An Open-Ended Benchmark and Formal Framework for Adjuvant Research with MLLM

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.