An Open-Ended Benchmark and Formal Framework for Adjuvant Research with MLLM
Authors: yi chen
Yu Zhang
Jian Xu
Hua Yue
Xinming Wang
Zequan Lyu
Xu-Yao Zhang
Wei Wei
Cheng-Lin Liu
Venue: ICLR 2026 Poster (2026)
Keywords: Adjuvant
Scientific Benchmarks
Multimodal Large Language Model
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
TL;DR: We introduce the first benchmark and formal framework for evaluating multimodal LLMs in adjuvant research, comprising 1,294 Q&A and 1,364 formal data entries.

Abstract: Adjuvants play a critical role in modulating immune responses and are central to the development of vaccines and immunotherapies. Yet progress in this field is constrained by data scarcity and incomplete understanding of mechanisms of action, which limit the transition from experience-based design to AI-driven approaches. To address these challenges, we present the first benchmark dedicated to adjuvants, constructed in an open-ended Q\&A format and annotated by domain experts. The benchmark comprises 1,294 Q\&A pairs and 1,364 formal descriptions, providing a resource for evaluating general-purpose multimodal large language models (MLLMs) and for developing domain-specific systems.  
We systematically assess 11 closed-source and 18 open-source MLLMs across dimensions including domain-specific Q\&A, hallucination rejection, data generation, and instruction following. Results indicate that OpenAI-o1 (STS = 0.7495, LLM Score = 7.7) and DeepSeek-R1 (STS = 0.7415, LLM Score = 7.7) achieved the strongest performance among closed- and open-source models, respectively. In addition, we introduce a formal description framework for representing adjuvant design principles and immune mechanisms as structured abstractions, which can serve as building blocks for future domain-specialized MLLMs. Overall, this work provides a first step toward systematically integrating MLLMs into adjuvant research by offering a dedicated benchmark, comparative evaluation of existing models, and a formal foundation for future development. Data and code will be released at https://github.com/banjiuyufen/Adjuvant-Benchmark.

Published as a conference paper at ICLR 2026
AN OPEN-ENDED BENCHMARK AND FORMAL FRAME- WORK FOR ADJUVANT RESEARCH WITH MLLMS
Yi Chen!.36 Yu Z Jian Xu Xu-Yau Zhang Hua Yue
Xinming Wang Zequan Lyu?., Wei Wei24, Cheng-Lin Liu 1.3, Liu
State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS) Institution of Automation, Chinese Academy of Sciences, Beijing, China
2 State Key Laboratory of Biopharmaceutical Preparation and Delivery Institute of Process Engineering, Chinese Academy of Sciences, Beijing, China
3 School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
4 School of Chemical Engineering, University of Chinese Academy Sciences, Beijing, China
School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, Beijing, China
Zhongguancun Academy, Beijing, China
{yi. chen, xyz, liucl/@nlpr ia ac.aacccm cn, (jian. xu, wangxinming20244 ac. cn (zhangyu21, hyue, weiwei 0 peppe..c.. on, 1vzequan24@maills ucas.
ABSTRACT
Adjuvants play a critical role in modulating immune responses and are central the development of vaccines and immunotherapies. Yet progress in this field is constrained by data scarcity and incomplete understanding of mechanisms of action, which limit the transition from experience-based design Al-driven ap- proaches. To address these challenges, we present the first benchmark dedicated to adjuvants, constructed an open-ended Q&A format and annotated by domain experts. The benchmark comprises 1,294 Q& pairs and 1 1,364 formal descriptions, providing resource for evaluating seneral-purpose multimodal large language models (MLLMs) and for developing domain-specinf systems. We systematically assess 11 -ousce and 19 open-source MLLMs across dimensions including domain-specific Q&A, hallucination rejection, data generation, and instruction following. Results indicate that OpenAl-ol (STS = 0.7495, LLM Score = 7.7) and DeepSeek-PR R1 (STS 0.7415, LLM Score 7.7) achieved the strongest per- formance among closed- and open-source models, respectively. In addition, we introduce formal description framework for representing adjuvant design prin- ciples and immune mechanisms as structured abstractions, which can serve as building blocks for future domain-specialized MLLMs. Overall, this work provides a first step toward systematically integrating MLLMs into adjuvant research by offering a dedicated benchmark, comparative evaluation of existing models, and formal foundation for future development. Data and code will be released at https::/gituub.cmmbbajjuuuuu hwtpa//nnnnuuuaeee/a/aaaaaaaaaaa
AdjuvanttBBncnharrt.. Benchmark
INTRODUCTION
Artificial intelligence (AI) has become important driver of scientific discovery, offering
perspectives and tools to address increasingly complex challenges Hessler & Baringhaus (018):
Jumper al. (202T) Xu al. (2021): Esteva al. (2019) 202T); Rajpurkar et al. (2022) Early applications in science often relied on task-specific datasets and bespoke neural architectures Krenn al. (2020): Wu al. (2018): Xic & Grossman (2018): de Teresa-Truee
et al. (2023). but recent advances in multimodal large language models (MLLMs) have shifted attention toward more general
frameworks capable of integrating heterogeneous information sources Liu et al. (2023): Li al.)
"gqual contribution.

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(2024); OpenAl 2023); Team al. (2023) These models demonstrate broad capabilities in domains
ranging from language and vision to biomedicine, enabling new paradigms for reasoning and analysis
He et al. 2022)) ;; Xie et al. (2023); Outeiral & Deane (2024); /Wan et al. (2025b). Representative
work includes LLaVA-Med a vision- rision-language assistant for biomedical images Li et al. 2023). and
BiomedGPT, generalist biomedical foundation model /Zhang al. (2024b).
Table 1: Cross-domain availability of datasets, methods, and mechanistic principles.
Drug Discovery
Protein Structure Genomics/Omics
Catalyst Design Battery Materials
Adjuvants
Datasets
X
Methods
X
Principles
X
Adjuvants are indispensable components of modern vaccines, as they enhance immune responses, prolong protection, and in some cases determine whether a vaccine is clinically viable Glenny et al. (1926); [wasaki & Omer (200);; Reed et al.] (2013). They are particularly critical for emerging infectious discases and cancer immunotherapy. where rapid and robust immune activation is essential. Despite their importance, the field remains underserved by AI. As shown in Table [ unlike drug large-scale datasets and standardized benchmarks already exist- adjuvant research faces three persistent barriers: (i) limited systematically curated data, (ii) a lack of Al methodologies tailored to adjuvant knowledge, and (iii) heterogeneous definitions and mechanisms that complicate systematic modeling Guy (2007). As result, existing biomedical benchmarks cannot be directly applied, and building domain-speciife infrastructure is necessary for progress.
discovery or protein structure prediction-whte
Question: does the clomll expression of an ligig reveptors untrabbie to the Jamarr system's a diversity?
Adjuvant Formal Formal Language:
Ag_1 + 1Datg of Ripotitia
clonal expression astigen receptors essures Reasoning Process: posserata unique
specifit
Md1. PEE-PAKEP
particular ontaget, this diversitt csventirl for the system to recegnize and respond to side variaty of pathogers.
Stree_1
particie
Stree Capslyear
Asswer:
Clanal expression allow for vast array arique antigen system racogrize respand
Strec Inorgante salt
diveras pathogans.
f.2 = Farn(Struc J, (g.3)
(a) A Case from Open-ended Q&A
141 Fore[Struc 2, PSS-PAEEP)
Question:
How do essale ReD aenoperticles promste the recruitment and activatiin of wntigen-gresenting cells?
441.2 Fora(Strur J, aluntnus)
Vaci : Mtx(Ag J, 161_1) DG
tivdent seture mosic nenpertisles failitate greater sptake intigen-rresenting calls (19Cs) and enhance the activetion of these cells due to the increased antigennc stieslus.
Reasoning Process:
Voc2 Nax(he 2, set 2)
languel Test: study stillcing polppoophecster based cationic polyear, reo PMEEP sdfacast. The antiges used is lispatstis a surface antiger (iaake is the fore virus-like particlss (ULPs) Verious formilations are prepared with supertrental
Answer: Pensic s10 ranperttciess through their multinalent display, incressing antigen densityy leading greater immare activation.
connlets the smsag-Aip coabired with osa- higher concentration. while control groips Inclute formilations witt alsmirse adjuast and without my adjivart, The sdjuant PE6-XEESF is a blodeg sdable calionis polymar, shile clanimm aljuret ssll or a posiiive control.
Reason for Enror after the antigon is esserbled into ramperticllss the nano size foreed is conductve to the uptaks sc calls.
(b) A Case from Hallucination Data
(c) Case from Adjuvant Formal Data
Figure 1: Three Types of Data Display in Adjuvant Benchmark
To address this gap, we present the first benchmark for adjuvants. We adopt an open-ended Q&A format to capture mechanistic reasoning, design considerations, and safety issues that cannot be represented through multiple-choice tasks. In parallel, we propose a formal description framework that translates complex biological intuitions into structured abstractions support reasoning beyond retrieval. Concretely, we generated candidate data with four state-of-the-e MLLMs and conducted rigorous expert annotation across vaccine subdomains. The resulting benchmark consists of three components (Fig. Ty: Open-ended Q&A Data, Hallucination Data, and Adjuvantt Formal Data. We then evaluated 11 closed-source and 19 open-source MILMS on these tasks, assessing domain- specific answering, hallucination rejection, and instruction following, and complemented these with expert-based subjective assessments of generation quality.
Our main contributions are summarized as follows:
We establish the first high-quality benchmark dedicated to adjuvants, explicitly designed to fill a long-standing gap in biomedical AI benchmarks and to support subsequent MLLM research.
We perform the first systematic evaluation of mainstream general-purpose MLLMs (11 closed-source and 19 open-source) on adjuvant knowledge, assessing critical capabilities including data generation, domain-specifie QA, hallucination rejection, and prompt follow-
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ing. This provides initial conclusions the capabilities and limitations of current models, and concrete guidance for selecting base models in this domain.
We introduce formal descriptions of adjuvants, converting their complex biological mech- anisms into simplified abstractions that can be directly used in training or reasoning. This approach lays the groundwork for future domain-specific MLLMs that combine statistical learning with symbolic reasoning.
2 RELATED WORK
2.1 ADIUVANTS
Adjuvants are crucial components that are used to improve the effectiveness of vaccines, primarily by stimulating the immune system to improve recognition and response to antigens. By increasing the potency vaccines, they enable the immune system to respond more rapidly and effectively to pathogens Zhao et al. (2023). Adjuvants can encompass diverse range of substances, including synthetic small molecule compounds, complex natural extracts, and particulate materials, each contributing uniquely to the modulation of immune responses McKee et al. (0077)
Despite the long-standing and increasing diversity adjuvants used vaccines, the mechanisms by which they enhance immune responses not yet fully understood. With the elucidation of how the innate immune response regulates the adaptive immune response, researchers began to gain insight into the operational mechanisms of adjuvants Coffman et al. (2100). Although this work has provided a certain degree of elucidation regarding the modes of action of adjuvants, a systematic overview and summary of their mechanisms remain scarce due to the broad definition and complex nature of adjuvants.
Recent studies have begun explore the integration of adjuvants with machine learning optimize adjuvant selection, such as Nagpal et al.) (018)) used the support vector machine (SVM) to develop a hybrid model for predicting A-cell epitopes, which enhances the identification of immune epitopes. Ma et al. (2023) utilized machine learning to identify molecular properties that target Toll-like receptors (TRs)) and designed two new adjuvants to enhance vaccine responses. These effectively promote strong immune responses, significantly suppressing tumor growth and metastases. Chaudhury used random forest algorithms develop a predictive model that achieves 92% accuracy in predicting adjuvant conditions based on immune response data, facilitating the identification of immune characteristics of different adjuvants and aiding the rational pairing of vaccines and adjuvants.
However, these methods often lack generalizability, limiting their effectiveness in complex scenarios. In contrast, MLLMs can Iearn from vast and diverse modalities, identifying underlying patterns that traditional methods may overlook. This capability enables them to generate more accurate and efficient insights and predictions. By integrating MLLMs with adjuvant research, aim to accelerate adjuvant development, provide a more responsive approach to public health emergencies, and shift the current paradigm from trial-and-error, experience-based methods to more Al-driven and efficient process.
2.2 SCIENCE BENCHMARK
Recently, there has been increasing attention on MLLMs in scientific research. To evaluate and improve the performance of MLLMs in specific research domains. it is crucial to establish rigorous benchmarks. These not only help in assessing the accuracy and efficiency of the models but also ensure that the evaluation of different methodologies used is consistent and fair across the same studies.
Zhang et al. (2024a) developed ChemBench, an innovative chemical benchmark consisting of 4,100 multiple-choice questions in nine tasks related to chemical molecules and reactions, aiming at ob- jectively measuring the chemical proficiency of large language models (LLMs). Chen et al. (2023) proposed an extensive benchmark study on biomedical text generation, which highlights the strengths and weaknesses of ChatGPT in addressing biomedical tasks, potentially inspiring further advance- ments in NLP models for biomedical data analysis. Zhang et al. (2025) introduced DataSciBench, a novel and comprehensive benchmarking tool aimed at deeply evaluating the capabilities of L.MMs
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in data science through natural and challenging tasks. [e et al. (2023 proposed system called SeiGuard to control misuse risks associated with AI models in the field of science. They also intro- duced red-teaming benchmark, SciMT-Safetty to assess the safety of different systems. Gao et al. (2025) proposed a model-level evaluation framework that emphasizes practical metrics aligned with real-world applications address the limitations in structure-based drug design (SBDI)).
Summary. Despite the proliferation of benchmarks domains like chemistry, biomedicine, and data science, none of them address the unique characteristics of adjuvant research. Existing biomedical benchmarks (e.g., PubMedQA Jin et al. (2019), ChemBench Walker et al. (20T0)) mainly evaluate molecular properties, literature summarization, adjuvants involve heterogeneous substances, multi-scale immune mechanisms, and a lack of structured training data. This makes impossible to directly apply existing benchmarks to this domain. Our work therefore, fills critical gap by introducing the first dedicated benchmark for adjuvants, explicitly designed to eapture mechanistic reasoning, safety evaluation, and design-oriented knowledge that are absent from prior benchmarks.
general biomedical knowledge. In contrast,
ADJUVANT BENCHMARK
3.1 OVERVIEW
Although immunology and adjuvant research have seen significant progress, the systematic integration of MLLMs into this field remains unexplored. To address this gap, we introduce the first benchmark explicitly designed for evaluating MLLMs adjuvant-related knowledge and reasoning. By curating high-quality academic resources and leveraging multiple state-of-the-an MILMs, we construct a domain-specific evaluation suite that captures both mechanistic understanding and practical design considerations. The following sections detail the benchmark construction pipeline, expert annotation process, and subsequent analyses of the resulting data.
3.2 PIPELINE OF BENCHMARK CONSTRUCTION
The overall construction pipeline illustrated in Fig. We first collected 739 peer-reviewed papers together with classic textbooks, from which MILMS automatically generated approximately 35k: open-ended Q&A pairs on adjuvants and immunology. each accompanied by an explicit reasoning step (The generation prompts are described in Appendix G.I.. To ensure quality and domain relevance, 1.5k samples were randomly selected for expert review. After cleaning and careful labeling, 1, 294 high-quality Q&A pairs were retained the meta dataset. The Krippendorff's among annotators was 098119.
Guidance/Opinion
Discriminate
Prompts
Evaluated
Guidance
BD
Textbooks/Bood
Generate
Basic knowledge
Knowledge Dataset
A
MLLMs
Advanced knowledge
Hallucination Dataset
Papers
Define
Guidance Template
Formal Dataset
Professors
Formal Variables/Functions
Figure 2: The Benchmark Construction Pipeline.
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The annotation team consisted of 13 experts spanning infectious disease, cancer, and bacterial vaccines. All were trained under unified guidelines and evaluated each Q&A-reasoning triplet strictly against the source material. Items were labeled either valid or ballucinated, with justifications provided for the latter. Detailed preprocessing and annotation workflows are descrihed in Appendix B
To reduce model-specific bias particulllllllly since the same system might otherwise generate and answer its own questions we employed several MLLMs with long-context and multimodal support, including GPT-4o OpenAl, Claude3.5-Sonnet Claude Brnie4.0-Turie Baidu and DeepSeck-R Guo
et al. (025).
Following expert annotation, the meta dataset was organized into three complementary components: Open-ended Q&A Data, Hallucination Data, and Adjuvant Formal Data. Each component is described in detail in the subsequent sections.
3.3
STATISTICS OF BENCHMARK
3.3.1
OVERALL ANALYSIS
The overall distribution of the benchmark is shown in Fig. 3 Specifically, Fig. 3a illustrates the
proportions of different data types, while Fig. (3b summarizes the contributions of various models to Q&A generation. The benchmark is primarily composed of open-ended Q&A items and formal adjuvant data, with GPT-4o and DeepSeek-R contributing the majority of the high-quality entries. This reflects their comparatively stronger performance in preliminary generation and evaluation. Further details and representative examples are provided in Appendix A and Appendix
G
-ppenenneed DRA
108
Sefety
1254
HHaladration
Biology Prinoples
Busic Knowledge
155
300
400
221
Oper rnded CMA
Adlp morh Tormal
Cludaih Samut
EreadD Sadaa
Deapllek
(a) Distribution of the Data Types
(b) Distribution of MLLMs for Generating Q&A Data
Figure 3: Distribution of the Adjuvant Benchmark
3.3.2 OPEN-ENDED Q& DATA
The open-ended Q&A component intended evaluate the extent to which MLLMs capture adjuvant-related knowledge. It draws both basic and advanced material curated from textbooks and peer-reviewed publications. The advanced category covers two major themes: biological principles (e.g., immunological mechanisms of adjuvant action) and design & safety (e.g., strategies for developing or modifying adjuvants and approaches for evaluating safety). Table [oole[]summarize the distribution of these data, with the advanced subtypes highlighted.
Table 2: Distribution of Open-ended Q&A
Table 3: Hallucination Data
Data
Basic Knowledge
Advanced Knowledge
Biology Principles
Design Safety
Data Type
Question
Answer
Overlap
Total
Type
Count
221
1073
846
227
Hallucination
27
54
In addition, the benchmark includes multimodal content: 1,135 entries are text-only (87.7%), while 159 involve image-asse associated inputs (12.3%). This enables evaluation of both purely textual reasoning and multimodal understanding. Illustrative examples are provided in Appendix
3.3.3 HALLUCINATION DATA
In this study, the hallucination data follow the same structural format as the open-ended Q&A but differ in that the questions answers have been reviewed by domain experts and explicitly judged
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to be incorrect. Rather than discarding these items, we retain them dedicated resource for evaluating the ability of MLLMs to recognize and reject hallucinations in the context of adjuvant and immunology tasks. For clarity, hallucinations. This dataset provides a controlled setting for analyzing the sources of hallucination emors and offers reference point for the development of more reliable model evaluation and training strategies.
distinguish two categories: question hallucinations and answer
3.3.4 ADIUVANT FORMAL. DATA
Formal descriptions are introduced to translate complex biological processes related to adjuvants into structured variables and functional transformations, with the aim of improving both the reasoning capacity and interpretability of MLLMs in this domain. Such formalized pathways also provide systematic means of representing mechanisms that may otherwise remain implicit or fragmented in the literature.
To construct these descriptions, we worked with the same team of adjuvant experts described in Section 3.2 design set of formal variables and functions, thereby establishing expert-defined standards. These standards were organized into templates and incorporated into prompts for GPT-4o, which subsequently generated total of 1,364 formal entries (Fig. [T]. The data are divided into two balanced categories: adjuvant design and adjuvani activation & immune processev, each comprising 682 items. Detailed definitions of the variables and functions are provided in Appendix D
Although this framework has not yet been applied to downstream model training, the released vari- ables and relationships-such as Form (Struc, Ag) and Load (A, B, Surface) -erve as structured building blocks for future straction of design principles and immune response processes, the framework establishes foundation that can be extended in subsequent research.
MLLMs. By providing computable ab-
EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
MLLMs: The set of evaluated models is listed in Table Models highlighted in blue were also used in the data generation stage and were subsequently reviewed by adjuvant experts.
Table 4: Models evaluated on the adjuvant benchmark. Blue rows indicate models additionally used during data generation.
#Size
Form
GPT-4o
N/A
api
latest
Qwen2.5.Vi
7B
open
instruct
GPTF-4.1
N/A
api
latest
Qwen2.5 VVV-722
72B
open
instruct
OpenAT-al
N/A
api
latest
Qwen1-8B
8B
open/api
think
Claude3.5
N/A
api
sonnet
Qwen3-32B
32B
open/api
think
Clande3.7
N/A
api
Qwen3-30R.co A.3
30B
open/api
think
Geminil 5-Poro
N/A
api
latest
Qwen3-235B-A
235B
open/api
think
Gemini2.00 0-Pro
N/A
api
flash
Intervv12.5888
8B
open
v2.5
Gemini2.5-PPo
N/A
api
flash
Intemv12.5575
7SB
open
v2.5
E
N/A
api
Intemv13.0088
8B
open
Ernie4.0
N/A
api
turbo
Intemm133.0-721
72B
v3.0
Doubao1.5-Pro
N/A
api
250115
InstructBlip-13B
open
DeepSeek--R R1
671B
upen/api
reasoner
Idefics-91
9B
instruct
DeepScek-V3 V3
671B
open/api
chat
Darwin
7B
open
LLaVA1.5-7B
7B
upen
v1.5
BiGGPT
1.5B
Large
1.
13B
open
ScienceOne
open
Inference: Closed-source models were accessed through their official APIs. For open-source models, inference was performed with the official implementations on x NVIDIA A800 GPUs, following recommended hyperparameter settings. To ensure comparability across models, all were evaluated under identical prompts in zero-shot setting (see Appendix G.2 for prompt details). To ensure fairness, regardless whether the model supports multimodal input, we utilized a unified OCR engine to process the images and concatenated the OCR output with the original input text. Furthermore, assessed the performance of the top multimodal models on the image-related subset, with detailed experimental results provided in Appendix

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Evaluation metrics: To assess knowledge comprehension in the adjuvant domain, employed combination of automatic metrics and model-based scoring. Standard measures included Seman- tic Textual Similarity (STS) and BERTScore. In addition, introduced an LLM-basee rubric, implemented with GPT-4o and DeepSeek-RI, which scored answers along three axes: Similarity Score (SS), Scientific Rationality Score (RS), and Inclusiveness Score (IS), each 0-10 scale. This approach provides a scalable and reproducible way to evaluate factual accuracy and conceptual soundness, while reducing dependence on manual annotation. To further probe robustness, we report Hallucination Rejection Ratio (HRR), which quantifies the ability of models to detect and avoid incorrect content. Detailed metric formulations are given in Appendix
4.2 EVALUATION OF GENERATION
During the annotation process, experts assigned subjective scores (0-10) six dimensions: Ques- tioning Ability, Answering Ability, Reasoning Ability, Knowledge Reserve, Chart Analysis, Context Utilization, and Instruction Following. These scores reflect the overall quality and reliability of the generated content.
The results summarized in Fig. 4a DeepSeek-RI RI obtained comparatively higher scores in both questioning and answering ability, indicating that it can produce relevant prompts and generate responses with coherent reasoning in the adjuvant domain. GPT-4o achieved the highest score in questioning ability and also performed well in instruction following, suggesting that it is effective at generating focused inquiries and adhering task specifications. In addition, GPT-4o showed broad coverage of domain knowledge, contributing to more comprehensive responses.
GT741
Quantaning Ability
Asswaring
Reowiing Abiltty
Knwwlelge BouNE
6embri55
Osaed eourre MILLfe
200
Dowh
(a) Subjective Evaluation in Generation
(b) LLM Scores
Figure 4: Comprehensive Evaluation of MLLMs the Adjuvant Benchmark
By contrast, Ernie4.0 and Claude3.5 received consistently lower scores across several categories, suggesting limitations in handling complex material from adjuvant-related literature.
Overall, the expert assessments highlight GPT-4o and DeepSeek-RI as the strongest performers within this evaluation setting, particularly in tasks requiring both domain-specift questioning and reasoned answering. These findings provide basis for selecting suitable base models for future work in adjuvant-focused applications.
4.3 EVALUATION OF ADJUVANT OPEN-ENDED Q&
We evaluated 11 closed-soures and 19 open-source MLLMS on the adjuvant open-ended Q&A task. Results are reported in Table with blue highlighting used indicate models achieving tfate-ofthh- art performance under the given metrics. A comparison between closed- and open-source models is summarized below.
Closed-source vs. Open-source: On average, closed-source models achieved higher overall perfor- mance, with a mean LLM Score of 7.3 and an STS score 0.7263, compared to 6.4 and 0.6922 for open-source models. Nevertheless, several open-source models exceeded the closed-source averages. DeepSeek-RR R1 (LLM Score= 7.7. STS = 0.7415), DeepSeek- V3 (LLM Score = 7.88, STS = 0.7289), Qwen3-235B (LLM Score = 7.6, STS 0.7331), and Qwen3-32B (LLM Score 7.6, STS = 0.7259) performed comparably to the strongest closed-source models, particularly in scientific rationality and inclusiveness. However, in terminology consistency, reflected by BERTScore, open-source models

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averaged 0.5504, which remains below the closed-source average of 0.5656. This suggests that while optimization strategies enable some open-source models to close the gap, challenges remain in aligning with domain- -speciii---iieeeeeeee vocabulary. Overall, the observed performance differences appear to relate more to the extent of domain knowledge integration than to the proprietary or open-source nature of the models.
Inference Models vs. Think Models: Think- -rrente models obtained higher scores in Rationality Score (RS), Inclusiveness Score (IS), and STS compared to inference-style models. This pattern suggests that their explicit reasoning pachanisms-sucee as multi-step causal decomposition and contribute to producing more logically consistent and comprehensive answers. At the same time, the reliance on explicit reasoning chains increases decoding complexity, which may constrain efficiency
logical verification confiiation-conntiuuueee
resource-limiter settings. These observations indicate that combining explicit reasoning strategies with domain knowledge and structured representations could be promising direction for future model development.
Closed-source Models: OpenAl-ol (LLM Score = 7.7, STS 0.7495) was the best-performing closed-source model in our setting. By contrast, Ernie4.0 (LLM Score = 6.9) and Doubao 1.5-Pro (LLM Score 7.1) obtained lower scores within this cohort. While performance differences may correlate with factors such as data coverage, training procedures, and model scale, the proprictary nature of these systems prevents attributing causality from our evaluation alone. Closed- source models also pose practical constraints for scientific use, including limited transparency and higher inference costs, which may hinder broad adoption in open research workflows. Future work could explore more transparent and collaborative evaluation practices to facilitate integration into scientific pipelines.
Table 5: Evaluation Result of Adjuvants Open-ended Q&A
Model Chaggey
STS Scoce
BERTSoore
X5
RS
15
55
RS
IS
LLM Scose Arg
Inferesce Miskir
GP54
1371775
3.5155
7.1
7.3
(laked 117
0.7250
0.3150
5.2
6.3
6.8
8.2
6.
Cereel
313
0372255
3.5911
5.5
Cemer20 Phi 1007
027118
2.6155
5.3
82
63
6.0
K7
21
23
0.5554
Imael
0.2238
2.5587
5.1
K11
6.1
5-1
K.2
62
69
Deshoo
0.5532
8.5
QyeaAla OpasA1
0.7405
59
7.1
1.3
8.9
7.2
73
Anerag
Opm sonncc MILLMs
DessSed.
V-lau
027229
D.2215
5:5
2.0
9.1
7.7
114W1150
0.7154
0.5013
5.2
7.0
5.3
Quen2 2.V22.V
032151
2.3651
5.5
Owen 5.V
0.9535
6.5
9.
0.7
8.4
0.896
0.3005
5.0
0,087
0.8225
3.0
7.6
6.0
50
1.0
TeanciRipe
1.500
3.555
033622
2-7115
4.5
0.2
4.5
6.1
42
50
Plink
DeepSok
0.3015
0.775
5.5387
6.5
811
61
71
8.6
73
74
Qredi-22 Sragria mord
5.6
6.9
8.5
Dwn'' Qee1i-355 7358 A25 Alleotrrr 1015
0.733
3,597
4.5
7.0
8.9
7.6
Dosaate
Mudiii
0.5465
5,9955
12
1.5
Scien0Oe (amuu of Anthrrie Crrne Anadery Scleaco
0.501
69
7.0
8.5
Anera
0,9922
8.5584
52
Among the open-source systems, DeepSeek- V3 achieved the highest overall performance. DeepSeek-F R1 (LLM Score 7.7). Qwen3-32B (LLM Score 7.6), and Qwen3-235B (LLM Score 7.6) all surpassed the closed-source average (7.3). These models employed explicit reasoning strategies, such as causal decomposition and multi-step verification, which contributed to higher scientific rationality scores and in cases exceeded those of closed-source models (e.g.. Claude3.5). This suggests that open-source models can exhibit strong logical reasoning capabilities, although they often rely decomposition and iterative reasoning to mitigate limitations in domain- specific knowledge.
Open-source Models:
DeepSeck-RI R1 and Qwen3-235B also incorporate Mixture of Experts (MoE) architectures, where dynamic expert routing enables finer-grained knowledge integration. While MoE contributes to improvements in reasoning and task decomposition, terminology consistency remains challenge:
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BERTScore for these models is still lower than that of closed- models such GPT-4o. This indicates that MoE and reinforcement learning approaches alone are insufficient, and domain- -ddaptive pretraining remains necessary for accurate use of specialized terminology.
An additional observation is the non-linear relationship between parameter scale and performance: Qwen3-235B and Qwen3-32B achieved similar L.M Score despite their large difference in size. This pattern highlights diminishing returns from scaling alone and underscores the importance of targeted knowledge injection for domain adaptation. By contrast, models such as InstructBlip- and Idefics-9B underperformed across most metrics, reflecting architectural and training-data limitations in earlier generations of multimodal LLMs.
The comparatively lower performance of domain-specific biomedical and materials models indicates limitations in directly transferring such architectures adjuvant tasks. Their training objectives, often centered on literature summarization or general biomedical QA, are not well aligned with the requirements of adjuvant-focused Q&A, leading to weaker answer quality (see Appendix F.4 for detailed examples).
Domain-Speciffi Models:
Notably, ScienceOne, which fine-tuned on general scientific data based on the Qwen3. While outperforms its base model on several metrics, it does not achieve a comprehensive lead across all evaluation dimensions. Although integrating general scientific knowledge undoubtedly provides a beneficial foundation for comprehending broader scientific concepts, this reveals critical limitation: broad knowledge injection alone does not automatically translate to a comprehensive understanding of specialized adjuvant research. To achieve the in-depth comprehension and nuanced reasoning required for adjuvant research, foundational scientific models must undergo further targeted training on domain-specific adjuvant data. These results reinforce the view that progress in adjuvant research requires purpose-buill datasets and dedicated models, rather than relying solely on fine-tuning with broader biomedical, materials, or general scientific corpora. Furthermore, the Darwin model obtained the highest BERTScore among all models, which can likely be attributed to its training on synthesized data from the materials and biological domains, enabling effectively grasp and utilize highly specialized terminology.
Overall, models from the GPT, DeepSeek, and Qwen3 families demonstrated relatively strong performance across multiple metrics, suggesting that these families already possess the capacity to contribute as auxiliary tools for basic research and as potential foundations for future adjuvant- specialized systems.
4.4 EVALUATION OF HALLUCINATION REJECTION
We evaluated the top five models (both closed- and open- spen-source) based on their LLM Scores for their ability to reject hallucinations. Results are reported in Table
Table 6:
Evaluation of Hallucination Rejection Capabilities (Mean + SD over 10 shuffled evaluation)
Model Category
Question HRR (%)
Answer HRR (%)
Overall HRR (%)
GPT-4o
30.74% (+ 4.95%
23.33% (+ 2.92%
26.23% (# 2.85%
GPT-4.1
22.22% (+ 2.47%)
14.26% (# 1.25%)
17.10% (# 1.14%)
OpenAT-ol
24.07% (+: 4.70%)
18. 18% (+: 2. 2.10%)
20.58% (+ 2.72%)
Gemini 2.5 Pro
18. 18.15% (+: 3.68%)
9.07% (# 1.84%)
12.32% (t 1.41%)
Claude3.7
13.33% (# 2.59%
22.96% (# 1.79%)
21.59% (# 1.59%)
DeepSeck-Vy V3
0.00% (+ 0.00%)
2.69% (# 0.92%
2.10% (# 0.72%)
DeepSeck-RI
22.59% (t 4.08%)
12.04% (# 3.18%)
16.23% (# 3.19%)
Qwen3-8B 8B
12.96% (+: 4.70%)
10.37% (+ 1.79%)
11.74% (+ 1.74%)
Qwen3-32B
14.81% (+: 3.49%
8.52% (# 3.17%)
8.52% (# 3.17%)
Swen3-30B-AMe
21.11% (+: 5.25%
17.22% (# 1.96%
18.99% (# 2.41%
Swen3-235B-A2225
23.33% (# 6.06%)
15% (t: 3.53%)
18.73% (t 3. 13%%
Both closed- and open-source models exhibited limited capability in hallucination rejection. The median HRR for closed-source models was 20.58%, compared to 13.99% for open-source models, which falls below the level generally required for reliable application in practice. For example, DeepSeck-Ve V3 performed strongly the adjuvant Q&A task (LLM Score 7.8) but obtained the lowest HRR (2.10%), highlighting the inconsistency between knowledge answering and hallucination rejection.

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These findings suggest that current models often rely surface-level language correlations rather than deeper domain reasoning, which constrains their ability to identify and reject incorrect content. Improving hallucination control in this setting will likely require Comain-adaptivg fine-tuning bined with structured knowledge representations, in order to enhance logical coherence and scientific reliability. Additional analyses are provided in Appendix
CONCLUSIONS AND LIMITATIONS
This work presents the first benchmark dedicated to adjuvants, combining 1,294 expert-annotar Q&A pairs and 1,364 formal descriptions. Using this resource, we systematically evaluated 11 closed- source and 19 open-source MILMs across open-ended Q&A, hallucination rejection, and instruction following. Our results highlight comparatively strong performance from the GPT, DeepSeek-RI. and Qwen3.0 families, and we propose a formal framework that abstracts adjuvant design principles and immune mechanisms into structured representations support future domain-speciffo models.
While our study provides an initial foundation, further progress will require stratified benchmarks to capture varying task diffieulty, Nomain-adaption training for expert knowledge integration, and hybrid neuro-symbolic architectures that leverage the proposed formal framework. Beyond technical evaluation, the benchmark and formal abstractions may lower the entry barrier for applying MLLMs in immunology and help systematize reasoning in vaccine adjuvant research. These resources are intended solely for research purposes and should not be used directly in clinical contexts without expert validation.
ACKNOWLEDGEMENT
This work is sponsored by CAS Project for Young Scientists in Basic Research (YSBR-083), Strategic Priority Research Program of Chinese Academy of Sciences (No. XDA0480000, XDA0480200 and XDA0480204), Young Scientists Fund of The State Key Laboratory of Multimodal Artificial Intelligence Systems (No.ES2P1001 10), 2035 Innovation Mission Project of CASIA (N0.E4)10102), Zhongguancun Academy Project No.02012501.
ETHICS STATEMENT
This paper adheres to the ICLR Code of Ethics. We acknowledge that our research follows ethical guidelines, ensuring compliance with all relevant regulations.
Human Subjects: No human subjects were involved in this research. All data used in the study are publicly available datasets or generated synthetically.
Data Privacy and Security: We have taken care to ensure that all data used complies with relevant data protection laws. Datasets used in this research do not contain any personal, confidential, or sensitive information.
We confirm that the research presented is original and has not been plagiarized. All sources and data are properly cited and documented.
Research Integrity:
Legal Compliance: We confirm that the research complies with all applicable laws and ethical standards, including data usage and research practices.
This statement is meant to address potential concerns in accordance with the ICLR Code of Ethics.
REPRODUCIBILITY STATEMENT
We ensure that our work is reproducible by providing all necessary resources for others to replicate our experiments. Specifically:
Benchmark Data and Experimental Setup: All datasets used in the experiments, along with detailed data processing steps, are publicly available. The experimental setup is also
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provided, including hyperparameters, evaluation metrics, and the benchmarking environ- ment.
The code used to process the data and conduct the benchmarking experiments is available in anonymous repository. This includes all scripts necessary for replicating the experiments as described in the paper.
Source Code:
By providing these resources, we aim to make our results fully reproducible and facilitate further research based on our work. The details required for reproduction can be found in the anonymous repository.
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APPENDIX OVERVIEW
This appendix provides supplementary material to support reproducibility and clarity. is organized follows:
Appendix A: Dataset Construction
Additional statistics, distributions, and category breakdown.
Appendix B: Data Preprocessing and Expert Annotation
B.1 Details of the preprocessing pipeline.
B.2 Details expert annotation workflow, quality control procedures, and inter-
annotator agreement notes.
Appendix C: Representative Q&A Examples
Selected samples from the benchmark illustrating different knowledge categories.
Appendix D: Formal Framework
D1.
Definitions of formal variables.
D2.
Definitions of formal functions.
D3
Definitions of functional transfer relationship.
Appendix E: Evaluation Metrics
Mathematical formulations of Semantic Textual Similarity (STS).
Mathematical formulations of BERTScore.
Definition of LLM Score and consistency verification between the human expert
score.
Mathematical formulations of Hallucination Rejection Ratio (HRR).
Appendix F: Experimental Analyses
F.1
Extended results on prompt-following.
The State-of-the-ar model category comparisons.
Evaluation in visual-related subsets.
Error visualization analysis of domain-specific models.
Appendix G: Prompts
G.1
Data generation prompt templates.
G.2
Evaluation prompt templates.
Appendix H: Word Cloud Visualization
Supplementary visualizations of key concepts and terminology distribution.
Appendix I: Usage of Large Language Models.
The usage of Large Language Models in this paper.
A DETAILED DATA FOR CHART
A.I DISTRIBUTION OF THE ADJUVANT BENCHMARK
Table 7: Distribution of the Data Types
Category Open-ended Q&A
Hallucination
Adjuvants Formal
Count
1294
69
1364
A.2 DISTRIBUTION OF MILMS FOR GENERATING Q&A DATA
A.3 SUBJECTIVE EVALUATION MLLMS IN GENERATION
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Table 8: Distribution of MILMs for Generating Q&A Data
Model
DeepSeek-R1
GPTT4o
Claude3.5-Soneet
Frnie4.0-Turbyy
Count 559
471
143
121
Table 9: Subjective Evaluation MLLMs Generation
Ability
GPT-4o
Cladue3.5
Erine4.0
DeepSeek-R1
Questioning
8.0
6.4
6.9
7.9
Answering
7.0
6.5
7.4
8.1
Reasoning
7.7
7.1
7.7
8.0
Knowledge Reserve
7.0
6.9
7.1
7.5
Chart Analysis
6.2
5.3
5.0
7.7
Context Utilization
7.2
7.1
7.8
Instruction Following
8.0
6.5
6.0
7.5
Questioning Ability
Answering Ability
Ressoning Abiltyy
Knowledge Reserve
Chart Analysis
Contect Utilization
Instruction Following
GPT-4o
Cladun3.5
DeepSeeic-11
Figure 5: Visualization of Subjective Evaluation in MLLMs Generation
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B DATA PREPROCESSING AND EXPERT ANNOTATION DETAILS
B. DATA PREPROCESSING
The corpus consisted of 739 peer-reviewed research articles and two classic texthooks in im- munology and vaccine adjuvants. We employed a document parsing pipeline based on PyMuPDF + pytesseracee extract structured text while preserving paragraph hierarchy and separating embedded figures. Both extracted text and figures were then provided context to multimodal models (e.g., GPT-4o). For unimodal models such as DeepSeek-RI. figures were processed with an OCR engine to obtain textual content, ensuring consistency across evaluations (Fig. [6. All outputs were subsequently reviewed, and only accurate and relevant Q& A pairs were retained the benchmark.
H
Images
pytesseract
A
Papers & Books
PyMuPDF
Structured Text
MLLMs & LLMs
Text
Figure 6: Data preprocessing pipeline.
From the processed corpus, 1,500 candidate Q&A items were randomly sampled for expert review.
B.2
EXPERT ANNOTATION
B.2.1
EXPERT TEAM
Annotation carried out by 13 specialists spanning complementary areas of vaccine research:
Infectious disease vaccines: senior researcher, 2 PhD students, 3 MSc students.
Cancer vaccines: researcher, PhD students, MSc students.
Bacterial vaccines: 2 MSc students.
All annotators had domain training in immunology or vaccine-related research.
B.2.2 EXPERT ANNOTATION WORKFL.OW
Annotation followed standardized guidelines co-developed by Al and immunology experts. The workflow was:
1. Define goals: establish the first domain-specific benchmark for adjuvant research.
2. Curate source material: collect high-quality PDFs of papers and textbooks.
3. Determine data requirements: textbooks for foundational knowledge; research articles for advanced content (mechanisms, design principles, safety).
4. Pilot phase:
generate trial items with MILMM; refine through expert feedback.
5. Batch generation: perform large-scale API-based generation once validated.
6. Annotation protocol: assess each item for (i) correctness of the question, (ii) correctness
of the answer, (iii) validity of reasoning. (iv) overall quality. Incorrect questions + question hallucinations; incorrect answers = answer hallucinations.
7. Standardization: annotators trained to rely exclusively on the source text, avoiding subjective inference.
8. Validation: first-round expert review followed by quality control from Al researchers before later batches.
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B.2.3 CONSISTENCY ASSURANCE
To guarantee reliability and minimize subjectivity, we adopted the following measures:
Reference standard: The original source text was defined as the sole criterion for correct- ness, preventing reliance on prior knowledge or subjective inference.
Unified training: All annotators underwent standardized training and participated in trial phase before formal labeling.
Joint calibration: Approximately 30% of the samples were jointly annotated to align
interpretations across experts.
Independent labeling with discussion: The remaining 70% of samples were labeled
independently, with ongoing discussions to resolve uncertainties.
This multi-step protocol ensured consistent, transparent, and reproducible labeling across the bench- mark.
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C CASES FROM ADJUVANT BENCHMARK
Question:
what mechanisms contribute to the diversity of the antigen receptor repertoire lymphocytes?
Reasoning Process: The diversity of antigen receptors is generated through somatic
recombination, which includes rearrangements of gene segments and the addition of nucleotides.
Answer:
Somatic recombination, combinatorial diversity, junctional diversity, and somatic hypernutation contribute to the diversity antigen receptors.
Figure 7: A Case from Basic Knowledge Open-ended Q&A
aGC into the lipid bilayer affect the kinetics and efficacy of the immune response?
Question: the incorporation
MPLA and aGC are lipophilic adjuvants incorporated into the lipid bilayer of the particles. MPLA serves as a TLR4 agonist, promoting sustained immune responses through activation of dendritic cells and B cells. aGC, a glycolipid, stimulates invariant natural killer T cells through CD1d presentation, leading to rapid antibody production These adjuvants enhance both the magnitude and duration the antibody response by engaging different immune pathways.
Reasoning Process:
Answer: MPLA enhances sustained immune responses by activating TLR4 pathways, while aGC promotes rapid antibody production through invariant NKT cell activation. Their incorporation into the lipid bilayer results in more effective and durable immune response by engaging complementary immune pathways.
Figure 8: A Case from Advanced Knowledge (Biological Principles) Open-ended Q&A
Question: what structural features of the VLPs are revealed by electron microscopy, and why are these important in Fig?
Electron microscopy images show the size and surface features of VLPs, such as spikes that mimic natural influenza virions, indicating proper assembly and potential for effective antigen presentation.
Reasoning Process:
Answer:
Electron microscopy reveals VLPs with a diameter 80-128 nm and surface spikes, indicative proper assembly and potential to mimic natural virions for effective antigen presentation.
Figure 9; A Case from Advanced Knowledge Vision-related Open-ended Q&A
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Question:
what does Fig reveal about the cellular immune response to #F-adjuvanted vaccines?
A
Coating cationic polymers
PLA MPs
PLA MPs
PLAPE
TUAPO
PLA
PLA-
PLA-
Reasoning Process:
Understanding cellular immune response data is crucial for evaluating
vaccine efficacy.
Answer: Fig reveals the levels IFN-Y produced, indicating the cellular inmune response elicited by aP-adjuvanted vaccines with different surface charges, suggesting that more positively charged particles may induce stronger cellular response.
Figure 10: A Case from Advanced Knowledge Vision-related Open-ended Q&A
How do the material properties of PBC micelles (e.g., size, charge, CMC) contribute to their adjuvant function?
Question:
Reasoning Process: 1. PBC micelles have a 38.4 nm diameter, +5.82 mV zeta potential, and CMC 8.3 pg/ml. 2. Cationic charge promotes Ag binding and APC uptake. 3. Sub-CMC unimers lack micellar structure, failing to cross-link BCRs.
Answer: PBC micelles' nanoscale size (38.4 nm) enables efficient lymphatic drainage and APC uptake. The cationic charge (+5.82 mV) facilitates electrostatic Ag binding and BCR interaction. A low CMC (0.3 pg/ml) ensures micelle stability in physiological conditions, whereas unimers (below CMC) cannot scaffold Ag. These properties collectively enable efficient BCR mooss-linking and humoral responses.
Figure 11: A Case from Advanced Knowledge (Adjuvant Design) Open-ended Q&A
Question: What are the potential safety concerns associated with PEG-PAEEP, and how are they addressed?
Reasoning Process: Safety concerns
PEG-PAEEP include hemolysis and cytotoxicity.
study evaluates these aspects by comparing the hemolytic activity cytotoxicity
PEG-PAEEP with those of aluminum adjuvants, demonstrating that PEG-PAEEP has significantly hemolytic activity and comparable or lower cytotoxicity at effective concentrations.
Answer: PEG-PAEEP demonstrates lower hemolytic activity and comparable or lower cytotoxicity than aluminum adjuvants, addressing potential safety concerns effectively.
Figure 12: A Case from Advanced Knowledge (Adjuvant Safety) Open-ended Q&A
Question: what safety and usability considerations are addressed for these cpG-loaded particles?
The of redox-responsive, biodegradable polypeptide carriers that the particles be safely degraded after achieving their function. Additionally, the controlled release of minimizes systemic exposure, reducing the risk of adverse effects associated with widespread immune activation.
Reasoning Process:
Answer:
Safety and usability are enhanced by using biodegradable carriers and controlled release to minimize systemic exposure and potential side effects.
The article does mention the controlled release of cp6 by the particles. Although particulate adjuvants often have the capability for controlled release, this is not the case in this article. This reflects that the general model has deep understanding of conventional common knowledge, but still falls short in in-depth exploration of the specific article.
Reason for Error:
Figure 13: A Case from Question Hallucination Generation by GPT-4o
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Question: did experimental design that the observed immune responses the multimerization of antigens nanoparticles?
Reasoning Process: The design involved comparing immune responses from mice immunized with soluble antigens, homotypic nanoparticles, and mosaic nanoparticles. The inclusion of different control groups allowed the researchers to isolate the effects of antigen multimerization on immune response.
Answer: The experimental design included controls such soluble antigen immunization, which did not elicit cross-reactive responses, thereby demonstrating that multimerization nanoparticles was responsible for the enhanced immunogenicity observed.
Reason for Error: The immune response of soluble antigens and nanoparticle antigens needs to evaluated by the antibody titer (this the most important) and cross-reaction.
Figure 14: A Case from Answer Hallucination Generation by GPT-4o
Question: does the design of CpG-loaded particles enhance cell responses and persistent antibodies?
The particles are designed to facilitate efficient delivery and prolonged release of Cpō to pDCs, which are crucial for initiating strong adaptive immune responses. By activating pDCs, the particles enhance antigen presentation and cost costimulatory signals, promoting robust cell activation and aiding the development of long-lasting antibody responses.
Reasoning Process:
Answer: Design enhances T cell responses and antibodies by ensuring effective CpG delivery and sustained pDC activation, promating strong adaptive inmunity.
First, the article does not include indicators related to cells and antibodies, so the question itself goes beyond the scope of the article. While the reasoning might be correct in general, there is not much content in this article it, as it only covers enhanced delivery and PDC activation. The answer is even more erroneous, as mistranslates cells antibodies and reverses their order with pDc activation, while also focusing entirely on aspects that were not mentioned in article.
Reason for Error:
support
Figure 15: A Case from Q&A Hallucination Generation by GPT-4o
What further research would needed to develop these RV-VLPs into viable rabies vaccine candidate?
Question:
While the article demonstrates promising initial results, several additional steps would be needed to develop this into a viable vaccine candidate. These would likely include optimizing production and purification processes, conducting more extensive immunogenicity studies, evaluating protective efficacy against rabies virus challenge, assessing long-ters immunity, and conducting safety studies.
Reasoning Process:
Answer: Further research needed would Likellyinclude::.ggiiiiiiiig RV-VLP production and purification processes.
Figure 16: A Case from the Invalid Data Generation by Claude3.5: Incomplete Answer
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D ADJUVANT FORMAL DATA
D.1 DEFINITION OF ADJUVANT FORMAL VARIABLES AND FUNCTIONS
D.1.1 FORMAL VARIABLES
Vaccines (V): The vaccine, if there are multiple new vaccines, they can defined as V {Vacı, Vacz,.... .... Vaen].
Experimental Group (EG): Defined as Vac, = EG.
Control Group (CG): Defined as Vac, = CG.
Original Viral Surface Antigen (Ag): The original viral surface antigen defined as Ag. there are multiple antigens, they can be enumerated as Agı, Ag2,....
Structural Configurations (Struc): If antigens possess specific structural configurations, such as particles dimers, these structures are described and defined as Struc. Multiple structures may be defined as
Antigens in New Vaccines (Ag): In new vaccines, the antigens employed are similarly defined as Ag.
Adjuvant Forms (Struc): The forms of adjuvants include small molecules, particles, gels, inorganic salts, vesicles, and others. If the literature explicitly specifies the structural forms of adjuvants (e.g.. nanoparticles, microparticles, etc.), these adjuvants should be formally incorporated and defined as Adj1. Adj2, and so forth.
Movement Variables Related to Vaccine Delivery: Examples include residence (Stay at), drainage (Drain to), and targeting (Target to), as illustrated below:
Stay at the injection site lung / gut/....
Stay at Mnjection,,i/LLnngn/
Drain to lymph nodes / spleen / bone marrow/....
Drain to Lymphhaae/Sppeeeeee Sppleen/ /Bone
Targeted delivery lymph nodes / spleen / bone marrow / dendritic cells T cells
Target to Lymph /Spleennee/Slleen/ /Sppee//Boeennnoneeennee /Bo///aron/////e//eeeaaaaaaaaaal /DCs//CCCS// Ceess//TTss/......
Definitions of Innate Immune Cells:
Conventional Dendritic Cells (cDC1): Celli =
cDC1 injection site
Moroeyle-derived Macrophages (MoM): Cellp MoM in peripheral blood
Vissue-resident Macrophages (TRM): Cell3 = TRM in spleen
Neutrophils: Cell, Neutrophils in lymph nodes
Plasmacytoid Dendritic Cells (pDC): Cells = pDC peripheral blood
Maturation-induce indduced Macrophages: Cellg Mature Macrophages in tissues Actions of Innate Immune Cells:
Recruitment and Activation of DC: Recruit / Activate of DC
Uptake of Antigen / Adjuvant/ Other by DC: Antigen Adjuvant/ Uptake of DC Secretion of Cytokine / Chemokine / Other by DC: Cytokine / Chemokine / ... Secret of DC Antigen Presentation by DC: Antigen presentation of DC
Migration of DC: Migrate of DC
Phagocytosis by DC: Phagocytosis of DC
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Costimulation of T cells by DC: Costimulation of T cells by DC
Cytotoxic activity of DC: Cytotoxic activity DC
Definitions of Various T Cells:
Follicular Helper Cells (Tfh): Cell, = Tih lymph node
CD4+ T Cells: Cell2 CD4 T cell in peripheral blood
CD8+ Cells: Cell3 CD8 cell in spleen
Thl Cells: Cell. Th1 in lymph node
Th2 Cells: Cell, Th2 in spleen
Memory T Cells: Cell, = Memory T cell in peripheral blood
Actions of T Cells:
Proliferate: Movement = Proliferate of Cell
Activate: Movement Activate of Cell
Differentiate: Movement = Differentiate of Cell
Secref: Movement Secrete of Cell
Migrate: Movement = Migrate of Cell
Mutate: Movement Mutatee of Cell
Definitions Cells:
Germinal Center B Cells (GCB): Cell; GCB in lymph node and spleen
Plasma Blasts: Celly Plasma Blast peripheral blood and lymph node
Plasma Cells: Cellg Plasma Cell in bone marrow
Memory B Cells: Cell, = Memory B cell in bone marrow and lymph node
Long-Lived Plasma Cells: Cell, Long-Lived Plasma Cell in bone marrow
Actions of B Cells:
Proliferate: Movement Proliferate of Cell
Activate: Movement Activate of Cell
Differentiate: Movement = Differentiate of Cell
Migrate: Movement Migrate of Cell
Mutate: Movement = Mutate of Cell
Definition of Antibodies:
Antibodies have three defining criteria: the source of the body fluid (such as serum or brolchoalveolar lavage fluid), the target antigen (defined according to the strueture of the antigen established in the first step), and the type (such as IgG, IgA, or simply Antibody, which must refer to the terminology used in the literature).
Each type of antibody must be defined structurally, following the format:
Ab = "Type" to "Antigen" in "Body Fluid Type"
Example: The antibody IgG to antigen Ag in serum:
Ab IgG to Ag in Serum
Actions of Antibodies:
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Secrete:
Movement refers to the increase in antibody titers, which can be interpreted as increase
in antibody secretion.
Movement Secrete of Ab
Affinity: Movement refers to the enhanced binding of antibodies to viruses, interpreted as an increase in affinity.
Movement = Affinity of Ab
Movement refers to the enhanced binding of antibodies antigens from other variants, interpreted as an increase in cross-reactivity.
Cross-reactiviy:
Movement Cross-reactivity of Ab
Neutralization: Movement refers to the ability of antibodies to block pathogenic activity through
target engagement.
Movement Neutralization by Ab
Definitions of Other Immune Reactions:
In the text, there may be summary- descriptions of immune responses, such as GC responses and T cell responses. Sometimes, it is not appropriate to define them as the above movement variable, instead, these immune responses be directly defined movement variables.
Example: The GC response in the lymph node:
Movement GC responses in lymph node
D.1.2 FORMAL FUNCTION
Representation of Composition (Form): If antigen Agı forms structure Struci, it can be expressed as:
Form(Struci,
The function Form(A, BA,//CCD/.... represents the composition and refers to the assembly of substances or antigens B/C/D/ ... into A (adjuvants, structures, etc.).
Loading: The function Load(A, A, B, Inside/Surfacel...) refers to loading A into the inside or on the surface of B.
Load A, B, Inside)
Load( A, B, Surface)
Mixing: The function Mix(A, B) refers to simply blending A and B together.
Mix(A,
Chemical Coupling: The function Länk(A, B) refers to chemically connecting A to B via chemical bonds, protein linkage systems, or linkers.
Link(A, B)
Custom Combination Method: If new combination methods are specified in the literature, they should be defined appropriately, for example, function Combine(A, B) may represent new method of combination.
Combine( A, B)
D.1.3 FUNCTIONAL TRANSFER RELATIONSHIP
Comparative Relationships Between Experimental Group and Control Group:
Example 1: Experimental group EG enhances the action (Movement variable) compared to the control group CG.
EG Enhance to CG at at.......
Example 2: Experimental group EG reduces the action (Movement variable) compared to the control group CG.
EG Reduce CG at ./......
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For continuous comparative relationships, only when the original text explicitly contains words such as prolong, extend, persistent, sustained, or durable, can the persistent comparative relationship be established.
Persistent Comparison:
Example 3: Experimental group EG prolongs the action (Movement variable) compared to the control group CG.
EG Prolong to CG at.......
Transfer Relationships:
Causal relationship: For causal relationships that are explicitly stated or indicated in the article, use the symbol >> to represent them.
Comparative relationships and the conjunction: Use the symbol & to represent comparative relation- ships and the conjunction immune pathways.
Further extend causal relationships: After establishing clear causal inference, it is possible to further extend this causal chain based on existing background knowledge. However, only the actions and indicators explicitly stated in the article may be used, and no new actions can be introduced. Only the existing causal relationships between actions may be supplemented, represented by the symbol
D.2 THE CASES ADJUVANT FORMAL DATA
Adjuvant Formal Language:
Ag_1 Trp2 peptide from melanoma
Adj_1 PSA micelle
Struc Cationic micelle
Ag_2 Form(Struc_L, Ag_1)
Adj_1 Form(struc_1, ,,, polyethylenimine (PEI-2K)/stearic acid)
Vac1 2Aad(Ag_2, Adj_1, Inside) EG
Vac2 Mix(Ag_1, Adj_1) = CG1
Vac3 Ag_ CG2
Natural language Text: study introduces novel vaccine formulation where micelles utilized to deliver the Trp2 melanoma antigen peptide. These micelles demonstrate an average size of 28.7 8.2 nm with near-perfect encapsulation efficiency of the antigen. conjugating branched PEI-2k with stearic acid, forming
The PSA micelles are prepared amphiphilic structure. The PSA micelles enhance sptigin-specific CTL responses and show a preferential accumulation in draining lymph nodes, thereby minimizing systemic toxicity. This vaccine is compared against contrals such as free Trp2 and mixture of Trp2 with empty PSA micelles to assess its efficacy in enhancing immune responses and inhibiting tumor growth in a murine melanona model.
Figure 17: A Case from Adjuvant Formal about Adjuvant Design
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Adjuvant Formal Language:
Adjuvant Definition: Adj PEG-PAEEP EG
Control Group Definition: Aluminum adjuvants
Immune Component Definitions:
Movement_ Speciicp IgG titers
Cell = Igo-producing cells in bone marrow
Movement_2 Differentiate Cell_1
Combining Comparison Relationships and Immune Components:
EG Enhance to CG at Movement_1
EG Enhance to CG at Movement_2
Causal Relationship Inference:
Enhance to C6 at Movement_1
Enhance to at Movement_2
Comparative Relationships Extraction to Simpl ify Causal Chains:
EG to CG
Enhance (Movement__) 1) >> inhaenceMovemeet...
The PAE-P copolymer enhances the inmune responses of the HBsAg VLP vaccine compared to aluminum adjuvants, inducing significantly higher HBsAg-specific IgG titers in mice after the second inmunization.
Natural language Text:
Figure 18: A Case from Adjuvant Formal about Adjuvant Activation and Immune Process
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E EVALUATION METRICS
Traditional n-gram- based metrics, which rely on character or token overlap, are not well suited to open-ended question answering. They emphasize surface similarity and often miss deeper semantic alignment. We therefore adopt Semantic Textual Similarity (STS) and BERTScoree, which operate at the semantic level rather than raw overlap. addition, we report an LLM-bbaed score that complements embedding metrics by explicitly rating answers along predefined rubrics (similarity, rationality, inclusiveness). Together, these metrics provide complementary perspectives and allow for more reproducible and transparent evaluation.
To assess hallucination rejection, we leverage hallucination data from the adjuvant benchmark. Models are prompted with expert-annotated incorrect Q&A and asked to judge whether sample invalid; the resulting hallucination rejection ratio (HRR) measures the proportion of correctly rejected items.
E.1 SEMANTIC TEXTUAL SIMILARITY (STS)
STS evaluates the semantic proximity between two texts via cosine similarity of sentence embed- dings. We compute embeddings with the SentenceTrans all-mpnet-base- Reimers & Gurevych (019)).
Python module (model:
Given texts Tj and T2 with embeddings E(T1) and E(T2), the score is
E(Ti) E(7b)
STS(TI, 72)
(1)
[E(T; B(T2
The value ranges from -1 to with larger values indicating stronger semantic alignment.
E.2 BERTSCORE
BERTScore computes token-level semantic similarity using contextual embeddings. For generated text G and reference C, we form a similarity matrix
B(G.) B(C,)
S. = EEG [(C)
take P(i) max, Stg as the best match for token G3, and average:
BERTScore G
P(i).
Scores are normalized to [0, 1] (higher is better). Compared with STS (sentence-level semantics), BERTScore emphasizes token-level precision. For our BERTScore calculations, we use the www.:rttsscvvccb.uuccaaeeee model Bellagy et al. (2019) with its corresponding tokenizer.
E.3
LLM SCORE
To complement embedding-based metrics, we employ an LLM-based evaluation with GPT-4o and DeepSeek-R1. Each candidate answer assessed along three dimensions:
Similarity Score (SS): factual alignment with the expert reference answer.
Rationality Score (RS): scientific soundness and logical coherence of the reasoning process.
Inclusiveness Score (IS): coverage of essential points and completeness in addressing the question.
Each dimension is scored on [0, 10] scale, with the following interpretation in the context of adjuvant knowledge:
0-3 (poor): major factual emrors or incoherent reasoning, reflecting a lack of basic under-
standing of adjuvant concepts.
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4-6 (adequate): partially correct and logically consistent answers, but with noticeable gaps or oversimplifications in immunological mechanisms or design principles.
7-10 (strong): scientifically consistent, well-reasoned, and comprehensive answers that demonstrate solid grasp of adjuvant biology and related immunological processes.
These three dimensions are deliberately chosen for the adjuvant domain: factual alignment (SS) captures accuracy of immunological details, rationality (RS) reflects whether the explanation is mechanistically plausible, and inclusiveness (IS) ensures that answers go beyond isolated facts to integrate the multifaceted nature of adjuvant design and immune modulation.
Why LLM Score valid: Using large language models as evaluators ("LLMMas-a-juugee)) has become widely adopted practice in open-ended evaluation. Prior studies in general NLP benchmarks demonstrate that LLM-basee judgements correlate strongly with human preferences when prompts and rubrics standardized, and also analyze typical biases such verbosity self-preference Zheng et al. (2023): Dubois et al. (2024); Kim et al. (2024); Panickssery et al.] (2024); Xie et al. (2025a).
Beyond general-purpose tasks, recent work shows that LLM-based evaluation is also effective in scientific and biomedical domains. For instance, scientific question answering and biomedical information extraction have employed rubric-guided LLM judges to approximate expert assessment D Souza et al. (2025): Laskar al. (2025). In the medical domain, Wang al. (2025a) integrated LLM evaluators into a formal framework for clinical ambient scribing, published in a Nature journal. Similarly, mathematical reasoning tasks have used L.M judges to assess solution validity under verifiable criteria (Seehann et al. (202)). These precedents confirm that LLM-as-a-juude is not only scalable but also increasingly recognized across scientific subfields.
In our setup, we (i) fix prompts and decoding parameters to minimize variance, (ii) average scores from two nigh-performing evaluators (GPT-40 and DeepSeek-RR to reduce single-model bias, and (iii) complement LLM-based scores with embedding metrics (STS/BERTScore) for transparency and reproducibility. This multi-pon multi-perspeective design yields a reliable proxy for expert assessment while keeping annotation costs tractable, aligning with best practices reported across both general and scientific domains.
E.3.1 EXPERIMENT OF CONSISTENCY VERIFICATION BETWEEN LLM SCORE AND HUMAN EXPERT SCORE
To verify the effectiveness of using LLMs as judges in the field of adjuvants, we conducted consistency test between LLM scores and human expert scores. Specifically, we randomly selected 100 responses from all models, with 50 evaluated by GPT-4o and the other 50 evaluated by DeepSeek- R1. These 100 samples were then submitted to human experts for evaluation, using criteria that were completely consistent with those used for the LLM scores. The results are presented in Table TO
Table 10: Consistency Verification Between LLM Scores and Human Expert Scores
Scoring Model
Rating Dimension
Pearson Correlation Spearman Correlation
Kendall Correlation
Similarity Rationality
0.8412
0.8469
0.7752
0.8006
0.6342
0,5793
GPT-4o
Inclusiveness
0.8135
0.7487
0.6749
Avg
0.8407
0.8044
0.7084
Similarity
0.9145
0.9236
0.8185
Rationality
0.8767
0.8456
0.7732
DeepSeek-RI
Inclusiveness
0.8636
0.8749
0.7613
Avg
0,9125
0,9247
0.8019
Similarity
0.8854
0.8920
0,8019
Rationality
0.8443
0.7986
0.7239
Inclusiveness
0.8461
0.8416
0.7413
Avg
0,8803
0.8871
0.7739
The experiment and visualization (as shown in Fig 19p demonstrate high reliability in using these models as scorers. Both GPT-4o and DeepSeek show strong linear correlations with expert scores, particularly in the Similarity dimension, where GPT-4o achieves Pearson correlation of 0.8412 and
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(a) Similarity: GPT-4o
(b) Rationality: GPT-4o
(c) Inclusiveness: GPT-4o
(d) Similarity: DeepSeek-RI
(c) Rationality: DeepSeck-Rl
(f) Inclusiveness: DeepSeck-RR R1
Figure 19: LLM Score vs. Expert Score
DeepSeek reaches 0.9145. Although GPT-4o's correlations slightly lower, it remains consistently strong across all dimensions, indicating that both models align well with expert evaluations and are effective scoring agents.
Comparisan of Export Average [Connected by Dashed Lines]
Average Scane txpas Average
10
Acrragg State (Theos Dimuraan))
25
Sample Index (Al Sampless)
Figure 20: Comparison of LLM and Expert Average Scores
Further analysis using Spearman correlation reveals that DeepSeek outperforms GPT-4o, especially in the Similarity (0.9236) and Rationality (0.8456) dimensions. Despite GPT-4o's 's lower performance in Rationality (0.6342), it still maintains reasonable level of ranking consistency, reinforeing its validity as a scorer. The Kendall correlation results mirror these findings, with DeepSeck consistently showing higher scores across all dimensions, particularly in Similarity (0.8185 and Rationality
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(0.7732). Although GPT-4o's performance is lower in comparison, it remains within acceptable range, particularly in Similarity (0.7752), which indicates a strong consistency in ranking with expert scores.
Overall, both GPT-4o and DeepSeek show strong alignment with expert scores, as demonstrated in Fig 20 The average scores across the three dimensions for both the LLM and expert evaluations largely overlap, confirming their reliability as scorers for this task. While DeepSeek slightly outper- forms GPT-4o, particularly in Pearson and Spearman correlations, both models exhibit substantial effectiveness and are well-suited for scoring tasks in this domain.
E.3.2
THE PROMPT OF LLM SCORE
The Prompt of LLM Score Evaluation
You are expert in immunology and adjuvant design, with great achievements in immunol- ogy and adjuvant design. With the aim simplifying the thinking process, please score the model's answers and labels (out of 10 points) based the similarity between the answers and labels, the reasonableness of the answers, and whether the answers incorporate the meaning of the labels. Finally, please provide the results in the following format: Similarity Score:
Rationality Score: X
Inclusiveness Score:
E.4 HALLUCINATION REJECTION RATIO (HRR)
The hallucination rejection ratio (HRR) quantifies a model's ability to detect and resist hallucinated content:
Number of correctly rejected hallucinated samples 100%.
HRR
Total hallucinated samples
Here, the numerator counts expert-labeled hallucinated Q&A pairs that the model successfully flags as invalid. A higher HRR indicates stronger robustness, which is particularly important in safety-sensitive biomedical applications.
Prompt design. The evaluation prompt explicitly defines the label mapping: "Yes" means the Q&A scientifically valid, while "No" means it contains hallucination (i.e., invalid claims). Additional criteria are listed (factual errors, logical inconsistencies. causal reversal, pscudo-scientific claims, domain irrelevance, or incomplete answers). In practice, the model is instructed to return "No" whenever any of these problems occur, and "Yes" only when the Q&A is entirely correct and domain- relevant. The requirement to "strictly return Yes or No" ensures output consistency and facilitates automatic scoring; it does not prevent verification, since the ground truth hallucination labels were already provided by domain experts during dataset construction.
Sample size. The current ballucination dataset consists of 69 high-quality, expert- annert-a examples. While relatively small, represents the first curated hallucination resource in the adjuvant domain. To mitigate sample-size concerns, report mean HRR with standard deviations over ten repeated trials. This provides an uncertainty estimate and ensures that performance differences reflect systematic model behavior rather than random variation.
Together, this setup ensures that HRR measures what it is intended to capture: whether models can resist generating endorsing scientifically invalid claims in immunology and adjuvant science.
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E.4.1
THE PROMPT OF HALLUCINATION EVALUATION
The Prompt of Hallucination Evaluation
You are an expert in immunology and adjuvant design, with great achievements in the immunology and adjuvant fields. Determine if the provided Question its corresponding Answer contains scientifically invalid claims in the fields Biology. Immunology, or Adjuvant Science. Return only "Yes" (valid) or "No" (invalid) without explanations.
Input Format:
Question: [Insert question text]
Answer: [Insert answer text]
Evaluation Criteria
Return "No" if ANY of the following apply (including but not limited to):
1.Factual Errors
Contradicts established scientific consensus.
2.gogical Inconsistencies
Self-contradictory statements.
3. 3.Causal Reversal
Inverts cause-effect relationships.
..Psetf--Scientffic Claims
Unproven theories.
S.Domain Irrelevance
Topics outside hioley/mmuuooggddjjvaat science.
Incoopplete Answers Missing critical steps/meccanisms.
Return "Yes" ONLY if the Q&A pair is scientifically accurate, logically consistent, and domain-relevant.
Response Requirement:
Strictly return "Yes" "No" in single line.
No markdown, formatting, or additional text.
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F EXPERIMENT ANALYSIS
F.1 EVALUATION OF PROMPT-FOLLOWING
We assessed the prompt-following ability of 11 closed-source and 18 open-source MLLMs under identical hyperparameter settings. Results are summarized in Table 11
Closed-source vs. open-source. Closed-source models consistently outperform open-source counter- parts, showing higher average STS and BERTScore when prompts are introduced. This indicates stronger instruction parsing and more reliable adherence to task requirements.
Performance shifts in open-source models. Interestingly, while prompts generally improved semantic alignment (higher STS/BERTTScore), several open-source models exhibited drop overall LLM Score. Manual inspection suggests that prompts sometimes pushed these models toward verbose or rigidly formatted answers, inflating token-level similarity but reducing factual soundness and completeness. This mismatch highlights trade-off between surface-level semantic alignment and deeper reasoning accuracy in open-source systems.
Model-specific variability. Domain-specific systems such BioGPT-Large perform particularly poorly without explicit guiding phrases, in some cases even degrading compared to their no-prompt baseline. For example, BioGPT-Large often required deterministic prefix (e.g., "The answer is:") to produce stable and interpretable outputs. Without such hints, its responses tended to diverge from the expected format, which explains its negative improvement in Table II and [
inference Model vs. Think Model. "Think" models with explicit reasoning mechanisms show relatively stable performance when prompts added, compared to inference-only models. This stability likely stems from their multi-step reasoning pathways, which already encourage adherence to task constraints. By contrast, inference models exhibit more variability, suggesting that they are more sensitive to prompt phrasing. Notably, gains in STS and SERre for both families are partly explained by prompts eliciting more domain-specific terminology, which boosts surface alignment metrics.
Summary. These findings highlight prompt-following as a key dimension of model robustness. However, improvements in surface-level metrics do not always translate to better overall judgment (LLM Score), particularly for open-source systems. This underscores the need for evaluation frameworks that disentangle genuine reasoning gains from superficial prompt-induced artifacts.
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Table 11: Evaluation of Adjuvants in Open-ended Q&& (Without prompt)
LLM Score (GPT-4o)
LLM Score (DeepSeck-R) RI)
Model Canegury
STS Scure BERTS
SS
RS
IS
S8
RS
IS
LLM Score Ang
Clused MILMs
Infervnce Models
GPT-4.1
0.7150
0.5219
6.6
7.7
Claduc35
0.7153
0.5598
5.8
7.8
6.2
6.4
8.6
6.6
6.9
Claduc3,7
0.7322
0.559
6.3
8.0
6.5
688
8.6
6.8
7.2
Gemini P..Pro
0.7123
62
Gemini2
0.6027
(ermini2.5.PPo
Emii33.5
0.7121
0,5512
5.8
7,8
6.2
6.2
8.0
6.0
Emac4.0
0,7337
0.6027
6.1
7.7
6.1
6.1
7.9
6.6
Douhuol,5 P-Prr
0,7093
0.5490
6.0
7,8
6.2
6.6
Think Madede
OpenAl-ol
Average
0.7150
0.5569
6.2
8.0
6.7
Open-source MILMSs
Infervnce Models
DeepSeck-V3
0.7255
0.5251
6.5
8.2
6.8
7.5
LLAVA1.5-78
0.7115
LLaVA1.5:1
S.Ve2.5-VLL-88
0,2008
0.5513
5.5
7.6
5,
5.7
Qwen2.5-V 5-V--72-
0.7054
0.5723
5-7
7.5
5.9
5.9
7.8
5.7
6.4
Interm112.5 12.888
0.6874
0.5606
5.0
6.7
5.0
5.1
6.4
5.5
Inserme12 5-78-78
0.7156
5.8
7.6
Interm/13.0-227 13.0-788
7.8
InstnudtBii-1338
0,6017
0.5711
4.8
6.1
4,4
4.9
0.6632
0.5022
4.9
6,3
4.6
4.9
5.8
4.2
5.1
Think Models
DeepSeck-R1
0.7360
6.6
8.4
Qwen3-SB
Owen3-32B
0.7193
0.5316
6.6
8.1
6.8
7,3
0.7174
8.5312
63
8.2
6.7
7.1
7.2
7.4
Qwen3 230-3-238--2222
0.7262
0.5443
6.5
9.4
6.9
7.2
8.8
7.4
7.5
Domatnn Specific Models
0.5995
3.1
4.0
Average
0.6942
0.5514
5.5
7.2
5.6
5.8
6.2
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F.2 THE STATE-OF-THE-ART MODELS VISUALIZATION ANALYSIS
We randomly selected two Q&A pairs for visualization of GPT-ol and DeepSeek-RI. The results are shown in Fig. and Fig. 22
Fig. 21 illlstratssss that DeepSeek-R1 tends to emphasize specific molecular-Ievel mechanisms, whereas GPT-ol provides higher-level framework descriptions. DeepSeck-RI responses
responssss align more closely with the style of professional scientific literature, highlighting systematic mechanisms and technical details, making it suitable for readers seeking in-depth understanding. In contrast, GPT-ol produces more concise and accessible answers, which better serve audiences with less biological background by facilitating a quick grasp of core logic. These differences likely reflect variations in training data and design objectives.
By contrast, Fig. [2 demonstrates a case where both models perform poorly. The example concerns the comparison of polyclonal antibody responses elicited by RBD-NP and HexaPro S vaccines in non- human primates against SARS-CoV-2 RBD mutations, particularly at site 484. Both models attempt analyze differences in antibody responses across vaccine platforms, but their reasoning and depth vary significantly. GPT-ol ' response remains general and framework -rive,, while DeepSeek R integrates more Nomain--spciff knowledge but the cost wver-speculation.
Both models, however, exhibit a similar misconception: they assume that nanoparticles can evade the E484K mutation. In reality, relevant studies indicate that both RBD-NP and HexaPro remain vulnerable to E484K (as shown in the ground truth answer), underscoring the importance of the antibody binding site at position 484. Neither model captures this crucial detail, instead incorrectly assuming that nanoparticle polyvalence confers resistance, while full-length proteins are more affected. This reflects a naive generalization from antigenic polyvalence to mutation resilience.
This case highlights the limitations of current general purposs models in handling fine- fine-grained grained immunological knowledge. It underscores the need for specialized models fine-tuned with domain- specific data achieve reliable reasoning in highly specialized biomedical contexts.
F.3 EVALUATION OF TOP 5 MLLMS IN VISUAL-RELATED SUBSETS
We selected the Top5 MLLMs that performed the best across the entire Open-ended Q&A when using the same OCR engine (as shown Fig These models were then evaluated on visually- subset, utilizing their native multimodal capabilities.
Table 12: Evaluation of Adjuvants in Open-ended Q&A Visual-related Subsets (With prompt)
(GPT-40)
(DeepSeek.B) R1)
Model Category
STS Score
BertScore SS
RS
IS
SS
RS
IS
LLM Score Avg
Engine
GPT-4o
0.7015
0.5729
6.3
7.9
6.7
5.6
9.1
5.9
7.0
GPT-4.1
0.7053
0.5442
7.2
8.5
7.6
6.9
9.3
7.0
7.8
Claduc3.7
0.7205
0.5720
8.0
6.9
6.3
9.2
7.3
Gemini2.5-Pro
0.7111
0.5787
6.5
8.0
6.8
6.5
9.5
6.4
7.3
OpenAl-ol
0.7436
Average
0.7164
0.5780
6.7
8.1
7.0
6.4
9.3
6.6
7.4
Multimodal Capubility
GPT-4o
0.7083
0.5889
6.5
7.9
6.6
6.0
9.0
6.1
7.0
GPT-4.1
0.7019
0.5255
7.0
8.3
7.3
6.9
9.4
7.0
7.7
Cladue3.7
6.9
0.6913
0.5628
6.0
7.7
644
6.1
9.2
6.3
7.0
OpenAl-ol
0.7390
0.6217
7.3
8.5
7.4
7.2
9.4
7.8
Average 0.7068
0.5733
6.5
8.0
6.8
6.4
9.3
6.5
7.3
The result is shown in Table 12 When comparing the two settings (OCR-based input vs. native multimodal capability), an intriguing trend emerges: some models actually perform worse when relying their native multimodal abilities. Overall, OCR-engine preprocessing provides more stable results across semantic similarity metrics (STS/BertScore) and LLM- evaluations (SS, RS, IS). Although certain models, such OpenAI-ol, maintain strong performance under the multimodal setting, several seneral-purpose MLLMs demonstrate noticeable degradation when tasked with interpreting biological and adjavent visualizations.
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We hypothesize two main reasons for this discrepancy:
1. Lack of domain-specifie expertise: General- purpose multimodal models excel in everyday visual-language tasks but lack optimization for specialized biomedical charts and adjuvant- related diagrams, leading misinterpretations.
2. Task representation gap: OCR-based pipelines provide structured textual input, reducing ambiguity. In contrast, multimodal settings require models jointly handle visual parsing and domain reasoning, which increases task complexity and may reduce overall performance.
In summary, this experiment indicates that relying solely on the native multimodal capabilities of current MILMs may not be the most effective approach for domain-spectt research tasks. While multimodal models show promise, traditional OCR-based preprocessing provides a more consistent and reliable input format, particularly when dealing with complex and specialized biomedical content. Future research should focus on improving multimodal reasoning by incorporating specialized visual inputs related to biomedical and adjuvant-specific domains.
Furthermore, in this study, all models were evaluated using unified OCR engine. This approach was chosen ensure consistency across evaluations by standardizing the input format, thus minimizing potential biases caused by variations in how models handle visual data. Given the specialized nature of the adjuvant-related visualizations, OCR preprocessing offers structured and reliable form of textual input, which reduces the risk of misinterpretation by models that may not be fully optimized this domain.
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Question:
How do sonicated reduced cancer cells (sRCCs) promote the recruitment and cells (APCS)?
activation of
Ground Truth Answer: SRCCs loaded with adjuvants like CpG promote the recruitment and activation of APCs by enhancing the uptake kinetics and magnitude of adjuvant uptake, leading to increased DC activation and cytokine production.
Sonicated reduced cancer cells (sRCCs) promote the recruitment and activation of antigen-presenting cells (APCs) by exposing cumor-associated antigens and releasing immuostimulatory signals referred to as associatd molecular patterns (DAMPs). When cancer cells undergo mechanical disruption via sonication, they break apart and release intracellular contents, including proteins, DNA, and other molecules capable of activating pattern recognition receptors on APCs. \n'nney points of this process include: \n\u2022 Exposure tumor antigens: Sonication reveals previously hidden or intracellular turor antigens. These antigens, now accessible environment, be taken up by nearby dendritic cells and macrophages. \n\u2022 Liberation of DAMPS: Molecules such HMGB1, ATP, and heat shock proteins are released upon cell membrane disruption. These DAMPs serve potent \u201cdanger signals\u201d that bind to pattern recognition receptors (e.g., Toll-like receptors) on APCs, triggering their maturation and promating proinflammttory cytokine production. \n\u2022 Enhanced antigen presentation: Once dendritic cells internalize these tumor-associated antigens, they process them and present antigenic peptides on MHC class I and II molecules, effectively priming T cells against turor cells. \n\u2e22 Local inflammatory milieu: The release DAMPs and proinflamatorry cytokines from activated APCs also helps recruit additional immune cells, amplifying the local inflarmatory response. This heightened environment fosters an improved recruitment of other effector cells, including cells and natural killer cells,
OpenAl-o1 Answer:
the extracellular
facilitating a broader antitumor response. \n\nOverall, the combination of tumor-specific signals (antigens) and danger signals (DAMPS) from SRCCs acts an immunologic \u201calarm, Au201d mobilizing and activating professional APCs in the turor microenvironment, thereby contributing to more robust and targeted antitumor imnune response.
LLM Scores:
GPT-4o: Similarity score: Rationality score: 8 Inclusiveness score: 6
DeepSeek-R1: Similarity score: 7 Rationality score: 9 Inclusiveness score: 7
DeepSeek-R1 Answer: Sonicated reduced cancer cells (sRCCs) enhance the recruitment and cells (APCs) through several interconnected
activation of antigen-presente
mechanisms: \n\n1. **Release of Damage-Associater Molecular Patterns (DAMPs):** \n Sonication disrupts cancer cells, releasing intracellular DAMPs such as ATP, HMGB1, heat shock proteins, and nucleic acids. These molecules bind to pattern recognition receptors (PRRs; e.g., TLRs, RAGE, NLRP3) on APCs, triggering their activation. For example: \n **ATP** activates the P2X7 receptor, promoting NLRP3 inflammasone formation and IL-1f
secretion. \n ***MGB1** interacts with TLR4 or RAGE, inducing pro-inflamatory cytokine production. \n *Nuuleic acids** engage endosomal TLRs (e.g., TLR3, TLR7/9) or cytosolic sensors (CGAS-STING), driving type interferon responses. An\n2. **Enhanced Antigen Availability and ** \n Sonic fragmentation increases the surface area and accessibility of tumor antigens. Smaller cellular debris facilitates efficient phagocytosis APCs, enabling robust antigen processing pross-presentation to CD8 cells, critical for antitumor immunity. An\n3. **Chemokine- Mediated **cruitment:** \n DAMP signaling induces stromal immune cells to secrete chemokines (e.g., CCL2, CCLS, CXCL10), recruiting monocytes, dendritic cells, and macrophages to the tumor site. HMGB1 and ATP also directly chemoattract APCs. An\n4. **Exposure of \"Eat-Me\" Signals:** \n Sonicated cells may expose phosphatidylserine on their surface, a marker recognized by phagocytic receptors (e.g., Tim-4, TAM receptors) on APCs, enhancing ongulfment of apoptotic bodies. Anins. **Reduction of Inmunosuppressive Factors:** \n Sonication inactivates live cancer cells, potentially diminishing secretion immunosuppressive cytokines (e.g., IL-10, TGF-B), thereby relieving APC inhibition and fostering a pro-inflomatorr milieu. An\n6. *"Micrparrtice//Ec" Exxosoo Formation: An ultrasonication may generate microvesicles which efficiently deliver activation signals to APCs via nembrane fusion endogenous adjuvant by releasing DAMPs and
presentation via I/II. This promotes
exosomes carrying tuaor antigens and DAMPs,
endocytosis. *umma= SRCCs act tumor antigens, recruiting APCs via chemokines, and activating them through PRR engagement. This primes APCs for antigen presentation and co-stimulatory molecule upregulation (e.g., CD80/86), bridging innate and adaptive antitumor immunity.
LLM Scores:
GPT-4o: Similarity score: Rationality score: Inclusiveness score:
DeepSeek-R1: Similarity score: Rationality score: Inclusiveness score: 9
Figure 21: The Cases of GPT-4o and DeepSeck- R1
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How do the polyclonal antibody responses elicited by RBD-NP in non- human prinates (MHPS) compare to those from HexaPro vaccination in terms of resilience to SARS-COV-2 RBD mutations, particularly at position 4B4?
Question:
Bath RBD-NP and HexaPro S elicited polyclonal antibodies with similar resilience to nost RBD mutations, but nutations at position 484 (e.g., E484K) reduced neutralization significantly (9-12-fold). This indicates shared reliance on antibodies targeting the RBM around residue 484, key epitope for both vaccines.
Ground Truth Answer:
Studies comparing these two immunagen fornats NHPs generally find that R8D-NP immunization drives focused yet diversified anti-RBD response that is relatively less affected by single RBD mutations (including at E484) compared to antibodies elicited by the full\uze1olength HexaPro s. Because the RBD is presented at high density on nanoparticles, the immune systen tuned toward multiple overlapping neutralizing epitopes the RBD.
OpenAI-o1 Answer:
Consequently, even though E484 is an important escape site, BDD-WPppllyelona sera tend to retain better neutralization of E484-mutant viruses than sera raised against the entire spike (HexaPro 5), whose overall response sites.
more impacted by point mutations critical RBD
LLM Scores:
GPT-4o: Similarity score: 5 Rationality score: 7 Inclusiveness score: 6
DeepSeek R1: Similarity score: Rationality score: 7 Inclusiveness score:
The comparison of polyclonal antibody responses elicited by RBD-NP (Receptor Binding Domain nanoparticle) and HexaPro S (stabilized full-length spike protein) vaccines non-human primates (NHPS) regarding resilience to SARS-COV-2 RBD nutations at position 484 (e.g., E484K) involves several key considerations: Unneen
DeepSeek-R1 Answer:
Antigen Presentation and Epitope Diversity:**\n- **R5D-NP**; \n Presents the RBD in repetitive, multivalent nanoparticle format, which enhances 8 cell receptor cross-linking and germinal center responses. This promotes **broad, polyclonal antibody repertoire** targeting multiple epitopes across the RBD, including regions distant from position 484. \n Focuses inmune responses **xxcluivvely on the RBO** potentially reducing inmunodominance of non-neutralizing epitopes (e.g., N-terminal donain) and favoring antibodies against conserved or structurally critical RBD regions. Ankn- **HexaPro 5**; \n Utilizes full-length spike protein stabilized in prefusion confornation (via six proline mutations), preserving confornational epitopes critical for ACE2 binding and neutralization. non-RBD regions** (e.g., subunit, NTD),
May induce ant antibodien against **both RBD and neutralizing antibodies are predominantly directed. The stabilization could enphasize epitopes near the exepptor-binding notif (RBM), including residue 484, which is a hotspot for immune evasion. \n\ntee **2, Inpact of Position 484 Mutations:**MM **484K Mutation**; Reduces binding of antibodies targeting the RBM, particularly those with heavy interactions at this site. Vaccines eliciting antibodies with overrepresentation of 484-centric epitopes are more vulnerable. An- *<R8D-NP**; Likely induces **nore diverse R8D-specific response**, including antibodies targeting epitopes outside the region (e.g., residues 417, 453, or conserved cryptic sites). This diversity may buffer against 484-driven insune escape, \n- **HexaPro 5**; Antibody responses may cluster around prefusion-stabilizeed epitopes. However, structural stabilization might also preserve critical neutralizing epitopes, offsetting sone mutational effects. An\nass **3, Experinental Evidence:** **R8D-NP Studies**; Nanoparticle RBD vaccines (e.g., ferritin-nBa RBD) in preclinical models show **broader variant cross-reactivityyt, including resilience to E484K, due to epitope diversity and high-avidity antibody naturation. An- **HeaPPr Studies**; EexaPro-stabilize spike vaccines (e.g., mRNA- 1273, NVX-CoV2373) denonstrate strong neutralization of ancestral strains but **reduced titers against E484K variants** (e.g., Beta, Gamma). However, adjuvants (e.g., Matrix-N) in protein subunit platforms may enhance cross-reactive responses. AnAnana **4. Adjuvant Influence: In- Adjuvants in RBD-NP (e.g., TLR agonists) or HexaPro S (e.g., saponin-based) can skew Th1/Th2 responses and alter antibody affinity/epitope breadth. Nanoparticle delivery often enhances Th1 and germinal center activity, favoring high-quality, durable antibodies.An An\nees "Conclusion: **\REED likely elicits **more resilient antibody responses to position 484 mutations** conpared to HexaPro 5, due to its focused yet diverse targeting of RBD epitopes and reduced dopendency on 484-centric noutralizing antibodies. HexaPro s, while potent against ancestral strains, may exhibit greater vulnerability to epitopes. However, adjuvant choice and stabilization strategies in HexaPro could mitigate this effect. Direct comparative studies in NHPs measuring neutralization against E484K variants (e-g., pseudovirus assays) are needed validate these hypotheses.
stabilized RBD confornations, potentially increasing reliance on 484-adjacent
to immunodoninance
RBM
Scores:
GPT-4o: Similarity score:
6 Rationality score: Inclusiveness score: 5
DeepSeek-RY R1: Similarity score: Rationality score: 8 Inclusiveness score: 7
Figure 22: The Cases of GPT-4o and DeepSeek-RR
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F.4 ERROR VISUALIZATION ANALYSIS
F.4.1
BIOGPT-LARGE-PBBEEDCA
The performance of BioGPT-LLare did not show meaningful improvement even after fine-tuning on PubMedQA. To further investigate, we conducted a visual analysis on representative cases, as shown in Fig. 23
Fig. 24
and Fig. 25
Compared with the base BioGPT-Large, the PubMedQA-tuned version adapts better to the QA format and avoids producing large amounts of garbled text. Nonetheless, its responses still exhibit fundamental errors. In many cases, the answers degenerate into mechanical repetitions training- set patterns rather than genuine synthesis or reasoning. This limitation is partly attributable to its pre-training objective-focused focused primarily on biomedical text completion-whiel which does not equip the model handle open-ended scientific QA.
Moreover, unlike instruction-tume tundd models that respond coherently to natural prompts, BioGPT requires deterministic cues such the prefix "The answer is: produce stable outputs. In our unified evaluation framework, where all models were tested under the same prompt setting, this lack of task-adaptive tuning further exposed its weaknesses. Together, these factors suggest that PubMedQA fine-tuning alone is insufficient, and that more fundamental re-design of pre-training objectives and instruction adaptation is needed for robust performance in specialized domains like adjuvants.
Question: How do sonicated reduced cancer cells (sRCCs) pronote the recruitment and activation of priigeenpreeenttng cells (APCS)?
Ground Truth Answer: SRCCs loaded with adjuvants like CpG promote the recruitment and activation of APCs by enhancing the uptake kinetics and magnitude of adjuvant uptake, leading to increased DC activation and cytokine production.
PooPT--Lrge-PubMedOA Answer: 
How do sonicated reduced cancer cells (sRCCs) promote the recruitment and activation of we investigated the effects of sRCCs on the maturation and function of bone marrow-derived dendritic cells (BMDCs) vitro. We found that SRCCs promoted the maturation indicated by the increased expression (MHC) class molecules, and the production of interleukin-1 Pactor-\u83b1 (TNF-\u03b1) SRCCs also enhanced the ability of BMDCs stimulate T-cell proliferation and interferon- Aue3b3 (IFN-\Ue3b3) production. SRCC-conditioned medium (sRCC- CM) promoted the maturation and function BMDCs a contact-independent manner.
cells (APCs)? To address this question,
BMDCs, as CD8 0, CD8 6, and major histocompatibility complex
(IL-1 2) and tumor necrosis
Furthermore, SRCC-CM activated the nuclear factor-\u@3baB (NF-\u83baB) signaling pathway in BNDCs. Blockage of the NF- NF-\ueBbaB pathway significantly reduced the expression of CD8 0, CD8 6, and MHC class II molecules and the production of IL-1 and TNF-\u03b1 by SRCC-stimulated BNDCs. Taken together, these results suggest that SRCCs promote the maturation and function of BMDCs through the activation of the NF-\uBBbaB signaling pathway. FREETEXT ABSTRACT \u2583

 <aaccppppppa <pad> <padd<<add>>>>>>>>eo <pa
<pad> <pad>
Figure 23: The Error Cases of BioGPT-Lange-uuMMcCQAA
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Question: what are the key factors affecting the immune response enhancement by fullereno1?
The self-assembly fullerenol into virus- like particles, activation of TLR pathways, and its ability enhance antigen uptake and protection are key factors in enhancing inmune respanses.
Ground Truth Answer:

Nhat are the key factors affecting the immune response enhancement by fullereno1? What is the relationship between the immune response enhancement and the physicochemical properties of fullerenol? To answer these questions, three types of C6 e (OH) (n = 2, 4, 6) were synthesized and their physicochemical properties were characterized by transmission electron microscopy (TEM), dynamic light scattering (DLS), zeta potential measurement, and Fourier transform infrared (FT-IR) spectroscopy. The effects three types
BioGTT-Large-PuMMed0A Answer:
a (OH) (n = 2, 4, 6) on the immune responses in mice were
investigated. The results showed that all three types of C6 (OH) n (n = 2, 4, 6) could significantly enhance the cellular and humoral inmune responses against ovalbumin (OVA) mice. Compared with pristine 8, 0 (OH) (OH) showed the highest activity among the three types of C6
C6 (OH) exhibited higher immune-
enhancing activity, and (OH) Moreover, the immune-enhancing activity of the increase of the number of hydroxyl groups the surface
(OH) (n 4, increased with (OH) These results of the key factors affecting the immune-enhancing activity of c6 (OH) and C6 (OH) 4 with four hydroxyl groups the surface the most effective adjuvant among the three types of c6 e (OH) / FREETEXT
suggested that the number of hydroxyl groups the surface of C6 (OH)
ABSTRACT \u2583 

Figure 24: The Error Cases of BloGPT-Large-PubeedAA
Question: How does the freeze-thaw cycle affect the tertiary structure of the recombinant hepatitis B surface antigen (rHBsAg) when adsorbed to Alhydrogel
The freeze-thaw cycle induces blue shift the rHBsAg tertiary structure when adsorbed Alhydroge19, suggesting structural perturbation which may affect antigen recognition by the immune system.
Ground Truth Answer:

How does the freeze-thaw cycle affect the tertiary structure of the recombinant hepatitis surface antigen (rHBsAg) when adsorbed to Alhydrogel \u@0ae? The aim of this study was to evaluate the effect the freeze-thaw cycle on the tertiary structure of the rHBsAg adsorbed to Alhydrogel \ue8ae using circular dichroism (CD) and Fourier transform infrared (FTIR) spectroscopies. the rHBsAg adsorbed to Alhydrogel \ueeae were affected by freeze-thaw cycle. However, the thermal stability of the adsorbed rHBsAg was reduced after the freeze-thaw cycle. The adsorption of the rHBsAg Alhydrogel \ue9ae increased the stability tertiary structures of the rHBsAg during the freeze-thaw cycle. The adsorbed rHBsAg was more stable than the non-adsorbed rHBsAg during the freeze-thaw cycle. These results suggest that the adsorption of the rHBsAg to Alhydrogel \ue9ae increases the stability of the rHBsAg during the freeze-thaw cycle. FREETEXT > << ABSTRACT > \u2583 
<pad>
SOGGT-Large-PubMeedee
secondary and tertiary structures
the secondary and
Figure 25: The Error Cases of BioGPT.Large-PuMMe@AA
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F.4.2 DARWIN
We also conducted visual analysis of Darwin on the same three examples (Fig. 26 Fig. 27 and Fig. (8). Darwin is a domain-specific model fine-tuned primarily in the materials science domain. subclass of biomaterials, there is partial overlap between Darwin's 's training distribution and adjuvant-related knowledge.
Since adjuvants can be regarded
This overlap helps explain certain strengths: because Darwin's first-stage training involved open- ended Q&A data, its outputs follow the structure of adjuvant Q&A tasks more naturally, and its specialized fine-tuning improves lexical precision in technical terminology. However, the overlap limited, the core challenges of adjuvant research lie in immunological mechanisms and vaccine- specific contexts-area, that fall outside Darwin's ' primary training focus. Consequently, Darwin often produces superficially well-formed answers that nevertheless fail to capture domain-specific reasoning.
This case underscores broader point: while adjuvants share material properties with general biomaterials, the immunological dimension creates a significant domain gap. Bridging this gap requires resources and models purpose-built for immunology, rather than indirect transfer from adjacent scientific fields.
How do sonicated reduced cancer cells (sRCCs) promote the recruitment and activation of antigen-presenting cells (APCs)?
Question:
Ground Truth Answer: SRCCs loaded with adjuvants like CpG promote the recruitment and activation of APCS by enhancing the uptake kinetics and magnitude of adjuvant uptake, leading to increased DC activation and cytokine production.
Darwin Answer: SRCCs promote the recruitment and activation of APCs by providing a platform for the presentation of antigens and the activation of immune cells. \n

Figure 26: The Error Cases of Darwin
Question: What are the key factors affecting the immune response enhancement by fullerenol?
The self-assembly fullerenol into sirus-like particles, its activation of TLR pathways, and its ability enhance antigen uptake and protection key factors in enhancing immune respanses.
Ground Truth Answer:
The key factors affecting the immune response enhancement by fullerenol include its ability to act as an antioxidant, its ability to enhance the activity immune cells, its ability improve the immune response to pathogens. \n

Darwin Answer:
Figure 27: The Error Cases of Darwin
How does the freeze-thaw cycle affect tertiary structure of the recombinant hepatitis surface antigen (rHBsAg) when adsorbed Alhydrogel*?
Question:
The freeze-thaw cycle induces blue shift in the rHBsAg tertiary structure when adsorbed to Alhydrogel*, suggesting structural perturbation which may affect antigen recognition by the immune system.
Ground Truth Answer:
The freeze-thaw cycle has no effect on the tertiary structure of the rHBsAg when adsorbed to Alhydragel9.
Darwin Answer:
###l Instruction:\n Answer the question based on your knowledge. #*и Input:\n Can the tertiary structure the rHBsAg be affected by the freeze-thaw cycle when adsorbed to Alhydrogel@P\n ### Instruction: \n No, the tertiary structure of the rHBsAg cannot affected by the freeze-thaw cycle when adsorbed to Alhydrogel \n

Figure 28: The Error Cases of Darwin
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G THE PROMPT OF GENERATION AND INFERENCE
G.1 THE PROMPT OF GENERATION
The Prompt of Generation Basic Knowledge Open-ended Q&A
You are an expert immunology and adjuvant design, with great achievements in immunol- ogy and adjuvant design. Next, will provide you with the partial chapter context, including the text and images an adjuvant design or immunology book. Please ask at least 20 questions from the perspective of an expert and provide answers about these chapters, These questions cannot be the same as the exercises in the provided text and must in the form of questions and answers. If specific questions and answers cannot be generated based on the provided content, insights or questions related to specific chapters or topics in immunology and adjuvant design be provided. The first 10 questions you raised should be aimed at gaining a more systematic understanding order provide better and more comprehensive answers the questions. The remaining questions should have sufficient depth and difficulty. Please attach the reasoning process and answer for each question. All answers and reasoning processes should be detailed as points in the answer, which means all answers
the relevant knowledge of adjuvant design, in
possible and it is prohibited to elaborate must be in one paragraph. should be noted that the description of the reasoning process does not indicate which part of the chapter it appears in, but is based on its explanation. For the images provided to you in the context, you also need to ask at least professional questions. For images in PDF files, relevant professional questions need to be raised. Please follow the question format:
relevant
Question: XXXXX
Reasoning Process: XXXXXX
Answer: XXXXXX
If the problem is based on an image, please provide the image number and the path in the Question/Answer/Reesonni Process. Please follow the question format:
Question: XXxXx, Fig.x (XXхз/XXX//XXX²)
Reasoning Process: XXXXXX
Answer: XXXXXX
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Published as a conference paper at ICLR 2026
The Prompt of Generation Biology Principles Open-ended Q&A
You are an expert in immunology and adjuvant design, and have achieved great success in immunology and adjuvant design. Next, will provide you with PDF files of the relevant papers. Please provide at least 15 ~ 20 questions from an expert's perspective and provide answers. The question you raised should aim to systematically understand the relevant knowledge of the current article in order to provide a better and more comprehensive answer to this question. These issues may include but not limited to injection procedures, immune kinetics, and release curves. If there is no text provided in readable format, you can create questions and answers based the shared text in the PDF. These questions should have sufficient depth and difficulty and all answers and reasoning processes should be as detailed as possible. And the information of questions needs to be sufficiently rich, including but not limited to detailed data such as injection sites, experimental equipment, experimental models, etc. And the corresponding reasoning needs be provided. should be noted that the description of the reasoning process does not indicate which part of the chapter it appears in, but based on its explanation. All answers and reasoning processes should as detailed as possible and it is prohibited to elaborate on points in the answer, which means all answers must be in one paragraph. For the images provided to you the context, you also need to ask at least 2 ~ 5 relevant professional questions. Please follow the format:
Question: XXXXX
Reasoning Process: XXXXXX
Answer: XXXXXX
If the problem is based on an image, please provide the image number and the path in the Question/Answer/Reenn Answer/Reasoning Process. Please follow the question format:
Question: XXXXX, Fig.x (XXхз/XXXX/XXXX)
Reasoning Process: XXXXXX
Answer: XXXXXX
The Prompt of Generation Adjuvant Design&Safety Open-ended Q&A
You are an expert immunology and adjuvant design, and have achieved great success in immunology and adjuvant design. Next, I will provide you with files of the relevant papers. Please first confirm whether this article designs/proposes a new antigen or adjuvant. If so, please specify the name of the adjuvant and comprehensively analyze its main immune effects and design//mprovemmnt ideas, (The questions like "Does the current article propose a new adjuvant?" or "What new adjuvant is designed but instead use the name of the adjuvant in the current paper, such as "What is the immune function of xxxx", "How does xxXX promote the recruitment and activation of antigen- presenting cells", etc.), including promoting the recruitment, activation of antigen-presenting cells, enhancing T cell responses and persistent antibodies, etc Then please identify the key factors that affect these effects and provide a detailed explanation of the reasons, which should include design/improveemet ideas, small molecule drugs, antigen release behavior, etc. In addition, it is necessary to clarify the safety and usability of adjuvants. Please propose a corresponding question for each the above explanations and provide the corresponding reasons the answer to the question, along with detailed reasoning process. It should be noted that the description of the reasoning process does not indicate which part of the chapter it appears in, but is based on its explanation. The questions you raise should always be less than 8 and all answers and answer/reasoning processes should be as detailed as possible. Please follow the format: Question: XXXXX Reasoning Process: XXXXXX
proposed in this article?" are prohibited.
Answer: XXXXXX
If the problem is based on an image, please provide the image number and the path in the Dnestion/Answer/Ressnnng Process. Please follow the question format:
Question: ххххx, Fig.x (xXXX/XXXx/XXXx)
Reasoning Process: XXXXXX
Answer: XXXXXX
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Published as a conference paper at ICLR 2026
G.2
THE PROMPT OF INFERENCE
The Prompt of Inference
You are expert in immunology and adjuvants, with strong background in vaccine develop- ment. Your research and practice in this field have equipped you with a deep understanding of the mechanisms of immune response and how optimize vaccine efficacy through adjuvants. You excel in providing concise, precise, and professional responses to questions related to adjuvants and immunology. Please answer the following questions.
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Figure 29: The World Cloud of the Adjuvant Benchmark
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Published as a conference paper at ICLR 2026
1 USAGE OF LARGE LANGUAGE MODELS
I.1 WRITING
In this study, we primarily used LLMs to assist with grammar checking and sentence structure adjustments throughout the writing process. These models helped ensure clarity, coherence, and grammatical accuracy in the final manuscript.
1.2 EXPERIMENT
For the experiments in this study, focus testing the knowledge comprehension abilities of MLLMs and LLMs in the adjuvant domain. Given the nature of the tasks, we naturally leveraged large models to evaluate their performance and understanding of domain-specifie knowledge.
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Reviews:
[{"review_id": "61RfCOAOPI", "reviewer": "Reviewer_Z32D", "summary": "This paper introduces a domain-specific benchmark and formalized knowledge resource for evaluating LLMs and MLLMs in vaccine adjuvant research. The benchmark includes 1,294 open-ended QA pairs, 69 hallucination-focused samples, and 1,364 formalized entries describing design principles and immunological mechanisms. The authors evaluate 29 models across STS/BERTScore, LLM-as-a-Judge scoring, and hallucination rejection, finding that models such as OpenAI-o1 and DeepSeek-R1 perform best. The formalized representations aim to support future neural–symbolic reasoning and domain-specialized systems.", "questions": "- Why does the paper refer to all 29 evaluated models as “MLLMs” when many are text-only? Will the terminology be corrected to clearly distinguish LLM vs. MLLM?\n- Since all images were converted to text via OCR, is the benchmark effectively evaluating text-based reasoning rather than multimodal reasoning? Can the authors provide a comparison between OCR-only input and native visual input for top multimodal models?\n- Could the authors report key inference settings (e.g., temperature, top-p, random seed)? The absence of these prevents exact reproduction of the reported results.\n- Is there any empirical evidence that the 1,364 formalized entries improve retrieval, consistency, or hallucination reduction? Even a small-scale downstream experiment would substantiate this contribution.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- The work introduces the first benchmark specifically targeting the adjuvant domain, covering factual QA, hallucination rejection, and mechanistic reasoning. Existing biomedical benchmarks provide limited coverage of this problem space.\n\n- Data collection and cleaning follow a clear process involving literature extraction, expert annotation, and filtering. The evaluation spans a broad range of closed- and open-source models with consistent prompt settings.\n\n- Metrics, data distributions, annotation pipeline, and prompts are clearly documented, and the appendix provides formulas and templates that increase reproducibility.\n\n- The benchmark offers the community a structured foundation for evaluating scientific reasoning and reliability in immunology-related tasks. The results provide insights into how “reasoning” models and domain-informed knowledge can help mitigate hallucination.", "weaknesses": "- Potentially misleading use of “multimodal.”\nThe title and main text repeatedly claim evaluation of 29 “MLLMs,” yet some are purely text-based LLMs. If all models were truly multimodal, OCR would not be required. The paper should distinguish LLM vs. MLLM more precisely, adjust wording accordingly, and expand evaluation to include domain-relevant multimodal biomedical models (e.g., LLaVA-Med).\n- Multimodal evaluation design masks visual differences.\nAll images are transformed into text via OCR, eliminating spatial and structural cues essential for true visio-linguistic reasoning. This turns the task largely into text-only QA and may suppress advantages of genuinely multimodal models. OCR noise also introduces confounding error. The paper should clearly acknowledge this limitation and ideally present an “original image vs. OCR-only” comparison or dual leaderboard.\n- Lack of critical experimental details hurts reproducibility.\nKey inference settings (temperature, top-p, random seeds) are not reported. Important dataset details—such as the list of source papers, annotation protocol, annotator backgrounds, and inter-annotator agreement—are also missing. Some model performances in Table 5 are extremely close, yet no error bars or significance tests are provided.\n- Risk of self-enhancement bias in LLM-as-a-Judge.\nDeepSeek-R1 (and potentially related models) played roles in both data generation and evaluation, raising the possibility of stylistic bias. Although the appendix provides human–LLM correlation analysis, this limitation should be explicitly acknowledged in the main text, and future versions should consider using a disjoint panel of judges.\n- Formalized entries lack empirical validation.\nThe paper introduces 1,364 formalized entries as a resource for neural–symbolic reasoning, but no experiments demonstrate that they improve retrieval, consistency, or hallucination reduction. Even a small controlled study would substantially strengthen this contribution.\n- Data release and licensing are not clearly described.\nIt is unclear whether images, textbook content, or literature excerpts can be redistributed legally. Without explicit licensing clarification, the dataset’s usability and adoption may be limited.", "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": "r3SrSfrYFb", "reviewer": "Reviewer_2Z13", "summary": "Introduces the first adjuvant-focused benchmark: 1,294 expert-vetted open-ended Q&A pairs, 69 hallucination cases, and 1,364 “formal descriptions” of mechanisms/design.\n\nEvaluates 29 MLLMs (11 closed, 18 open) on domain QA, hallucination rejection, instruction following; reports STS/BERTScore + an LLM-as-judge rubric (Similarity, Rationality, Inclusiveness).\n\nBest reported: OpenAI-o1 (closed) and DeepSeek-R1/V3, Qwen3-x (open). Overall hallucination rejection remains low.\n\nProposes a formal abstraction framework (variables/functions for immune processes) as scaffolding for future neuro-symbolic models.\n\nClaims novelty, releases benchmark + code (anonymous), and describes expert annotation pipeline.", "questions": "IAA & QC: What are inter-annotator agreement stats (Cohen’s κ/Krippendorff’s α) for validity and hallucination labels? How often did adjudication change labels?\n\nLength & bias controls: Did you apply length-controlled judging (e.g., LC-AlpacaEval-style) or verbosity penalties to mitigate LLM-judge bias?\n\nJudge neutrality: Can you re-run main tables with held-out judges (e.g., Claude/Gemini + a smaller open model) and report rank correlations vs human scores?\n\nAblations: Remove items originating from GPT-4o / DeepSeek-R1 and re-evaluate those same models; how do rankings shift?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Clear problem framing & real need: adjuvants are underserved by existing bio/chem benchmarks.\n\nOpen-ended evaluation better matches scientific reasoning than MCQ; includes a multimodal slice.\n\nScale + expert pass: sizable curated set with domain-expert filtering; explicit hallucination subset.\n\nMethodical reporting: model families, “think” vs “inference” comparison, and some robustness checks (e.g., HRR with mean±SD).", "weaknesses": "Evaluator circularity / bias: GPT-4o & DeepSeek-R1 both generated data and act as judges; risk of self-preference and contamination despite multi-model generation.\n\nSmall hallucination set (n=69): limits power; HRR estimates are noisy and may not generalize.\n\nMetric fragility: STS/BERTScore conflate surface similarity with correctness; LLM-as-judge can reward verbosity/formatting; limited length controls and no strong human-only adjudication for the main tables.\n\nIncomplete reliability stats: no inter-annotator agreement numbers (κ/α), limited statistical tests (no CIs/bootstraps for main scores), and few ablations.", "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": "JalJbNm7Me", "reviewer": "Reviewer_2T68", "summary": "The paper introduces a benchmark for adjuvants, curated by LLMs and montiored by domain experts, which includes an open-ended Q&A, hallucination data for rejection tests, and adjuvant formal data—template-driven formal descriptions produced with expert-designed variables. The benchmark is then used to evaluate frontier open and closed source models via automatic and LLM as a judge based metrics.", "questions": "1. How exactly is the formal description used or intended to be used now? from the title/abstract it sounds central, but did not understand from the text where it is utilized other.\n2. Is it possible to include an ablation study on the possible bias of models on their own curated samples?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The dataset curation pipeline is clear and involves human experts in the loop for validation. There is a clear attempt to reduce generator–evaluator bias by using multiple MLLMs.\n2. Samples discarded by experts are used for the hallucination subset.\n3. Evaluation consists of a large set of automatic and LLM as a judge based metrics.", "weaknesses": "1. Since some of the LLMs (e.g. GPT-4o, DeepSeek) are used in the data curation pipeline and are among top performers, studying possible advantage of these models on their curated samples is missing.\n2. It is not clear from the text if the formal description of adjuvants is used in the data curation process or in evaluation, despite being framed as a main contribution.", "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": "GHBBeNRmHi", "reviewer": "Reviewer_zNQ7", "summary": "This paper introduces the first benchmark specifically designed for evaluating Multimodal Large Language Models (MLLMs) in the domain of vaccine adjuvants. Recognizing a critical gap in AI resources for this field—characterized by data scarcity and complex, heterogeneous mechanisms—the authors construct a high-quality dataset comprising 1,294 expert-annotated open-ended Question-Answer (Q&A) pairs and 1,364 formal, structured descriptions of adjuvant design and immune mechanisms. Using this benchmark, they perform a systematic evaluation of 29 MLLMs (11 closed-source, 18 open-source), assessing their capabilities in domain-specific Q&A, hallucination rejection, and instruction following. The results identify top-performing models like OpenAI-o1 and DeepSeek-R1. A key secondary contribution is the proposal of a formal framework to abstract complex biological processes into structured variables and functions, intended to serve as a foundation for future domain-specialized models.", "questions": "1. Beyond using multiple MLLMs for data generation, what specific steps were taken in the prompt design and expert annotation phase to ensure that the benchmark does not overfit to the stylistic or reasoning patterns of the specific generator models (like GPT-4o and DeepSeek-R1)?\n2. The results show that even top-performing models struggle with hallucination rejection (HRR < 27%). Did your error analysis reveal any common patterns in the types of hallucinations that models fail to reject? For instance, are they more likely to miss factual errors or logical inconsistencies?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The creation of the first dedicated benchmark for adjuvants is a good contribution that can catalyze AI-driven research in vaccinology and immunology.\n2. The evaluation is exceptionally thorough. It goes beyond simple Q&A to include critical aspects like hallucination rejection and instruction following. The comparison of 29 models across closed-source and open-source categories provides a valuable landscape analysis for the community.", "weaknesses": "1. The hallucination dataset, with only 69 examples, is relatively small. While its curated nature is valuable, the small size may limit the statistical power and generalizability of the HRR metric. The standard deviations reported in Table 6 highlight this variability.\n2. The paper provides relatively few statistical analyses (e.g., confidence intervals, significance testing) to support claims about model ranking differences.\n3. The benchmark's Q&A data is generated by a subset of the models being evaluated (e.g., GPT-4o, DeepSeek-R1). This could introduce a bias, as a model might perform better on questions that are stylistically or structurally similar to its own generation pattern. The authors acknowledge this and use multiple models to mitigate it, but it remains a minor methodological concern.", "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)