From What to Why: A Multi-Agent System for Evidence-based Chemical Reaction Condition Reasoning
Authors: Cheng Yang
Jiaxuan Lu
Haiyuan Wan
Junchi Yu
Feiwei Qin
Venue: ICLR 2026 Poster (2026)
Keywords: Multi-Agent System，Large Language Model，Evidence-Based Reasoning
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: The chemical reaction recommendation is to select proper reaction condition parameters for chemical reactions, which is pivotal to accelerating chemical science.With the rapid development of large language models (LLMs), there is growing interest in leveraging their reasoning and planning capabilities for reaction condition recommendation.Despite their success, existing methods rarely explain the rationale behind the recommended reaction conditions, limiting their utility in high-stakes scientific workflows. In this work, we propose ChemMAS, a multi-agent system that reframes condition prediction as an evidence-based reasoning task. ChemMAS decomposes the task into mechanistic grounding, multi-channel recall, constraint-aware agentic debate, and rationale aggregation. Each decision is backed by interpretable justifications grounded in chemical knowledge and retrieved precedents. Experiments show that ChemMAS achieves 20–35\% gains over domain-specific baselines and outperforms general-purpose LLMs by 10–15\% in Top-1 similarity, while offering falsifiable, human-trustable rationales, which establishes a new paradigm for explainable AI in scientific discovery.

Published conference paper ICLR 2026
FROM WHAT TO WHY: A MULTI-AGENT SYSTEM FOR EVIDENCE-BASED CHEMICAL REACTION CONDITION REA- SONING
Cheng " Lu², Haiyuan , Junchi Yu', Feiwei Qin 'Hangzoou Dianzi University. ?Shanghai Artificial Intelligence Laboratory, TTinghu University, "University Oxford
ABSTRACT
The chemical reaction recommendation to select proper reaction condition parameters for chemical reactions, which is pivotal to accelerating chemical science. With the rapid develop- ment of large language models (LLMs), there is growing interest in leveraging their reasoning and planning capabilities for reaction condition recommendation. Despite their success, existing methods rarely explain the rationale behind the recommended reaction conditions, limiting their utility in high-stakes scientific workflows. In this work, we propose ChemMAS, multi-agent system that reframes condition prediction as an evidence- based reasoning task. ChemMAS decomposes the task into mechanistic grounding, multi-channel recall, constraint-aware agen- tic debate, and rationale aggregation. Each decision is backed by interpretable justifications grounded in chemical knowledge and retrieved precedents. Experiments show that ChemMAS achieves 20-35% gains over domain-specific baselines and outperforms general-purpose L.Mss by 10-15% in Top-1 I similarity. while offering falsifiable, numan-trustabee rationales, which establishes a new paradigm for explainable Al scientific discovery.
INTRODUCTION
The progress in chemistry has long relied the ability design chemically valid reactions that yield scientific insights (Tu et al.] [2023] [smail et al. (022). Central to this task is selecting proper reaction condition parameters, such as solvent, temperature, catalysts, and reagent ratios, which are pivotal to reaction success, selectivity. and
scalability (Ball al. 2025 Taylor et al.
2023). The traditional approach involves extensive human labor
explore the chemical reaction space, which cannot satisfy the growing demand for efficient and safe chemical synthesis (Lyall- Brookes et al. [2025] et al, 2024 Lee et al. 2025). Recent advances in deep learning and data- driven modeling have opened up new opportunities for reaction recommendation, enabling automated exploration of reaction space and the discovery of novel, scalable synthetic routes with minimal manual intervention (Ali let al., 2024, Liu et al. 2023). Early work typically trains relatively small-scale models, such as graph neural networks (Wu al. (2200) and Transformers (Vaswani al. 2017), from scratch, achieving strong performance when abundant labeled data are available (Wang et al.] (023).
With the rapid development of large language models (LLMs) (Naveed et al. 2025 Zhao et al. 2023}, there has been growing interest in leveraging their powerful reasoning and planning abilities for reaction condition rec- ommendation (Bran al. 22225. Current LLM-based approaches can broadly categorized into retrieval-based
(Zhang et al. 2024b] Chen et al. 2023) and reasoning-based approaches. Retrieval- approaches search for similar reactions from external databases and transfer their conditions to the query reaction, which is usually en- hanced by learned molecular embeddings or unsupervised chemical priors to improve retrieval quality (Andronov) (t al. 2003). In contrast, reasoning- based approaches directly prompt or fine-tune LLMs to infer suitable reaction
conditions from molecular structures or textual descriptions (Qian et al. 2023 Zhou al. 2025), and achieve improved zero-shot and few-shot generalization capabilities.
However, despite their success in predicting plausible reaction conditions, these approaches rarely address the deeper scientific question of why such conditions are appropriate. In the context of scientific discovery, under- standing why is arguably more critical than merely predicting what. reliable system should not only propose a solvent or temperature but also provide a mechanistic justification: Which functional group governs the reac- tivity? What prior experimental evidence supports this choice? Which constraints exclude alternative reagents solvents? Without such explanatory reasoning, models risk being opaque black boxes, limiting their utility in high-stakes scientific workflows.
To tackle this challenge, we introduce ChemMAS, multi-agent system that treats condition selection as a reason- ing task grounded in chemical knowledge, mechanistic constraints, and peer deliberation. ChemMAS decomposes
"Corresponding author: Feiwei Qin (D-mailgiineiiwei@hdn.cc hdu.edu.cn).

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Published conference paper ICLR 2026
Reactant
Product
Chemist
Reaction Base
Gardidate Condition
Aultt-Agent Debate
Main Functional Group
@+C+ @+ ++0+
Reactars 2
Steti-Step
Reasoning
By-product
Selection
Valleg
Knowledge
Evidence
Reaction type
Summary
Schottor Saumann
50 Condition
1
Conversation
Memory
Report
Figure Overview of ChemMAS. collaborative multi-agent system for evidence-based reaction-condition rea- soning from SMILES inputs. ChemMAS demonstrates strong versatility and delivers state-of-the-art performance reaction condition reasoning.
the problem into four collaborative stages. first grounds chemical reactivity via mechanistic analysis, where a general chemist agent parses SMILES to identify functional groups, balance stoichiometry, and infer plausible by- products. The system then retrieves condition exemplars through multichannel queries over structured reaction database. These candidates are refined via a tournament-stylee elimination process, in which agent panels conduct pairwise comparisons using memory- -infore- multi-step reasoning. Finally, ChemMAS aggregates rationales for each decision by combining mechanistic plausibility, retrieved evidence, and constraint checks into interpretable justifications.
By shifting from mere top-k ranking to interpretable, evidence-backed reasoning. ChemMAS offers new paradigm: that is not only predictive but also justifiable, auditable, and suitable for closed-loop experimen- tation. In our evaluation, ChemMAS outperforms specialized chemical models (e.g.. RCR (Gao et al., 2018), Reagent Transformer (Andronov et al. 2023)) by 20-30% Top-1 similarity and surpasses leading peneral-purpose LLMs (e.g., GPT-5, Gemini 2.5) by 10-15% average, validating its effectiveness and robustness.
Our contributions threefold:
We reformulate reaction condition recommendation evidence-basee chemical reaction condition rea- soning, requiring models output not only "what"-leve conditions but also "why"-level evidence.
introduce ChemMAS, a multi-agent system that couples chemistry-aware tool calling with multi- channel recall, multi-step mechanistic reasoning under constraint verification, and dehate-hased aggre- gation, producing interpretable, falsifiable condition reasoning.
We benchmark ChemMAS against specialized chemical models and cutting-edge general-purpose LLMs, showing sfate-of-the-aee art performance with up to 30-point gains in Top-1 similarity and robust generaliza- tion across diverse condition types.
2 CHEMMAS
2. PROBLEM DERNITION
Unlike the existing reaction condition recommendation, formalize evidence-based reaction condition reasoning follows. An input reaction x = (R, P) with reactants R, products P. optional context I. condition configuration is a structured object E C, where may mix discrete and continuous factors. The system returns K configurations č = (C1,...,Co .x and rationale for each p(c) (M, S, E, II) comprising domain reasoning M, verifiable checks S, aligned evidence E, and concise derivation II. Validity is
(p(e) = X((onstr(SS) Align x; c) 8 Coherent(ll,, M, B3)
Here, Constr(S) true when all hard checks in S pass. Align(E; x, c) [o, 1] scores how well the evidence supports (x, c) using signals such as reaction-type matches. functional-group overlap. MCS alignment, or learned embeddings, with 5 fixed threshold. Co M, E)) verifies that the derivation II is logically consistent with the mechanistic summary M and the evidence E. The indicator J returns only when all criteria hold. The

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Published conference paper ICLR 2026
"Reactant":
Memory
Multi-Step Reasoning
-octctejc-ojCkkcl Ccicce((cccaassc33ccc
"Product": Coclcc)[--//N2Ccaa hrtp:o/tteooocccoosssseeeeoeeleo
Reaction Report
judge two options and pick the best one. Behavioral logic: evidence
Goals:
Main
Tools
Input
methow
Relevant Knowleage
Functional Group Tagger
Beactant2:
Keywords
Store
Cithoophece rin.... .....
Constraint Engine
Nb By-product
Summary
Chemical Knowledge
Initial Resporsse
Seneral Chemist
Conversation
Reaction type Schocion Baumann
Debate
Supplementary
Finol Decision
Reaction Type
Reactant
Product
Conversation
Recall
Multi-Agent Debate
Typo
Reactants
Product
Canalsst
Solvent
Reagent
Summary
Type Retrieval
Candidate Condition
Majority Voting
++++
Type
++
++0+O
Matched
Advanced
O
Resctant
C+
Reactent1
+
++
C+
0+0+0
Reactan12
Sinttrr
X Eliminated
++ O
++
Product
+
++
++0+
Top 50
5k
Reaction Base Searching
Chenical Knowledge Bose Searching
Memory Searching
Figure 2: Architecture of ChemMAS. The left side shows how the General Chemist processes SMILES and Multi-Channel Recall retrieves reaction conditions from the Reaction Base. On the right, candidate conditions are paired and evaluated through Multi-Agent Debate, where four agents with Multi-Ster Step Reasoning select the top-50 conditions via Tournament Selection.
objective is
max C.P
Σ u(c;x) + ADv((C) s.t. JC = K, Valid 1 Vc. cgc
(2)
The first term accumulates a success proxy u over selected configurations, where u may be a calibrated yield pre- dictor, feasibility score, learned pairwise preference aggregator. The diversity term Div promotes coverage across condition dimensions to avoid mode collapse, controls trade-off between utility and diversity. The constraints enforce fixed budget K and require every selected configuration to be valid, upgrading recommenda- tion to reasoning by demanding justified and verifiable outputs. Classical recommendation optimizes only. Our task requires each proposed to carry a falsifiable, evidence-alignes certificate p(c).
2.2 OVERVIEW
As illustrated in Figure ChemMAS realizes the proposed reasoning framework through multi-stage agent- based pipeline, with intermediate representations stored a shared memory. The process begins with General Chemist that parses the input reaction (R, P) using domain-specific tools to extract mechanistic signals, align stoichiometry, and predict reaction type. Outputs structured into Reaction Report written to memory. Con- dition hypotheses are generated via the Multi Channel Recall module, which independently queries historical condition database using reaction type, reactant, and product features, followed by combinatorial synthesis into candidate sets of similar conditions. The Tournament Selection phase ranks these candidates through pairwise comparisons conducted by specialized agents, each focusing on one condition dimension (e.g. catalyst, solvent, reagent) under context-aware constraints. Finally, each agent engages in Multi-Step Reasoning over memory and retrieved evidence. and the Multi-Agent Debate aggregates these judgments via majority voting to produce K verified configurations (c.....,Cy) ). each paired with a rationale p(c).

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Published conference paper ICLR 2026
2.3 GENERAL CHEMIST
Given a chemical reaction specified by Reactant SMILES R= = {rz} and Product SMILES P {ps}. the General Chemist (Agen) extracts mechanistically informative priors for downstream condition prediction. The General Chemist agent orchestrates three tools, including Functional Group Tagger, Constraint Engine, and Chemical Knowledge Base, to (i) identify main functional groups, (ii) infer balanced stoichiometry and by-products, and (iii) retrieve reaction-type evidence. All outputs are written to Memory.
Functional Group Tagger. A curated library IL {(nameg, SMARTS;)) of common organic motifs (e.g., acyl chlorides, amines, alcohols, heteroaromatics) is used to match each r; via SMARTS substructure search, yielding F(r,). The union FR U. F(r,) is then ranked by role salience considering electrophile/mulleophie tags, activation levels, and motif frequency across reactants. The top-ranked entries are designated as the Main FG set and stored in Memory with atom indices for downstream reference.
Constraint Engine. Reactant and product molecular graphs are canonicalized (including implicit hydrogens), aligned by maximum common substructure to derive an atom mapping. An integer linear program computes stoichiometric coefficients group rules, are used to enumerate neutral species B, from which the most parsimonious by-product hypothesis selected. Both the balanced equation and consistency diagnostics are written to Memory.
(MR, Mp, Vaux). Changes mapped atoms, combined with heuristic leaving-
Chemical Knowledge Base. Query templates built from FR. product scaffolds, and molecular identifiers are used to retrieve supporting evidence from public repositories (e.g., PubChem) and locally indexed mirror. Re- trieved exemplars and co-occurrence statistics yield signal features Sekb = {Styper Szole: Sbyprod }. which support reaction type classification and by-product confirmation. The resulting labels, along with citation metadata, are stored in Memory for use in later reasoning stages.
2.4 MULTI-CHANNEL RECALL
We maintain structured Reaction Base D [(TT. Fn: Pn: Gn)]y= where each entry contains the reaction type Tay molecular representations of reactants r, and products Pn, and condition triple c, = (cal, sol, reag). Given the current reaction context (f, R, P) from Memory, we perform three parallel queries including type-, reactant- and mroduct-centric, to obtain candidate index sets S,, Sr,,S, (exact type match for S, top-k nearest neighbors by functional- group, MCS, and embedding accuracy for S, and Sp). Without any scoring or rank fusion, an entry admitted into Matched Conditions if it hits on any of the three tags. We define the unified retrieval result as the deduplicated union:
Smatched dedup(S, US)),
(3)
Smatched) } as experience-driven condition proposals. Optional feasibility filters, egg. mass/charge balance, known by-product constraints, can be applied to screen out invalid entries. To promote diversity, we construct Similar Conditions via applying controlled slot-level recombination II(c) that replaces one or two elements of e with high co-occurrence alternatives conditioned on (f, FR), while removing infeasible or near-duplicate combinations. The overall candidate pool the truncated union:
and collect (c,,
truncate:000) ( Smarched Ssimilar),
which is forwarded to downstream selection and debate.
2.5 CANDIDATE PAIRING AND TOURNAMENT SELECTION
We refine the initial pool of 5,000 Candidate Conditions into final Top-50 via tournament-style knock- out that emphasizes head-to-head preference (Liu et al., (025) under comparable context rather than brittle global scoring. Let C {c, 13000 We apply random permutation and form disjoint pairs p(D) ((C=(1): (r ..... (199)) (5000))))) }. In round f, each pair (a, b) p(t) is adjudicated by an agent panel, and the winner is determined by majority vote:
win(a, b) arg max
Eurl, o).
(5)
(5(a.b)
with a confidence-sum tie-break when necessary. Winners form W( = {win(a, (a, b)}. which is reshuffled and re- re-paired to yield p((+1) pair(shufle(W(())). Iteration stops when [,i(2)] 50. We prefer this pairing-and- knockout protocol to global scoring since absolute scores are difficult to calibrate across heterogeneous condition sets and amplify noise in near-ties; head-to- head comparison avoids global calibration, anchors judgments in matched contexts, and affords inear-time selection with natural parallelism.

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Published conference paper ICLR 2026
2.6 AGENT-AGENT DEBATE
Multi-Step Reasoning. For a candidate option o E {a, b). each agent Apull, ACot: A Area executes an evidence-seeking chain. The agent parses the Memory Reaction Report (main functional groups, by-product, reaction type) to extract keywords Kj. queries the Chemical Knowledge Base to obtain support O (o), and composes initial assessment
[ni LLM (o), structured format).
(6)
Across micro-rounds O, ..... U - the agent refines its stance by reading peer summaries from the conversa- tion buffer and re-querying when uncertainty detected:
(u+1) (uc (o) - of Dee (1) (o)) (eers!") (
Dec
where Φ(-) integrates new citations, Constraint Constraint-Engine checks (e.g., base required to capture HCI), and potential failure modes. Upon convergence or budget exhaustion, the agent outputs final decision dj {a, b) with rationale saved Memory.
Majority Voting. After each agent completes Multi-Step Reasoning for both a and b, the panel engages in a structured debate: agents post final assessments and key citations to a shared Memory board, while a designated facilitator enforces turn-taking and prompts resolution of conflicts (e.g., solvent polarity nucleophile strength). The pairwise outcome is determined by majority voting as in
win b) arg max
Iddd o). (a.bb
(8)
with confidence- sum breabreaks if needed. The winning option advances to the next tournament round, losers are eliminated, and iterating over reshuffled winners progressively reduces the 5k candidates to the Top-50.
TWO-STAGE MULTI-TOOL COLLABORATIVE TRAINING FRAMEWORK
3.1
CHEMICAL TEACHING
We adopt cold-start Supervised Fine- Tine-Tunig (SFT) recipe to endow the backbone LLM with initial Tool- Integrated Reasoning (TIR) (Dong et al. (2025) for chemical condition judgment. Given training pairs (x,, (6). we apply the standard Supervised Fine-tuning objective on the backbone model Pe with parameters
((0)
Σ (1).8))
log (o mx))
where x; denotes the input prompt containing a reaction and paired candidate conditions, and yi is a structured target consisting of (i) vi step-wise chain that incorporates tool invocation logic and special tokens. (ii) y concise Judgement section that independently critiques each response and declares the preferred option. The reasoning trajectory integrates two types of tools, namely Chemical Knowledge Base searching and Memory searching, serialized in special formats (e.g., (sscarcn>).. (//p//////r/reeeee ren). <m.nory3).... [5/mmmm ry>),, enabling the model learn the fundamental rules of tool invocation during the SFT process. Ultimately, this process yields a cold-start LLM ft that learns when and how to invoke chemical tools, thereby establishing an initial capability for TIR in chemistry.
3.2 TOOL INCENTIVIZATION
After obtaining the cold-start model à via SFT, we apply tool incentivization RL. to align the policy with both answer correctness and collaborative tool usage, obtaining RR.
Hierarchical Reward. Given valid format, augment task accuracy Acc with multi-tod bonus r'M when both tools appear (Dong et al. 2025), otherwise we weight:
max(Acc + TM,, Acc), Format ok and Acc > 0, Format ok and Acc 0,
0,
-1,
Otherwise,
(10)
0.1,, 3 (<search>)
<memozy>
TM =
0.
otherwise.
This explicitly rewards combined tool use without sacrificing correctness.

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Published conference paper ICLR 2026
Stage I: Chemical Teaching
Stage 2: Tool Incentivization
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Figure Two-stage Multi-tool Collaborative Training Framework of ChemMAS. Chemical Teaching uses SFT for cold-start training, enabling the LLM to master TIR, and Tool Incentivization employs RL to align the model's s policy with both answer correctness and collaborative tool usage.
Chol-Incentivizaation RL. For each query and tool-augmented output o, we adopt Group Relative Policy Opti- mization (GRPO) (Shao et al. 20244) as our RL algorithm, which extimates the buseline using a group of rollouts. Concretely, we sample G rollouts {o compute group- normalzore advantages with group baseline, and optimize
lo.1
(0) = E
min (p. clip(p,,, 1, e) DKI,fft |l ftret]
(11)
0g
where
no(o,, q, 0i,c()
Pa. (0)
(12)
(o., q 0i,<t))
controls PPO clipping, 3 weights the KL regularization to the fixed reference feef, and A., denotes the advantage normalized with respect to the group baseline.
EXPERIMENTAL SETTINGS
4.1 TRAINING AND EVALUATION SETTING
All agents in ChemMAS are initialized from the same backbone, Deven3-8B-Instrrn and are trained under a unified Two-stage tool Collaborative Training Framework that applies SFT and RL; while the optimization protocol is shared, the learning objectives and accessible tools differ across agents. We independently trained distinct models: one for the Ageos and another for the multi-agent system comprising AFulls Açats Asol, ARea- More training details are in the Appendix.
We measure performance using Top-k Similarity, defined as the maximum Tanimoto similarity between the ground truth and top-k predicted candidates, averaged over composite of molecular fingerprints (Path-based, MACCS, Morgan). This metric reflects the best structural match retrieved by the model. Details are in the Appendix.
4.2 DATASETS
We curate a private dataset of organic reactions, consisting of 544,591 entries represented as reaction equations SMILES format. For each entry, the reactants and products defined as the input, while the reaction conditions, including catalystl, solventl, solvent2, reagent), and reagent2, are defined the output. Based on this setting, we construct question-anse answer pairs and split the dataset into training. validation, and test sets with a ratio of 8:1:1.
Furthermore, we incorporated the RCR subset of ChemCoTBench (Li et al. 2025) as a lightweight public bench- mark for small-scale evaluation. This subset of 90 tigh-tuality serl-structured reaction-Cone condition reaction-condition QA instances allows us assess system generalization and stability under distribution shift. Further details on the private dataset and the ChemCoT CeannhhBBehh--ee BeBBBenh-CCC RCR subset are provided in the Appendix.

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Published conference paper ICLR 2026
LLM--acre Pipeline
120 14.7%
LLM as Judge
VB
Model OUTgUT
MerYC
Mabric
Foaetee Tyee
Repart
Figure Model Interpretability Evaluation and Scoring Methodology. (Left) Accuracy of ChemMAS outputs compared human expert annotations. (Center) Human alignment performance comparison: blue bars indicate LLM-Scores and green bars indicate BLEU-4 scores. (Right) Schematic representation of the LLM-Score pipeline and the question-answering based evaluation workflow.
RESULTS AND DISCUSSIONS
5.1 MAIN RESULTS
We assessed our proposed method, ChemMAS, against selection of current models. We compared with special- ized chemical models including RCR (Gao et al. 2018), Reagent Transformer (Andronov et al. (023), and MM RCR (Zhang et al. 2024b), which represent the latest advances in reaction-specifie prediction. In addition. we benchmarked against general-purpose large language models (LLMs), such as Qwen3-23333
(Yang et al. 2025), GPTS (OpenAl), Claude 3.7 Sonnet (Anthropic 2024). DeepSeek-R1 (Guo al. 2025), and Gemini2.5- Pro (Comanici et al. 2025), which epitomize the cutting edge in general reasoning and knowledge transfer.
As shown in Table 2 ChemMAS surpasses both spe- cialized chemical models and state-of-the-ard LLMs across all reaction types and Top-k settings. achieves relative Top-1 similarity improvements ranging from 70% to over 90% when compared Nomain-specifte baselines such as RCR, Reagent Transformer, and MM RCR. Even against top-tier general-purpose LLMs like GPT-5 and Gemini 2.5-Pro, ChemMAS yields consis- tent relative gains 15-25% Top-I similarity, un- derscoring its strength in fine-grained mechanistic rea- soning.
Table 1: Generalization evaluation on ChemCoTBench.. Top-k similarity (%) for k E (1. 5, 10). The best and second-best results are bolded and underlined. Green values in parentheses show relative improvements over the second-ber results.
Top-k Similarity (%)
Model
Catalyst
Solvent
Reagent
10
5
10
5
10
Zero-shot LLMs
Qwen2-23BB-A23B
40.1
53.1
58.6 36,4
41.1
52.9
36.4
50.
58.7
GPTS
41.9
59.2
66.1
44.1
57.5
65.2
40.1
55.1
61.1
Claude3.7 Sonnet
38.5
56.2
59.1
40.4
52.1
61,2
34,3
48.0
54.3
5.2 GENERALIZATION
DeepSeek-Ri RI
39.7
55.6
62.0
38.4
48.3
56.3
35.2
47.6
55.4
EVALUATION ON OUT-OF- DISTRIBUUIN DATA
Gamini2.5-Pro
45.6
62.1
69.5
42.1
58.6
71.2
38,9
52.1
59.8
ChemMAS
62.1
68.3
76.1
57.8
66.5
76,8
51,2
59.1
67.7
To rigorously evaluate the generalization capability of our framework and assess chemical reasoning in out- of-distribution (OOD) scenarios, conducted addi- tional experiments on ChemCoTBench, a standardized benchmark. The primary objective this experiment to verify that ChemMAS does not merely rely retrieving near-duplicate samples from the knowledge base, but truly possesses the ability perform robust reasoning on novel reaction types.
(+16.5) (46.2) (46.)
(+137)
(47.)
(-5.)
(+I1.))
(+4.0)
(468)
Specifically, the challenging Top-1 setting, as shown in Table [ ChemMAS achieves significant accuracy advantage. For catalyst prediction, ChemMAS attains an accuracy of 62.1%, surpassing the second-best model (Gemini 2.5-Pro) by a margin of 16.5%. Similarly, for solvent and reagent prediction, ChemMAS outperforms the strongest competitor (GPT5) by 13.7% and 11.1%, respectively. These substantial performance gains on OOD data demonstrate that our ChemMAS framework effectively generalizes beyond the training distribution, exhibiting fine-grained mechanistic reasoning rather than relying solely on memory retrieval.
5.3 EVALUATION OF MODEL INTERPRETABILITE
To ensure the interpretability of ChemMAS, we conducted a two-level evaluation focusing mechanistic ground- ing and reasoning quality. First, we validated the intermediate outputs of the General Chemist against human ground truth (Figure 4 Left). The agent demonstrates high reliability, achieving accuracies of 95.8% (MainFG), 90.2% (By-product), and 52. (Reaction Type). consistently surpassing the 90% threshold. This high alignment confirms that the system builds its downstream reasoning on a correct and verifiable mechanistic understanding.

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Published conference paper ICLR 2026
Table 2:
Main results on the private dataset. We report the Top-k similarity (%) across five reaction condition types: catalyst, solventl, solvent2, reagentl, and reagent2. Results are evaluated at & E (1,5, 10). The best and second-best results are bolded and underlined. Green values in parentheses indicate relative improvements over the second-bes results.
Top-k Similarity (%)
Model
Catalyst
Solvent1
Solvent2
Reagent1
Reagent2
5
10
10
Pretrained Models
RCR
40.3
52.6
60.7
49.9
62.1
68.5
45.3
52.8
60.3
50.1
56.2
63.3
36.4
43.3
44.9
Reagent Transformer
35.3
49.3
56.6
38.2
46.3
52.3
37.7
46.4
54.3
46.3
61.3
64.2
37.9
40.1
47.2
MM RCR
43.4
60.1
75.9
53.7
70.7
73.7
49.3
56.3
65.6
55.7
65.2
71.6
40.2
56.3
59.6
sero-shot LLMs
Owen3-235B-AMe A22B
55.4
75.2
77.9
64.0
70.6
73.7
48.4
58.6
64.2
68.3
76.2
82.7
44.2
57.7
60.2
GPT5
62.7
74.2
83.2
73.7
83.7
86.2
65.9
74.3
83.6
67.2
86.9
90.1
68.4
84.9
86.1
Claude3.7-Somme
43.6
52.9
60.1
46.0
55.7
58.7
39.2
45.7
53.9
52.3
63.9
67.1
46.2
52.3
54.7
DeepSeek-RI
52.8
69.4
73.2
67.2
73.5
78.1
45.2
54.9
62.2
60.4
71.4
75.7
53.6
67.6
72.3
Gemini2.5-PMe
63.4
79.4
80.5
68.0
83.6
86.4
63.1
74.0
78.6
64.3
82.6
90.1
63.7
76.8
82.2
ChemMAS
78.1
92.3
96.3
85.4
93.9
96.9
76.3
83.2
93.1
88.3
93.6
94.3
73.6
85.2
87.7
(+14.7)
(+12.9)
(+13.1) (+11.7) (+10.2) (+10.5) (+10.4) (+8.9) (49.5) (+20.0) (+6.7)
(+4.2) (+5.2) (+0.3) (+1.6)
Table 3: Ablation on different components in ChemMAS. The best and second-best results are bolded and underlined.
Top-k Similarity (%)
Method
Catalyst
Solvent
Solvent 2
Reagent
Reagent 2
5
10
5
10
1
5
10
5
10
5
10
w/o Main FG w/o By-Product w/o Reaction Type
66.7 82.6 87.6 65.9
76.3
82.7
63.1
70.5
76.8 64.1
76.9
87.6
60.7
65.7 72.3
Memory
70.3 88.4
90.1 78.4
84.1
89.6
69.7
76.0
85.9
74.5
82.8
90.1
68.2
74.9
81.6
74.6 88.6 92.5 82.4
91.6
93.8
73.8
78.6
86.9
81.6
90.3
92.0
70.0
78.1
85.3
w/o Multi-Agent Debate
65.7 77.9 80.1 66.2 74.1 80.3
58.3
68.2
74.6 62.9 75.6 80.1 52.6 62.0 69.8
Framework w/o Multi-Step Reasoning
62.4 79.8 83.5 70.5 79.3 87.5 62.5 72.5 81.3 69.1 84.3 87.2 61.3 72.5 79.8
w/o Candidate Pairing
74.1 89.7 92.6 81.6 90.1 92.5 72.8 80.4 89.8 84.2 89.3 91.5 71.4 79.4 82.8
ChemMAS
78.1 92.3 96.3 85.4 93.9 96.9 76.3 83.2 93.1 88.3 93.6 94.3 73.6 85.2 87.7
Building on this foundation, we assessed the quality of the generated reasoning trajectories using a dual-metric framework comprising BLEU-4 and semantic LLM-Score (Figure Right). The LLM-Score employs an "LLMMas-a-Judge" mechanism to verify if the generated rationale logically supports expert-derived QA pairs. As general-purpose purpose LLMs, achieving a superior L.M--Scre of 92.8 compared the 62.5-77.2 range of baselines like DeepSeek-RI and GPT-5. This substantial gap, alongside a BLEU-4 score 0.26, demonstrates that ChemMAS generates scientifically sound explanations rather than merely plausible text.
shown in Figure E (Center), ChemMAS significantly outperforms general
5.4 ADDITIONAL QUANTITATIVE ANALYSIS
Ablation Studies. We conducted ablation study analyze the contribution of different components in Chem- MAS. The ablation settings are as follows: (I) w/o Main FG, w/o By-Product, and w/o Reaction Type denote removing the corresponding elements from the Memory module; (2) w/o Multi-Agent Debate replaces multi- agent collaboration with single-agent reasoning process, thereby eliminating conversational exchanges; (3) w/o Multi-Step Reasoning removes the iterative evidence-based reasoning chain within each agent, such that agents can only rely on prior knowledge and inter-agent debate without tool invocation; (4) w/o Candidate Pairing dis- cards the pairwise elimination mechanism for candidate conditions, instead applying a global scoring and ranking procedure to directly select top-50 candidates. As illustrated Table removing key components leads substantial performance drops, underscoring their critical role in ChemMAS. Specifically, removing Main FG from the Memory module results in significant decrease in performance. with an average drop of +8.4% across all reaction conditions, highlighting the crucial role of functional group extraction and analysis reaction con-

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Published conference paper ICLR 2026
Table 4: Ablation study on the SFT, RL, and specific components of the hierarchical reward function, including Acc and TM. The best and second-best results are bolded and underlined.
Top-k Similarity (%)
Training
Catalyst
Solvent
Solvent
Reagent
Reagent
Framework
5
10
5
10
5
RL
70.6
88.3
90.4
82.6
90.5
71.2
80.4
88.5
87.5
90.2
70.2
82.3
w/o SFT
67.9
84.3
90.5
81.3
84.6
88.4
72.6
78.1
87.4
79.2
83,5
91.9
67.7
80.9
83.2
w/o Acc
72.6
90.8
93.7
84.1
91.8
92.1
76.0
81.6
91.3
90.1
92.0
72.5
84.1
86.0
w/o TM
71.9
89.5
91.2
83.8
91.5
91.0
73.5
81.5
88.7
84.6
88.6
90.8
71.6
82.9
84,9
SFT+RL
78.1
92.3
96.3
85.4
93.9
96.9
76.3
83.2
93.1
88.3
93.6
94.3
73.6
85.2
87.7
Analysis Multi-Agent Collaboration (Top-1)
022
Alw AuA
Ara
Ace
AT000
Adra
Pull Syetem
Top- Accuracy
40
Cacope
Sptpeit
Sabeet
Arotent
Figure 5: Multi-agent ablation: Top-1 similarity improvements across Catalyst, Solvent and Reagent when adding specialized agents on top of Agen+ AFul-
dition prediction. Similarly, removing Multi-Step Reasoning causes an average similarity decrease of 12.3%, underscoring the importance of evidence-based multi-round reasoning.
To evaluate our framework, we ablated SFT, RL. and specific hierarchical reward components. As shown in Table 4 removing SFT RL significantly degrades Top-k Similarity across all conditions. Notably, excluding SFT causes a larger drop than removing RL, underscoring the importance of SFT for initialization. We further investigated the reward terms in Eq. (10) by removing task accuracy (Acc) and the multi-tool bonus (rM)) Results show that ablating 7M impairs performance, validating the explicit reward for combined tool usage. Similarly, excluding Ace degrades results, confirming that prioritizing correctness is essential. These findings validate our two-stage framework and hierarchical reward design, where all components play complementary roles.
Analysis of Multi-Agent Collaboration. To assess the utility and synergy of different agents, we evaluate com- binations built the base AAentAPull which listed in Figure 5 Introducing specialized agents yields improvements. Specifically, Acat enhances performance on Catalyst, with an average Top-I increase of 8.5%. Asat shows strong contributions on Solvent1/2, with an average Top-I gain of 11.6%. Anea provides the largest gains Reagent1/2, with an average Top-1 increase of 18.4%. When all three specialized agents incorpo- rated, the full system achieves macro-average Top-1 increase 16-19% across all condition types. These results show that the specialized agents contribute substantial, domain-aligned improvements, and multi-agent debate is conducive enhancing overall performance. For the analysis of Top-5 and Top- 10, see the Appendix.
CONCLUSION
We introduce ChemMAS, multi-agent system reframing reaction condition recommendation as evidence-based reasoning grounded in domain knowledge, mechanistic constraints, and interpretable evidence. Unlike prediction- only baselines. ChemMAS explains why conditions are appropriate, enhancing trust and utility. Empirically, achieves up to 30% Top-I similarity gains over specialized models and outperforms general LLMs. These results validate the transition from black-box predictions auditable decision-making Future work will extend this framework broader domains such as materials design and bioinformatics.

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Published conference paper ICLR 2026
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Supplemental Material of ChemMAS
This document provides supplementary material complement the main paper. It includes detailed descriptions of the ChemMAS system, prompt templates, training pipeline, additional experimental results, and reproducibility assets. Specifically;
Appendix A describes how large language models (c.g., GPT-5 and Google Nano Banana) were used writing assistance and figure generation.
Appendix provides the reproducibility statement and access link to the code and data repository.
Appendix summarizes related works in three areas:
Appendix
C.1
Reaction Condition Prediction
Appendix
LLM-Based Multi-Agent Systems
Appendix
LLM-Based Reasoning Models
Appendix details the ChemMAS methodology, including:
Appendix D.I
Algorithm of ChemMAS Framework and Multi-Agen Agent Debate
Appendix D.I Two-Stage Multi-Tool Collaborative Training
Appendix outlines the experimental settings, including:
Appendix
Training Pipeline Agents
Appendix
Evaluation Setting Details (Candidate Ranking)
Appendix details the evaluation protocol and metries:
Appendix
SMILES Canonicalization and Validity
Appendix
Tanimoto Similarity
Appendix
Molecular Fingerprint Types
Appendix
Aggregate Evaluation Metrics and Top-k Similarity
Appendix G
introduces the dataset details:
Appendix Public Dataset (ChemCoTBench RCR subset)
Appendix Private Dataset euration and statistics
Appendix presents the prompt templates for different agents.
Appendix
[oppendix[preeeeeeee presents additional experimental results discussions:
Appendix L.] Additional Quantitative Results (Top-5 and Top-10 Analysis)
Appendix Result Visualization and qualitative analysis
A THE USE OF LARGE LANGUAGE MODELS
In this work, the large language model GPT-5 used general- purpose tool for polishing the writing, in- cluding improving clarity and grammar. In Figure 2 the five images representing the agents and small tool icons were generated with the assistance of GPT-s while the overall framework created by the authors. The three
images representing different models Figure were produced with the help of Google Nano Banana The conceptual design of both figures were entirely implemented by the authors.
REPRODUCIBILITY STATEMENT
We provide complete code, part of data with instructions, which are available at (Coee
C RELATED WORKS
C.1 REACTION CONDITION PREDICTION
Predicting reaction conditions from reactants and products long-standing challenge in computer-aided syn- thesis. Early large-scale efforts such as (Gao et al. 2018) used feedforward neural networks trained on millions of Reaxys records to jointly predict catalysts, solvents, reagents, and temperatures, achieving promising top-k
'hutss//chattpp..com/ httpp::/wwww.nnoooomaaa banana...i

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Published conference paper at ICLR 2026
accuracies despite sparsity and label imbalance. Focusing on cross-coupling families, (Maser et al. 2021 for- mulated the task as multi-label ranking, developing role-specific encoders and leveraging graph-based features to yield accurate, context-aware predictions. To improve generalization and interpretability. (Wang et al.) 2023)
released benchmark datasets and proposed Parrot, a Transformer model augmented with unsupervised reaction center learning. Parrot achieved significant gains in condition similarity and temperature estimation while offer- ing interpretable attention maps localized to reactive substructures. Separately, (Andronov et al. (023) addressed data quality limitations by training Molecular Transformer to impute missing reagents in USPTO reactions. Their system not only improved reagent recall but also enhanced downstream product prediction models.
Retrieval-augmennee -ugmmented methods incorporate external knowledge to improve robustness. TextReact (Qian et al.) 2023) pairs structure-based encoders with retrieved literature snippets to inform condition prediction and ret- rosynthesis. By integrating textual context into training. significantly outperforms molecule-only baselines. In peptide catalysis design, (Edwards et al. (022) proposed semi-automated ML framework for selecting universal catalyst libraries and discovered novel, high-selectivins peptides via efficient search in large tripeptide space. At the interface of language and chemistry, (Edwards et al., 2022) introduced MolTS, a pre-trained encoder-decoder model that translates between molecules and natural language. It supports molccule-to-caption generation and chemically constrained lext-to-molecule synthesis, offering foundation for LLM-based explainability. More recently, (Zhang et al.] 2024b) proposed a text-augmented multimodal L.LM framework for reaction condition recommendation. Their method jointly encodes SMILES, molecular graphs, and relevant text to achieve state-of- the-art similarity across open benchmarks and improve generalization under low-data OOD settings. Despite these advances, current methods primarily focus on recommending what the potential reaction conditions are, but fail to provide explanatory why-level evidence for why such conditions are important or mechanistically justified.
C.2 LLM-BASED MULTI-AGENT SYSTEMS
LLMs are increasingly deployed as autonomous agents equipped with retrieval, reasoning, and tool-use capabili- ties. (Boiko et al. 2023) showcased early efforts in autonomous laboratory control, with LLM agents performing iterative web search, experimental planning, and execution. istry by coupling GPT-4 with 18 specialized tools for retrosynthesis, property prediction, and literature search. The resulting system could autonomously complete multi-step syntheses and identify new chromophores. In reaction condition recommendation, (Chen al. (0023) leveraged strieval-auggmenter generation by combining molecular similarity search, literature parsing, and in silico condition evaluation, mimicking the workflow of expert chemists.
(M. Bran et al., 2024) extended this direction in chem-
To address hallucinations and unreliable reasoning, multi-agent collaboration has emerged as a promising direc- tion. (Du et al., 2023) proposed multi-agent debate framework where LLMs iteratively critique each other's ss answers, leading improved factuality and robustness. (Zhu al. 2025) benchmarked agent interactions across collaborative and competitive settings, revealing that structured debate and agent role specialization improve task success. Recent work further explores coordination protocols. (Kaesherg et al. 22255) found that consensus-based based decision-making outperforms majority voting on complex QA tasks, while (Zhang et al.. 2024a) introduced a compression pipeline that reduces inter-agent communication by up to 70% without degrading performance. (Wul let al. 2025) introduced Agentic Reasoning, general framework for LLMs to call sub-agents (e.g., web search, code execution, memory management), enabling long-horizon, tool-rich scientific workflows. Together, these systems demonstrate that combining L.MSs with external tools, structured memory, and agent-level reasoning can produce scalable, verifiable pipelines for high-stakes domains. However, how to enhance the factuality and reliability of reaction condition prediction remains largely unexplored.
C.3 LLM-BASED REASONING MODELS
A complementary line of work focuses improving the reasoning capabilities of LLMs, which is essential for high-stakes sreision-making and interpretability in scientific domains. general contexts, program-aided language models (PAL) (Gao et al.) 2023) execute intermediate logic through code to improve arithmetic and symbolic reasoning. CoT prompting, reff-consistency, and debate-style prompting have shown broad benefits in suet question answering. CoMAT (Leang et al. (224)) proposes mathematically annotated chain-of- thought mechanism to handle complex symbolic queries. MME-CoT (Jiang et al. (2255) benchmarks the reasoning abilities of large multimodal models across science, math, and logic domains. In chemistry, (Tang et al.) 2025) introduces a self-updating subtask library to facilitate memory- augmentd chemical reasoning. It decomposes complex tasks into reusable subtasks and retrieves relevant solutions, enabling LLMS to generalize over time via experience. However, the ability infer mechanistic or contextual rationales behind chemical reaction conditions rarely addressed in existing works.
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D METHOD DETAILS
D.1 ALGORITHM OF CHEMMAS FRAMEWORK
Multi-Agent Debate. In this section, we outline the overall workflow of our Multi-Agent Debate procedure. The process consists of two coordinated phases executed for each candidate pair, as illustrated in Algorithm I (see also the prompt specification in Figure 8):
(1) Evidence-Seeking & Refinement. Given pair (a. each agent A, initializes evidence-seeking chain by parsing the Reaction Report (main functional groups, by-products, reaction type) to extract keywords, querying the Chemical Knowledge Base for citations, and composing an imitial assessment. Across U micro-rounds, agents iteratively refine their stance by (i) reading peer summaries from the shared buffer, (ii) re-querying the KB when uncertainty is detected, and (iii) invoking the Constraint Engine (e.g., verifying that bases are present to capture HCI). This yields a final per-agent decision dj E {a, b) with confidence and citations.
(2) Panel Aggregation & Tournament. After convergence, all agents post their final assessments to the Memory board. The pairwise winner determined by majority voting; ties are broken by the sum of confidences. Winners advance while losers are eliminated, and repeated rounds over reshuffled winners progressively reduce the pool to the Top-50. This debate-driven pipeline promotes cross-agent verification, encourages tool-grounded reasoning, and produces interpretable, citation-backen outcomes archived in Memory.
Two-Stage Multi-Tool Collaborative Training. In this section, outline the overall workflow of our Two- Stage Multi-Tool Collaborative Training pipeline. The procedure alternates two phases over multiple cycles, as illustrated in Algorithm 2 (see also the prompt specifications in Figure 7 and Figure 8):
(1) Chemical Teaching (SFT). Starting from the Qwen3-8B -a----B--nnstete backbone, we perform supervised fine- tuning structured trajectories that serialize tool invocations (e.g., search, memory) before the final label. This phase teaches the model when and how to call tools and enforces a standardized output format, yielding a cold- start, tool-aware policy it.
(2) Tool Incentivization (RL). Initialized from èe. we optimize the policy with GRPO using a hierarchical reward that jointly encourages (i) format validity, (ii) answer correctness, and (iii collaborative multi-tool usage. For each query. the model samples G tool-augmented rollouts; advantages are normalized with a group baseline and regularized by a KI. term to a frozen reference. Policy parameters are then updated to maximize the GRPO objective.
This alternating scheme combines supervised teaching of tool protocols with reinforcement alignment for similar- ity and collaboration, resulting in robust tool-aware reasoning model = with interpretable, consistent behavior.
E EXPERIMENTAL SETTINGS
E.1 TRAINING PIPELINE
For both Agen and the multi-agent system, we employ a two-stage optimization strategy consistent with the main framework. In the SFT stage, the AdamW optimizer is used with 3 = (0.9, 0.95). an initial learning rate of 2 x 10-5, and weight decay of 0.1. Each model is trained for one epoch with a batch size of 128. In the subsequent RL stage, we adopt the GRPO strategy with learning rate x 10 6 KL coefficient 0.04, and number of iterations set to I. To enhance diversity, we set the temperature parameter to 0.75 during generation. All training and inference are conducted on 8 NVIDIA A100 GPUs.
General Chemist (Agen). The input is limited to Reactant and Product SMILES, and the output is the predicted Reaction Type. During SFT, the supervision target is structured as a step-wise chain that explicitly serializes three tool invocations-Functiii
Group Tagger, Constraint Engine, and Chemical Knowledge Base Searching- before emitting the final reaction type. This design enables the model to learn when and how to call tools. In the subsequent RL stage, we apply a hierarchical reward that integrates format correctness, answer similarity, and collaborative multi-tool usage.
Multi-Agent System (AFwll, Acat, Asol, ARea). These role-specialized agents share the same trained back- bone and are SFT on QA pairs generated in the Candidate Puiring stage. The supervision targets embed the invocation logic of two tools-Chemen Chemicall Knowledge Base Searching and Memory Searching. The RI. stage em- ploys the same reward design to align both judgment quality and tool collaboration, ensuring that agents deliberate effectively while remaining awar.
15

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Published conference paper ICLR 2026
Algorithm Ageti-Agent Debate with Multi-Step Reasoning and Majority Voting
Require: Agent {A1....; J: Candidates
Memory: Reaction Report (main__g. by-product, reaction-typel)
Chemical Knowledge Base (KB); Constraint Engine; Micro-rounds U/; target K550.
Output: Top-K surviving candidates
1: function S.AD..TURNAMENICE A, K)
while [C] > K do
pairwise tournament until Top-K form disjoint pairs
p PAIRSHUFFLE(C)
Cuzsi 0
for all b)
DEBATEMATCHIA, U)
VOTERI)YVOTR(D)
winner
Czea f Caau {o*}
end for
10:
Crest
11: while
12: return
13:
function
function DEBATEMATCH(a, U))
15:
D 2--00
per-agent final outputs and confidences b each agent reasons on both options
16:
for all A, A do
17:
for all o (a, b)
18:
K₃ +- (ATRAACTKEYYORRDSRRRRrii Report)
19:
) ( (o) QUERYKB(N,:
20:
Dec (o) <- COMPOSEINIT(SS, @))(())) (o))
21:
for to U- do
micro-round refinement
22:
Peers(") READPPERSUUMMARES((A (4,))
23:
DETRCTTXCCERTANTYYDDe."" (o), Peers then
24:
( (o) + QUERYKB(N),
25:
else
26:
(0) < (o)
27:
28:
p(++) (o) < CONSTRAINTCHECK(O, by product=HCII base-needed, ....
Dec (ut1) (o) + UHDATEDECISSOO(DDee (oers(") o ( (o), rf (o))
29:
30:
end for
31:
end for
32:
(dy, cit,) <- FeNALIZE(Dec, (a), (b)))
33:
TOITEEOOMEMORYBOADDDA dj, c, cit,)
b store rationale/citations
34:
D DU ((A,,,,.))
35: end for
36: return D
37: end function
38: function MAJORITYVOTE(D)
39: Da + -L Kld =a]: nb Eo H(d, =b]
40: ni, # nh then
41:
return arg maxoota.b)
42:
tie-break by confidence sum
43:
Sa + EAA .K|d, =
44:
Sb L(add)n)) 6j Mrd
45:
return arg (ab) (8.)
46: end if
47:
end function
E.2 EVALUATION SETTING DETAILS
We evaluate ceneral-purposs. LLMs in controlled candidate sraiiddtteernkinggge regime aligned with the ChemMAS pipeline. Directly prompting models with only Reactant and Product SMILES yields an excessively large decision space, leading to chemically plausible yet inaccurate suggestions and a Top-1 similarity of approximately 5%. To obtain a faithful assessment, for each reaction high-recall pool is first constructed via Multi- reaction-bal retrieval, functional- group cues, constraint heuristics, and memory lookup- -to produce Top-5000 candidate set spanning Catalyst, Solventl, Solvent2, Reagentl, and Reagent2. Each model ranks within the same 5k pool and outputs Top-50 list per head. All models receive identical candidate sets, instructions, and judgment interfaces, and are not permitted to modify the pool, ensuring that differences reflect discriminative

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Published conference paper ICLR 2026
Algorithm Two-Stage Mulli-Tool Collahorative Training
Require: Datasets {(r,, w)]; External tools 7' ((searcch)) Chomory ...);
Instruction SFT epochs Esi RL cycles steps per cycle rollouts C; GRPO hyper-parameters (c, )ka); temperature T: optimizer config.
Output: Trained policy RL
Stage Chemical Teaching (SFT)
/ stad-start tool-aware policy AdamW (8= wd 0.1, batch 128
1: Initialize backbone model we + (owen3-8B-Insruue
2: for с 1,..., Enn do
3: Sample minibatch B C D
4: Compute SFT loss (0) = -::====-oom lo
D y contains step-wise chain tool tokens (<search>
CmEnOEy>
5: Update 4- Ca)(()
6: end for
Freeze SFT checkpoint reference काग stopgrad(To): set
Stage II: Tool Incentivization (RL with GRPO)
align similarity tool use
1:
for 1......Co Co
RL cycles
2:
for s 1, S. do
optimization steps cycle
3: Sample batch 1) C D
for all E Do do
5:
q / @
Sample G rollouts with tools temperature (or 12 mo(- gg,
For each o,. compute reward R(az) with hierarchical scheme:
Format: if invalid > R(o,)
Similarity: Acc(o;) (0,1)
Multi-tool bonus: rx=0.1 if (csearch>)& (ememory)) appear, else
Final: if format ok. R(o,)= max (ac((()((()))))))))))))))))))))) HTM, Acc(o;))
8:
Compute group-normalized advantages {Ar,}} w.r.t group haseline
9:
Optimize GRPO objective:
loyl
(a)
min 1 .Ki. Dxr (To ame)
1=1
10:
Update + VoCorro()) end for
11:
12:
for
13:
for
return RL
ranking and evidence integration rather than retrieval coverage. This protocol mitigates search-space inflation, reduces hallucination, and provides evaluation setting consistent with the workflow of the framework.
F EVALUATION PROTOCOL DETAILS
In this section, we provide the formal definition of the structure-aware evaluation metries used our experiments. The Reaction Condition Recommendation (RCR) task requires models to predict appropriate reaction conditions given reactants and products. We evaluate the quality of predicted condition SMILES strings against ground truth annotations using molecular fingerprint similarity metrics.
F.1 SMILES CANONICALIZATION AND VALIDITY
Prior fingerprint calculation, all SMILES strings undergo canonicalization to ensure consistent molecular rep- resentations. Let be the predicted SMILES and 8* be the ground truth SMILES. The canonicalization procedure converts input SMILES to standardized canonical form:
Scanonical Canonicalize
(13)
This process removes representational ambiguity. Consistent with our evaluation constraints, stereochemical infor- mation is excluded during canonicalization (i someri cSmi 1 les=Fal se) focus evaluation constitutional structure.
The validity metric quantifies the proportion of predictions that correspond to chemically valid molecular struc- tures:
Validity
V [[Valldd&..)
(14)
N

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Published conference paper ICLR 2026
where [[]] the indicator function and IsValid(-) returns if the SMILES string can successfully parsed into a valid molecular graph by RDKit (version 2023.03 or later). Invalid predictions are assigned a similarity score of 0 for all fingerprint metrics.
F.2 TANIMOTO SIMILARITY
All fingerprint-basee based similarity calculations employ the Tanimoto coefficient (Jaccard index), For molecular fingerprints represented as vectors A and (corresponding to the predicted molecule Mp and ground truth molecule My). the Tanimoto similarity is defined as:
AnB B
c
T)A, = AUBB
(15)
a a+ b
where denotes the number of bits in A, denotes the number of bits set to I in B, and denotes the number bits set to both fingerprints simultaneously.
F.3 MOLECULAR FINGERPRINT TYPES
We employ three complementary molecular fingerprint representations to capture different aspects of molecular structure:
RDK Topological Fingerprint (/RDK) This is path-based topological fingerprint. The algorithm enumerates all linear paths of length E [1, atoms within the molecular graph. Each path is encoded as hash incorporating atomic numbers, bond types, and connectivity. The resulting hash values mapped to bit vector of length 2048. The similarity is computed as:
SKIK (Mp, M₁) T (fRIXK (M,), ffox) (M,)
(16)
MACCS keys fingerprint consists 166 predefined structural keys, each corresponding to specific substructures (e.g.. hydroxyl, carbonyl, aromatic rings). For each key k; (i [1, 166)), the bit is set to I if the substructure is present. This metric is particularly valuable for comparing molecules hased on functional group composition:
MACCS Keys Fingerprint (fMaces).
SMACeS (Mp, Mg) = T (MM)) (MAccs(MM))
(17)
Morgan Circular Fingerprint (MMrgga). We employ Morgan fingerprints with a radius parameter of = 2. equivalent to ECCP4, This algorithm captures the local chemical environment by iteratively identifying atom identifiers and their neighbors. excels detecting localized structural differences and functional group modifi- cations:
SMorgan (Mp, Mg) T (fMeegin r= fittegan (Mg.
(18)
F.4 AGGREGATE EVALUATION METRICS
To assess the model performance, utilize the Fingerprint Tanimoto Score (FTS), defined as the validity- weighted average of the three fingerprint similaritics:
SRDK + SMACES Satrgan
FTS-
Validity
(19)
3
where Styee represents the mean similarity score across all test samples for that specific fingerprint type.
F.5 T SIMILARITY
Since our model generates a ranked list of candidate predictions, we also report Top-k metric. Let S(8, 8*) be the pairwise similarity for single instance, defined as the average of the three fingerprint Tanimoto coefficients (assigned as if is invalid).
For a dataset of N reaction instances, where of is the ground-truth SMILES and S,,- f8 S is the set of the top-k predicted SMILES strings:
1
Score
S(B.).
(20)
N
(1...kk)

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Published conference paper ICLR 2026
Train Set
Atom Distmbution
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140000
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Count
80020
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Validation Set
Hoavy Atom Count Ditributionn
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weight (grmal)
Figure Heavy-aton aeavy-atom and molecular- molecular-weight distributions for the Train, Validation, and Test sets (top to bottom). Left column: heavy atom count; right column: molecular weight (g/mol).
G DATASET DETAILS
Public Dataset. We use the RCR subset of ChemCoTBenchh, which contains 90 well-structured samples cover- ing 10 reaction types. For each reaction type, there 9 examples: focused catalyst prediction. 3 reagent prediction, and on solvent prediction. All chemical entities (reacants/pooducsccoodiiosss are represented SMILES format to ensure consistency.
Private Dataset. We curate a large-scale private dataset of organic reactions to supplement existing public benchmarks and better represent real-world experimental scenarios. Sourced from the internal database of an anonymous chemical research institution, this dataset rigorously digitized and structured, comprising 544,591

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Published conference paper at ICLR 2026
entries. Similar in nature the USPTO-condition dataset, it encompasses broad spectrum of known chemical reactions, reflecting a chemical space.
For data standardization, all chemical entities are represented in SMILES format. Each SMILES string is pro- cessed with RDKit to construct molecular graph; unparseable strings are discarded. For every valid molecule, we compute the total atom count, the heavy-atom count (all non-hydrogen atoms), the molecular weight as the sum of average atomic masses (g/mol), and the exact mass as the sum of isotopic masses (g/mol), We then an- alyze and visualize the heavy-atom and gelleulaa-wwighhe distributions for the training. validation, and test sets, where the left column shows the heavy-atom counts and the right column shows the molecular weights in g/mol, as shown in Figure [ We frame the task as reaction condition prediction problem: for each entry, the reactants and products serve the input, while the reaction conditions are defined as the output. To enable fine- grained prediction, the output is structured into five distinct roles: catalyst (Catalyst solvents (Solventl, Solvent2), and reagents (Reagentl, Reagent2).
Based on this setting, construct Question-Answer (QA) pairs to facilitate model training. The dataset is ran- domly split into training, validation, and test sets with a ratio of 1:1:1. The inclusion of this private dataset provides robust supervision signals and allows for the evaluation of model generalization in complex, realistic chemical contexts.
H PROMPT TEMPLATES
As shown in Figure and Figure 8 there prompts for the different agents. Beyond the system-level instruc- tion, the prompt organized into four parts. First, the Tool Definition specifies the invocation schema tools together with their expected outputs. Second, the Interaction Protocol describes how the agent should interleave tool calls with reasoning traces using XML-style tokens, and how the final answer must be returned in a struc- tured format. Third, the Task Prompt clarifies the objectives. Finally, the Output Format enforces a ISON schema that standardizes the prediction into fields such as reaction type, main functional groups by-products, and evi- dence. This structured prompt design enables the model to understand tool usage, maintain consistent reasoning procedure, and produce verifiable outputs.
I RESULTS AND DISCUSSIONS
L11 ADDITIONAL QUANTITATIVE RESULTS
Top-5 Analysis. As shown in Figure 9
introducing specialized agents consistently improves Top-5 Similarity over the AGen + AFul baseline. Acat delivers targeted gains on Catalysi (+10.1%), aligning with its role specialization. Asa contributes the most on solvents, improving Solvens and Solven12 by +16.4% and +13.4%, respectively, ARea yields the largest boosts reagents (e.g.. Reagentl/2 with gains around +18.7% and +13.9%). When specialized agents are combined (e. +oat+Sol, RRaa+Rea, +Cat+Rea), the improvements remain additive and stable across condition types, and the Full System shows the most consistent Top-5 lift across all five heads, indicating effective collaboration among role-specialized experts.
Top-10 Analysis. As shown in Figure 10 the same trend holds for Top-10 Similarity. Acat most strongly benefits Catalyst (+)..1%) Asoi provides clear gains on Solvent1/2 (e.g.. +10.8% and +13.6%). Anea again dominates on R/agens1/2 with sizeable increments (e.g., +17.2% and +9.8%). Pairwise combinations further enhance coverage across heads, and the Full System achieves the highest Top- 10 metrics in macro sense, evi- deneing that mueti-agent collaboration scales beyond single-head expertise and produces robust gains under larger candidate sets.
I.2 RESULT VISUALIZATION
To better illustrate the performance of our framework, we visualize several representative reactions with both predicted and ground-truth conditions. As shown in T the predicted conditions generally align well with the ground-truth, especially for solvents and reagents that are strongly correlated with the transformation patterns in the reaction. For example, in reactions involving polar functional groups, the model consistently identifies appropriate polar solvents such as alcohols or cyclic ethers. Similarly. in -attaliye cross-coupling reactions, the model reliably predicts the use of palladium-baas catalysts, demonstrating its ability capture mechanistic priors from training data.
In cases where the predictions slightly deviate from the ground-truth, the model often proposes chemically rea- sonable alternatives. For instance, different bases such potassium carbonate and cesium carbonate are inter- changeable under similar conditions, and solvents like ethanol and methanol can play analogous roles. These

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Published conference paper ICLR 2026
System Prompt
You chemical assistont for reaction understanding and condition reasoning. You receive Reactant and Product SMILES as imputs. You can call THREE tools:
Tools Definition
General Chemist
1) Functional Group Togger
Invocation: <tragger> "reactants"--..11 "pr.ollppdducsst..... [[...g//ttrogger>
Purpose:
extract key functional groups (FGs) from given SMILES.
Expected (inside cresult> </result>):
"reactonts, fg": L..].
"products_ fg": [--]. "main_fg" ["acyl chloride","aminni "amine",
2) Constraint Engine
Invocation: kengine>( "reactants":... [[..., "products"'...) [.....)///nggnne
perform atom/electron balance and infer br-purducts. Expected result:
Purpose:
"bolanced":
true,
joy_ppoducts": ("HCI" "H20"),
3) Chemical Knowledge Base Search
Invocation:
ksearchs "query":"... ".."" ></search>
Purpose:
retrieve evidence related reaction conditions via keyword search.
Expected result:
"knowledge":
(* ("keyword""'amee amide fermattion"/evvidee=e"""
# Interaction Protocol
You may call tools at time using the XML tokens above.
Tool responses are always returned inside cresult> </result>,
This is the reasoning process. </thinko Show reasoning process inside <thinlo </thinlo. For exomple, <thinko <thinko
Provide only the structured final answer.
Task
Given Reactant Product SMILES:
Use tools to (i) extract Main Functional Groups, (ii) infer By-prudusts, and (iii) retrieve likely Reaction Type candidates.
Reconcile results choose most plausible reaction_ type.
Output in JSON schema, wrapped inside conswer> </onswers.
# Output Format kanswer>
"reaction_ type:: "string",
"main_fg": ("string",..] .....
"by byy_produtts": ("string"...
'confidence": 0.0,
rationale_shann shart": "1-3 sentences summarizing the key cues.", eevidence:: ["KB:source_orf _r__I",...
//nswer>>
Figure 7: Prompt for General Chemist
substitutions highlight the model's flexibility in generating valid yet diverse solutions, reflecting its capacity generalize beyond exact memorization of training examples.
Overall, the visualization confirms that the framework not only achieves high top-k similarity but also produces predictions that are chemically interpretable and robust. The ability to provide both exact matches and plausible al- ternatives underscores the potential of our approach for assisting chemists in condition selection and experimental design.

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Published conference paper ICLR 2026
# System Prompt
Multi- Ageti-Agent Debate
You are a chemical assistant specialized in reaction condition evaluation.
Your input consists of:
Reactant Product SMILES,
Two candidate sets of reaction conditions (Response 1 and Response 2).
# Tools Definition
Chemical Knowledge Base Search
Invocation: csearch>( "query":".." "..."" >//sercc>>>
retrieve knowledge of reaction type, mechanism, and precedent reaction conditions related the given SMILES candidate seavenss
Purpose:
Expected result:
"reaction info":[
("keyword""' SNAr methoxylation', ("orrr""""""""TTT""""""""""""""" widely used"]] ("keyword": NNccleppilic aromatic substitution" " "evidence" ["Reaxys:..."
by nitro group '])
2) Memory Searching
Invocation: ememory>( myypory_type""'nee fg', "by-product" }</memory>
Purpose:
retrieve prior memorized knowledge shippets of similar reactions and condition evaluations,
Expected result:
"content":( "main_fg": "Reactantl: methoxy, acyl chlcride thiophee ring", pboducouu::": "HCL"
# Interaction Protocol
You may call tools any time using the XML tokens above.
Tool responses are always returned inside <result> </result>.
example, <thinlo This is the reasoning process. </thinlo Evaluations should concise, evidence-based and academic/chemica style.
Show reasoning process inside <thinko </thinlo.
If both responses are poor, still select the relatively better one and justify.
# Task
Analyze the chemical transformation (Reactant Product).
For each candidate Response, cross-check the catalyst, solvent, reagents against tool outputs (ssearch: and <memorys).
Provide short evaluation of both Response and Response 2.
Finally, decide which Response is better and explain why cancisely. Return ONLY the final decision enclosed inside kanswer> s/answery:
# Output Format <answer>
"better_resee response": "Response 1*,
"responsel_ewwe eval": "Provides NoOMe MeOH, directly generating methoxide, Simple, efficient, and canonical for SNAr methoxylation nitro-activated aryl fluorides." "response2 eval": "Uses K2CO3 DMSO with MeOH methoxide source. Feasible but slower, less direct than NoOMeeMMeOH.,
"rationale_ cationale_short!:: "SNAr on nitro-aryl fluorides best driven by a strong methoxide source MeOH. Literature and memory confirm NoOMe/MeOH is the standard choice."
//nnser>>
Figure Prompt for Multi-Agent System

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Published conference paper ICLR 2026
Analysis of Muiti-Agent Collaboration (Top-5)
100
50
Top- Accuracy (%)
CLT EaSttraaETE
60
TEL
canoiel
Sriwent
Solemer
Raoyent
Bargeet
Acee
Aart Ase
Jooff
Alcet Are
System
Figure 9: Multi-agent ablation: Top-5 similarity improvements across Catalyst, Solvent1/2, and Reagent1/2 when adding specialized agents on top of Agen + Arull
Analysis of Multi-Agent Collaboration (Top-10)
20
Top- LD Accuraty [%]
26A
10+
60
50
Caoppa
Siiventt
S
Poopert
Agec Aeru
Aart--
Acyh Ava
Full Systam
Figure 10: Multi agent ablation: Top- 10 similarity improvements across Catalyst, Solvent1/2, and Reagent1/2 when adding specialized agents on top of AGentAPult

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Published conference paper ICLR 2026
Table Visualization several reactions with predicted (blue) vs. ground (red) labels.
Reactions
Catalyst
Solvent
Solvent (Pred/ GT)
Reagent 1
Reagent 2
(Pra//GT
(Pred GT)
(Pret/GT)
(Pred/GT)
AcOH
Bromine Bromine
AcOH
za
Toluene Toluene
TEA
EtOH BOH
Chloride Chloride
NaOH NaOH
Palladium Palladium
McOH McOH
THF
THF
THF
AIBN
THF
AIBN
McCN McCN
K₂COs K₂COs
200
Platinum Platinum
THF THF
TEA TEA
Pyridine Pyridine
Toluene Toluene
KaCOs CsyCOs
MM,
OH
E1OH McOH
H₂O
NaOEt NaOMe
H₂O


Reviews:
[{"review_id": "eNM5PAshtU", "reviewer": "Reviewer_DYA5", "summary": "This paper proposes ChemMAS, a multi-agent system for evidence-based reaction condition reasoning. The system decomposes condition recommendation into four collaborative modules: mechanistic grounding, multi-channel recall, constraint-aware agentic debate, and rationale aggregation. Through this process, each decision is supported by interpretable, falsifiable justifications grounded in chemical knowledge and retrieved precedents. Extensive experiments show that ChemMAS achieves 20–35% gains over domain-specific baselines and 10–15% improvement over general-purpose LLMs on Top-1 accuracy, while producing human-trustable rationales.", "questions": "1. Is there any expert evaluation of the interpretability or practical utility of ChemMAS’s rationales compared to baselines or human chemists?\n2. How does ChemMAS handle conflicting evidence retrieved from different sources during the agentic debate stage?\n3. Is there a measurable trade-off between interpretability (longer rationales) and computational efficiency?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1.Evidence-based reasoning: This paper introduces evidence-based reasoning for chemical reaction, which is intuitive and aligns well with how human chemists rationalize reaction design. Empirical experiments show that the proposed method outperforms baselines by a large margin and is interpretable via evidence-based reasoning.\n\n2.Chemical-grounded interpretability: The system employs general chemist agents equipped with specialized tools to extract key chemical features, such as functional groups, stoichiometric balance, and potential by-products, from molecular formulas. These extracted elements then serve as the foundation for multi-step reasoning, forming an innovative and interpretable workflow that adheres to fundamental chemical principles, rather than treating the problem as a purely linguistic translation task as in many existing LLM-based approaches.\nExtension of tool-based and multi-agent RL in Chemical domains: Multi-agent/tool-based  reinforcement learning has previously been applied to domains such as deep search, mathematical reasoning, and code generation. The authors naturally extend this paradigm to the multi-agent setting for chemical reaction condition reasoning, enabling more effective and collaborative evidence-based deliberation.", "weaknesses": "1.The paper briefly mentions broader applications. A short discussion on how ChemMAS could transfer to other reasoning-heavy scientific domains (e.g., physics, materials science) would improve the positioning.\n2.While the benchmark results are convincing, a discussion or demonstration of ChemMAS’s utility in closed-loop or experimental lab pipelines would further solidify its practical value.\nEven simulated lab feedback could strengthen the argument.", "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": "X5Apc2lUNv", "reviewer": "Reviewer_H2cV", "summary": "The paper proposes ChemMAS, a novel multi-agent system designed to move chemical reaction condition recommendation from simple \"what\" predictions to \"why\" evidence-based reasoning. The system uses a specialized team of LLM agents (e.g., General Chemist, Catalyst Agent) that collaboratively debate and reason using a Chemical Knowledge Base to select and justify reaction conditions. Empirically, ChemMAS claims to demonstrate substantial improvements in accuracy and sets a new standard for interpretability in this high-stakes domain.", "questions": "1. Computational Resources and Latency\n- Inference Cost: The proposed system involves complex multi-stage pipelines: recall, tournament pairing, and multi-round, multi-agent debate for each pair. What is the average inference time (wall-clock) and token cost per reaction compared to the single-pass baselines? Is this latency acceptable for real-time library screening applications?\n\n- Scalability of Debate: The tournament selection narrows 5,000 candidates down to 50 via pairwise debate. How does the computational cost scale? Did you explore more efficient ranking methods before the final expensive debate stage?\n\n2. Generalization and Out-of-Distribution Performance\n- Novel Reactions: The \"Multi-Channel Recall\" relies heavily on retrieving similar reactions (by type, reactant, or product). How does the system perform on truly novel reaction types that are chemically valid but have very poor coverage in the Reaction Base? Does the debate mechanism still function effectively when retrieval quality is low?\n\n3. Mechanism Analysis\n- Debate Impact: In the ablation study, removing \"Multi-Agent Debate\" hurt performance. Can you quantify how often the debate process actually changed the outcome compared to a simpler majority vote of the agents' initial, individual assessments? This would help justify the high computational cost of the debate rounds.\n\n- Agent Specialization: You used specialized agents (Catalyst, Solvent, Reagent). Did you observe instances where these agents conflicted in unhelpful ways (e.g., a solvent agent insisting on a solvent incompatible with the best catalyst), and how robust is the \"facilitator\" or voting mechanism in resolving strictly chemical incompatibilities?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "1. Paradigm-Shifting Research Focus: The core idea of requiring interpretable, falsifiable justifications for condition recommendations is highly valuable to chemists, addressing the main bottleneck in adopting black-box AI models for synthesis planning.\n\n2. Sophisticated Architecture: The ChemMAS multi-agent system is a well-structured solution to the problem. Decomposing the task into a series of specialized steps (parsing, multi-channel recall, tournament selection, and multi-agent debate) mirrors a more realistic, collaborative scientific process.\n\n3. Innovative Training Framework: The two-stage training (Supervised Fine-Tuning for \"Chemical Teaching\" followed by Reinforcement Learning for \"Tool Incentivization\") is a compelling method for ensuring agents not only know the correct answer but also effectively utilize their available chemical tools during the collaborative reasoning process.\n\n4. Exceptional Empirical Performance: The reported accuracy gains are highly significant. Outperforming specialized chemical models (RCR, Reagent Transformer) by 20–35% and general-purpose LLMs (like GPT-5, Gemini 2.5) by 10–15% validates the design philosophy and the efficacy of the multi-agent approach.\n\n5. Achieving Explainability: The system successfully produces human-trustable rationales, which is the primary stated goal and a major step forward for explainable AI in chemical discovery.", "weaknesses": "1. Out-of-Distribution (OOD) Performance and Generalizability: While performance on the benchmark dataset is excellent, a critical review question revolves around performance on novel, out-of-distribution reactions. Since the system relies on a \"Multi-Channel Recall\" from a structured database, performance on genuinely new reaction types not represented in the knowledge base or training data needs detailed analysis.\n\n2. Ablation of Agent Debate: The paper demonstrates that the full system is best. However, a deeper analysis of the multi-agent collaboration would be beneficial—specifically, how often the \"debate\" process actually changes a consensus decision established by the initial \"Tournament Selection.\" Understanding the cost-benefit of the full, computationally intensive debate is key.\n\n3. Computational Cost Analysis: Multi-agent systems with tool-use and multi-step reasoning often incur high inference latency compared to simpler end-to-end models. The paper should ideally provide a quantitative comparison of the wall-clock inference time and resource demands (e.g., number of agent calls, token count) compared to the baseline LLMs.\n\n4. Falsifiability of Rationales: While the rationales are deemed \"trustable,\" the paper could strengthen its claim by detailing a systematic human evaluation of the rationales not just for correctness, but for their adherence to known chemical principles and their degree of falsifiability (i.e., whether they rely on concrete, verifiable chemical concepts).", "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": "70zCZTc52D", "reviewer": "Reviewer_nePm", "summary": "The paper proposes a multi-agent system for the problem of reaction condition recommendation. The system operates through several stages including reasoning and tool-use LLMs, all of which are augmented by tools and retrieval, as well as a memory mechanism.\nA strong point in the exposed reasoning is that explainability, and knowing why decisions are made, is important for the use of these type of tools.\nThe authors employ several advanced techniques to improve their models, including SFT, RL, and some specific reward formulations to incentivize specific behaviours.\nThe authors show that this system outperforms any other system out there, including state-of-the-art LLMs like GPT5 and gemini-2.5-pro.\nLittle to no details are given with respect to the datasets used for training and evaluation.", "questions": "- Can you elaborate on the data collection and processing pipeline? it is very unclear on what data the evaluation was performed. Add descriptions of the resulting dataset such as distributions of reaction types, molecule size, etc.\n- Add more thorough ablations and evaluations of your system. As described above, it's not clear if all the contributions in terms of tools, memory, design of rewards, etc, indeed contribute significantly to the final performance. Otherwise they look like ad-hoc decisions that make the work very non-transparent.\n- Provide more details into the evaluation: Have the chemicals been standardized or preprocessed in any way? Also see the weaknesses section for details on this\n- Can you elaborate on the chemical explainations, or the reasoning traces given by your LLMs during inference? This can clarify how specifically the system produces results, and it would be very illustrative to analyze manually some successful trace that can show a key insight that led to the correct prediction of reaction conditions.\n- Connected to this, can you discuss the different failure modes that can be found upon inference with this system? An analysis of the failure modes would be great.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "The paper proposes several techniques that work together towards making the accuracy quite high and improving over several other baselines.\nThis tackles an important challenge in organic chemistry by enabling prediction of reaction conditions.", "weaknesses": "The authors make it a strong point in the introduction that explainability and knowledge about what factors drive reactivity is important for experts using systems like this, however no analysis or examples are shown that demonstrate that the predicted conditions are indeed connected to insights in the reasoning, and thus fail to establish causation between the applied reasoning and debate mechanisms and improvement in performance.\n\nThere are some ablations, however the authors should work on more ablations, to understand the role of \"peer deliberation\" in the results. Arguably this is a type of inference-time scaling, and as such it should be ablated against other types of inference-time scaling techniques, like simple aggregation or majority voting of unrelated reasoning threads.\n\nThe work claims state of the art across all the test categories in a benchmark, beating every other baseline including pretrained models and other LLMs in zero-shot mode. The evaluation is very unclear and concerning as:\n- there is no mention as to what is the source of the datasets used for testing, other than \">500k private samples\". There is barely any mention of the source, curation process, etc. The code only shows some data loading.\n- It is not clear how results are standardized for comparison with ground truths. Given the many ways that are used to write the same reagent (e.g. AcOH or H3CCOOH) it is highly unlikely that any system will achieve such high accuracies unless there is some reagent standardization method, which is by no means mentioned in the paper.\n- The role of RL training or of all the rewards is not at all discussed, and it is not clear if they actually provide any value. There should be more ablations in this direction, to really determine if all these mini contributions do indeed drive performance.\n- The comparison against zero-shot LLMs is unfair, in that it is not clear what prompts were used for the evaluation. In addition, these were not connected to memory, tools, or any other of the additional aids that were given to ChemMAS. It would be very good for transparency to also provide the benchmark results on the same framework, but substituting the LLM for one of the commercial LLMs, or other open-weights.\n\nIt is not clear why the \"functional group tagger\" tool needs to be a tool. As I understand it, it could simply be a pre-processing step that is hardwired to be done for every smiles string that is given in the input. There should also be ablations of this.\n\nNo discussion about the latency of the different systems is provided. It looks like there is a lot of different processes going on and several stages that make this approach notably expensive, and this has not been mentioned anywhere.", "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": "JZnMfmwdeR", "reviewer": "Reviewer_bFZ8", "summary": "This paper presents ChemMAS, a multi-agent system that reframes chemical reaction condition recommendation from \"predicting what\" to \"evidence-based reasoning why.\" The system uses a \"General Chemist\" agent to analyze reactions, recalls a large pool of candidates, and then uses a \"tournament-style debate\" among specialized agents to select the Top-50 recommendations, providing a detailed rationale for each. ChemMAS was trained on a large, private dataset using a two-stage \"SFT+RL\" framework, and its prediction accuracy (\"what\") surpassed top-tier LLMs like GPT-5.", "questions": "The paper's central thesis is the shift from \"what\" (prediction) to \"why\" (explanation), claiming to provide \"falsifiable, human-trustable rationales\". Yet, all quantitative results (Tables 1-3) only measure Top-k prediction accuracy (\"what\"). Without any human-in-the-loop evaluation or expert validation of the generated rationales, how can the authors substantiate their claim that the system delivers on the \"why\" by producing high-quality, scientifically sound explanations?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 4, "contribution": 2, "strengths": "1. Novety: The paper's greatest strength is its re-definition of the problem. Shifting reaction recommendation from a black-box prediction task to a transparent, evidence-based reasoning task is critical for the adoption of AI in serious scientific research11. The formal problem definition in Section 2.1 (requiring each output $c$ to be accompanied by a verifiable rationale $\\rho(c)$ 12) is excellent and directly targets a key pain point in modern AI for Science.\n\n2. Good Design. The architecture of ChemMAS is well-thought-out. It doesn't just stack agents; it builds a logically-sound workflow. From the \"General Chemist\" building a shared \"Memory\", to \"Multi-Channel Recall\" building the candidate pool , to the \"Tournament-Style Debate\" for ranking, each step is purposeful. The use of a tournament-style pair-wise debate instead of brittle global scoring is a particularly robust mechanism for ranking, and its efficacy is confirmed in the ablations.\n\n3. Paper writing and presentation are really good for me. The idea is easy to follow, and the workflow shown in the Figures is clear.", "weaknesses": "1. Private Dataset: The entire evaluation hinges on a private dataset of 544,591 reactions. This is a major weakness, as it severely hinders the reproducibility and external validation of the results. Furthermore, how was the \"Reaction Base\" for \"Multi-Channel Recall\" constructed? How much overlap does it have with the test set? If the system relies heavily on near-duplicate examples from the recall database, its \"reasoning\" capabilities might be overestimated, functioning more like a sophisticated k-NN lookup.\n\n2. Lack of Evaluation for the \"Rationale\": The paper's core claim is to provide \"falsifiable, human-trustable rationales\"  (the \"why\"). However, all quantitative evaluations (Tables 1, 2, 3) focus exclusively on Top-k prediction accuracy (the \"what\"). The paper provides no evaluation of the quality of the generated rationales themselves. Are these explanations chemically sound? Do they offer genuine insight, or are they just templated statements? This lack of human-in-the-loop evaluation (e.g., by expert chemists) leaves the central claim of the paper (\"From What to Why\") unsubstantiated.\n\n3. The ChemMAS system is extremely complex. To predict a single reaction, it must run $\\mathcal{A}_{Gen}$ (with 3 tool calls) 23, perform a large-scale multi-channel recall (generating 5k candidates)24, and then execute a multi-round tournament (approx. 7 rounds to get from 5k to 50). In each tournament round, a panel of agents (e.g., 4 agents) must adjudicate each pair via \"Multi-Step Reasoning\" (which is itself an iterative process)25252525252525. This implies massive computational overhead. The authors provide no analysis of inference cost (e.g., total tokens, API costs, or end-to-end latency). How does this compare in practicality to a single prompt sent to GPT-5?", "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)