3), utility-aware tool prioritization module (Section 3.2) and tool hierarchy con￾struction module (Section 3.1), resulting in three variants: -w/o Conflict, -w/o Utility, and -w/o Hier… view at source ↗
Figure 3
Figure 3. Figure 3: Case study. To further illustrate the reasoning process of ARMOR, we present a representative case study in [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
read the original abstract

Reaction feasibility prediction, as a fundamental problem in computational chemistry, has benefited from diverse tools enabled by recent advances in artificial intelligence, particularly large language models. However, the performance of individual tools varies substantially across reactions, making it difficult for any single tool to consistently perform well across all cases. This raises a critical challenge: how to effectively leverage multiple tools to obtain more accurate feasibility predictions. To address this, we propose ARMOR, an agentic framework that explicitly models tool-specific utilities, adaptively prioritizes tools, and further resolves the potential tool conflicts to produce the final prediction for each reaction. Unlike existing approaches that rely on simple aggregation or heuristic assignment over various tools, ARMOR organizes tools into a hierarchy that prioritizes top-performing tools and defers others when needed, characterizes their strengths through tool-specific patterns, and resolves conflicts via memoryaugmented reasoning. Extensive experiments on a public dataset demonstrate that ARMOR consistently outperforms strong baselines, including single-tool methods as well as various tool aggregation and tool selection approaches. Further analysis shows that the improvements are particularly significant on reactions with conflicting tool predictions, highlighting the effectiveness of ARMOR in leveraging the complementary strengths of multiple tools. The code is available via https://anonymous.4open.science/r/ARMOR-E13F.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript introduces ARMOR, an agentic framework for reaction feasibility prediction in computational chemistry. It explicitly models tool-specific utilities, organizes tools into a performance-based hierarchy for adaptive prioritization, and resolves conflicts among tools via memory-augmented reasoning. Experiments on a public dataset show consistent outperformance versus single-tool baselines and various aggregation/selection methods, with larger gains on reactions exhibiting conflicting tool predictions; code is released.

Significance. If the experimental results hold under scrutiny, the work could meaningfully advance multi-tool reasoning for scientific prediction tasks by replacing heuristic aggregation with utility-aware, conflict-resolving mechanisms. The focus on complementary tool strengths and the public code release are clear strengths that would support reproducibility and extension.

major comments (1)
  1. [Experiments] Experiments section: the central claim of consistent outperformance (especially on conflicting cases) requires explicit reporting of baseline implementations, hyperparameter choices, dataset statistics (size, class balance, reaction types), and statistical significance tests with error bars; without these, it is impossible to verify that gains are not due to implementation differences or post-hoc selection.
minor comments (2)
  1. [Abstract] Abstract: 'memoryaugmented' is missing a hyphen and should read 'memory-augmented'.
  2. [Abstract] The anonymous code link is appropriate for review but should be updated to a permanent repository upon acceptance.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and positive recommendation for minor revision. We address the major comment below and will revise the manuscript accordingly to improve clarity and verifiability.

read point-by-point responses

  1. Referee: [Experiments] Experiments section: the central claim of consistent outperformance (especially on conflicting cases) requires explicit reporting of baseline implementations, hyperparameter choices, dataset statistics (size, class balance, reaction types), and statistical significance tests with error bars; without these, it is impossible to verify that gains are not due to implementation differences or post-hoc selection.

    Authors: We agree that these details are necessary to fully substantiate the claims and enable independent verification. In the revised manuscript, we will expand the Experiments section with a new subsection that explicitly describes: (i) the precise implementations of all baselines (including any adaptations from original sources), (ii) all hyperparameter values and selection procedures, (iii) full dataset statistics (total size, class balance, and distribution across reaction types), and (iv) statistical significance tests (e.g., paired t-tests or McNemar tests) with error bars on all reported metrics. These additions will be placed before the main results tables to address concerns about implementation differences or post-hoc selection. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper introduces an empirical agentic framework (ARMOR) that models tool utilities, prioritizes tools hierarchically, and resolves conflicts via memory-augmented reasoning for reaction feasibility prediction. Performance claims rest on experiments against baselines on a public dataset with released code; no equations, derivations, fitted parameters renamed as predictions, or self-citation chains are present in the provided text that would reduce any result to its inputs by construction. The argument is self-contained against external benchmarks and does not invoke uniqueness theorems or ansatzes from prior author work.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities; the framework implicitly assumes that tool utilities can be characterized from patterns and that memory examples are sufficient for conflict resolution, but none are enumerated.

pith-pipeline@v0.9.0 · 5542 in / 1191 out tokens · 61870 ms · 2026-05-11T00:50:37.178031+00:00 · methodology

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Reference graph

Works this paper leans on

34 extracted references · 34 canonical work pages · 1 internal anchor

  1. [1]

    Llama 3 Model Card. (2024). https://github.com/meta-llama/llama3/ blob/main/MODEL_CARD.md SM Aithal and D Upadhyay

    work page 2024

  2. [2]

    Anthropic

    Feasibility study of the potential use of chemistry based emission predictions for real-time control of modern diesel engines.Applied Energy91, 1 (2012), 475–482. Anthropic

    work page 2012

  3. [3]

    Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al

    Dynamic selection of classifiers—a comprehensive review.Pattern recognition47, 11 (2014), 3665–3680. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al

    work page 2014

  4. [4]

    Yash Chainani, Zhuofu Ni, Kevin M Shebek, Linda J Broadbelt, and Keith EJ Tyo

    Language models are few-shot learners.Advances in neural information processing systems33 (2020), 1877–1901. Yash Chainani, Zhuofu Ni, Kevin M Shebek, Linda J Broadbelt, and Keith EJ Tyo

    work page 2020

  5. [5]

    Davide Chicco and Giuseppe Jurman

    DORA- XGB: an improved enzymatic reaction feasibility classifier trained using a novel synthetic data approach.Molecular Systems Design & Engineering10, 2 (2025), 129–142. Davide Chicco and Giuseppe Jurman

    work page 2025

  6. [6]

    The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation.BMC genomics21, 1 (2020),

    work page 2020

  7. [7]

    DeepSeek-AI

    DESlib: A Dynamic ensemble selection library in Python.Journal of Machine Learning Research21, 8 (2020), 1–5. DeepSeek-AI

    work page 2020

  8. [8]

    https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main/ DeepSeek_V4.pdf

    DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence. https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main/ DeepSeek_V4.pdf. Accessed: 2026-04-29. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova

    work page 2026

  9. [9]

    InProceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers)

    Bert: Pre-training of deep bidirectional transformers for language understanding. InProceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers). 4171–4186. Thomas G Dietterich

    work page 2019

  10. [10]

    Quzhe Huang, Zhenwei An, Nan Zhuang, Mingxu Tao, Chen Zhang, Yang Jin, Kun Xu, Liwei Chen, Songfang Huang, and Yansong Feng

    Deep learning for chemical reaction prediction.Molecular Systems Design & Engineering3, 3 (2018), 442–452. Quzhe Huang, Zhenwei An, Nan Zhuang, Mingxu Tao, Chen Zhang, Yang Jin, Kun Xu, Liwei Chen, Songfang Huang, and Yansong Feng

    work page 2018

  11. [11]

    William L Jorgensen, Ellen R Laird, Alan J Gushurst, Jan M Fleischer, Scott A Gothe, Harold E Helson, Genevieve D Paderes, and Shenna Sinclair

    Chemformer: a pre-trained transformer for computational chemistry.Machine Learning: Science and Technology 3, 1 (2022), 015022. William L Jorgensen, Ellen R Laird, Alan J Gushurst, Jan M Fleischer, Scott A Gothe, Harold E Helson, Genevieve D Paderes, and Shenna Sinclair

    work page 2022

  12. [12]

    JungZoona

    CAMEO: a program for the logical prediction of the products of organic reactions.Pure and Applied Chemistry62, 10 (1990), 1921–1932. JungZoona

    work page 1990

  13. [13]

    https://huggingface.co/JungZoona/ T3Q-qwen2.5-14b-v1.0-e3

    T3Q-Qwen2.5-14B-v1.0-e3. https://huggingface.co/JungZoona/ T3Q-qwen2.5-14b-v1.0-e3. Accessed: 2026-04-15. Albert HR Ko, Robert Sabourin, and Alceu Souza Britto Jr

    work page 2026

  14. [14]

    Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa

    From dynamic classifier selection to dynamic ensemble selection.Pattern recognition41, 5 (2008), 1718–1731. Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa

    work page 2008

  15. [15]

    Bhavyahshree Navaneetha Krishnan, Adel Heydarabadipour, and Herbert Sauro

    Large language models are zero-shot reasoners.Advances in neural information processing systems35 (2022), 22199–22213. Bhavyahshree Navaneetha Krishnan, Adel Heydarabadipour, and Herbert Sauro

    work page 2022

  16. [16]

    Kusuri Murakumo, Naruki Yoshikawa, Kentaro Rikimaru, Shogo Nakamura, Kairi Furui, Takamasa Suzuki, Hiroyuki Yamasaki, Yuki Nishigaya, Yuzo Takagi, and Masahito Ohue

    BioModel- sRAG: A Biological Modeling Assistant Using RAG (Retrieval Augmented Generation).arXiv preprint arXiv:2601.22684(2026). Kusuri Murakumo, Naruki Yoshikawa, Kentaro Rikimaru, Shogo Nakamura, Kairi Furui, Takamasa Suzuki, Hiroyuki Yamasaki, Yuki Nishigaya, Yuzo Takagi, and Masahito Ohue

    work page arXiv 2026

  17. [17]

    InAI for Accelerated Materials Design-NeurIPS 2023 Workshop

    LLM drug discovery challenge: A contest as a feasibility study on the utilization of large language models in medicinal chemistry. InAI for Accelerated Materials Design-NeurIPS 2023 Workshop. Sanggil Park, Herim Han, Hyungjun Kim, and Sunghwan Choi

    work page 2023

  18. [18]

    Daniel Probst, Philippe Schwaller, and Jean-Louis Reymond

    Machine learning applications for chemical reactions.Chemistry–An Asian Journal17, 14 (2022), e202200203. Daniel Probst, Philippe Schwaller, and Jean-Louis Reymond

    work page 2022

  19. [19]

    Yujia Qin, Shengding Hu, Yankai Lin, Weize Chen, Ning Ding, Ganqu Cui, Zheni Zeng, Xuanhe Zhou, Yufei Huang, Chaojun Xiao, et al

    Reaction classification and yield prediction using the differential reaction fingerprint DRFP.Digital discovery1, 2 (2022), 91–97. Yujia Qin, Shengding Hu, Yankai Lin, Weize Chen, Ning Ding, Ganqu Cui, Zheni Zeng, Xuanhe Zhou, Yufei Huang, Chaojun Xiao, et al. 2024a. Tool learning with foundation models.Comput. Surveys57, 4 (2024), 1–40. Yujia Qin, Shihao...

    work page 2022

  20. [20]

    Lior Rokach

    Tool learning with large language models: A survey.Frontiers of Computer Science19, 8 (2025), 198343. Lior Rokach

    work page 2025

  21. [21]

    Ohad Rubin, Jonathan Herzig, and Jonathan Berant

    Ensemble-based classifiers.Artificial intelligence review33, 1 (2010), 1–39. Ohad Rubin, Jonathan Herzig, and Jonathan Berant

    work page 2010

  22. [22]

    InProceedings of the 2022 conference of the North American chapter of the association for computational linguistics: human language technologies

    Learning to retrieve prompts for in-context learning. InProceedings of the 2022 conference of the North American chapter of the association for computational linguistics: human language technologies. 2655–2671. Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Christopher A Hunter, Costas Bekas, and Alpha A Lee

    work page 2022

  23. [23]

    Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean

    Molecular transformer: a model for uncertainty-calibrated chemical reaction prediction.ACS central science5, 9 (2019), 1572–1583. Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean

    work page 2019

  24. [24]

    OpenAI GPT-5 System Card

    Openai gpt-5 system card. arXiv preprint arXiv:2601.03267(2025). 11 Rodrigo GF Soares, Alixandre Santana, Anne MP Canuto, and Marcílio Carlos Pereira de Souto

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  25. [25]

    InThe 2006 IEEE international joint conference on neural network proceedings

    Using accuracy and diversity to select classifiers to build ensembles. InThe 2006 IEEE international joint conference on neural network proceedings. IEEE, 1310–1316. Wendy A Warr

    work page 2006

  26. [26]

    Tomasz Woloszynski, Marek Kurzynski, Pawel Podsiadlo, and Gwidon W Stachowiak

    A short review of chemical reaction database systems, computer-aided synthesis design, reaction prediction and synthetic feasibility.Molecular informatics33, 6-7 (2014), 469–476. Tomasz Woloszynski, Marek Kurzynski, Pawel Podsiadlo, and Gwidon W Stachowiak

    work page 2014

  27. [27]

    Feng Yang, Juan Liu, Qiang Zhang, Zhihui Yang, Jianghang Liu, and Guangsheng Wu

    A mea- sure of competence based on random classification for dynamic ensemble selection.Information Fusion13, 3 (2012), 207–213. Feng Yang, Juan Liu, Qiang Zhang, Zhihui Yang, Jianghang Liu, and Guangsheng Wu

    work page 2012

  28. [28]

    Towards global reaction feasibility and robustness prediction with high throughput data and bayesian deep learning.Nature Communications16, 1 (2025),

    work page 2025

  29. [29]

    12 A Tool Set Construction and Training Details Table A1: Dataset for constructing the tool set. Split #Reactions #Feasible #Infeasible Train 200,000 40,000 160,000 Validation 25,000 5,000 20,000 Training DataThe individual tools are trained using the FREAdataset (Yu et al ., 2026), which is derived from the U.S. Patent & Trademark Office (USPTO1). The de...

    work page 2026

  30. [30]

    (1) Classification-based Feasibility Predictors • BERT(Devlin et al ., 2019)

    andT3Q-Qwen-14B (JungZoona, 2025), respectively, as the base LLMs. (1) Classification-based Feasibility Predictors • BERT(Devlin et al ., 2019). We fine-tune bert-base-uncased as a sequence classification baseline for reaction feasibility prediction. Each reaction is represented as a plain text string by concatenating the reactant and product SMILES with ...

    work page 2025

  31. [31]

    These six retrieved reactions are provided to the LLM alongside the query, and the model is asked to make a feasibility judgment informed by the retrieved evidence

    to retrieve the top-3 most similar feasible reactions and top-3 most similar infeasible reactions from the training set via FAISS binary index with Hamming distance. These six retrieved reactions are provided to the LLM alongside the query, and the model is asked to make a feasibility judgment informed by the retrieved evidence. • ChemformerLlama / Chemfo...

    work page 2022

  32. [32]

    DES-KNN estimates these criteria within the k-nearest neighborhood of each test reaction, whereas DES-Clustering evaluates them within cluster-defined regions

    selects tools by jointly considering tool accuracy and diversity. DES-KNN estimates these criteria within the k-nearest neighborhood of each test reaction, whereas DES-Clustering evaluates them within cluster-defined regions. In addition, we include HarderMoE (Huang et al., 2024), which performs dynamic expert routing based on input difficulty. • LLM-base...

    work page 2024

  33. [33]

    We also include closed-source LLMs, including GPT-5.4-mini (Singh et al., 2025), DeepSeek-v4-flash (DeepSeek-AI,

    employs LLM-based scoring to assess tool utility and select tools for each instance, where the same LLM backbone is adopted as inARMOR for a fair comparison. We also include closed-source LLMs, including GPT-5.4-mini (Singh et al., 2025), DeepSeek-v4-flash (DeepSeek-AI,

    work page 2025

  34. [34]

    C Impact of Demonstrations Table A2: Impact of the number of demon- strations (K) on accuracy (ACC (%))

    and Claude-Sonnet-4.6 (Anthropic, 2026), which are prompted to evaluate tool utilities on the validation set and perform tool selection during testing. C Impact of Demonstrations Table A2: Impact of the number of demon- strations (K) on accuracy (ACC (%)). # Demonstration Overall Feasible Infeasible K= 090.80 92.53 89.07 K= 291.1292.5789.67 K= 491.30 92.5...

    work page arXiv 2026