@inproceedings{chen-etal-2026-savant,
title = "{SAVANT}: A Neuro-Symbolic Verification Framework for Adjuvant Design",
author = "Chen, Yi and
Zhang, Yu and
Xu, Jian and
Yue, Hua and
Wang, Xinming and
Lyu, Zequan and
Wang, Boran and
Liu, Hongyi and
Wang, Yan and
Cao, Peiyuan and
Zhang, Xu-Yao and
Wei, Wei and
Liu, Cheng-Lin",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.369/",
pages = "7728--7774",
ISBN = "979-8-89176-462-0",
abstract = "Large language models (LLMs) can propose vaccine adjuvant designs with plausible mechanistic rationales, but such outputs often lack grounding in established immunological knowledge. We present \textbf{SAVANT}, a neuro-symbolic framework for LLM-based adjuvant design that combines two-stage constrained generation with literature-grounded verification. SAVANT first generates a natural-language design rationale, then renders it into an ontology-constrained domain-specific language (DSL) using logic masks and finite-state control. To reason under fragmented evidence and incomplete first-principles rules, SAVANT verifies generated designs against literature-derived symbolic evidence through minimal sub-theorem proof. In expert-aligned evaluation, \textsc{SAVANT} shows greater stability in stage-level rank alignment than the strongest LLM-judge baselines, while achieving the strongest alignment for immune-effect support and mechanistic reasoning. SAVANT provides graded, interpretable assessments that distinguish supported mechanisms, under-supported hypotheses, and knowledge gaps for hypothesis-driven experimentation. Data and code will be released at https://github.com/banjiuyufen/SAVANT."
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<abstract>Large language models (LLMs) can propose vaccine adjuvant designs with plausible mechanistic rationales, but such outputs often lack grounding in established immunological knowledge. We present SAVANT, a neuro-symbolic framework for LLM-based adjuvant design that combines two-stage constrained generation with literature-grounded verification. SAVANT first generates a natural-language design rationale, then renders it into an ontology-constrained domain-specific language (DSL) using logic masks and finite-state control. To reason under fragmented evidence and incomplete first-principles rules, SAVANT verifies generated designs against literature-derived symbolic evidence through minimal sub-theorem proof. In expert-aligned evaluation, SAVANT shows greater stability in stage-level rank alignment than the strongest LLM-judge baselines, while achieving the strongest alignment for immune-effect support and mechanistic reasoning. SAVANT provides graded, interpretable assessments that distinguish supported mechanisms, under-supported hypotheses, and knowledge gaps for hypothesis-driven experimentation. Data and code will be released at https://github.com/banjiuyufen/SAVANT.</abstract>
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%0 Conference Proceedings
%T SAVANT: A Neuro-Symbolic Verification Framework for Adjuvant Design
%A Chen, Yi
%A Zhang, Yu
%A Xu, Jian
%A Yue, Hua
%A Wang, Xinming
%A Lyu, Zequan
%A Wang, Boran
%A Liu, Hongyi
%A Wang, Yan
%A Cao, Peiyuan
%A Zhang, Xu-Yao
%A Wei, Wei
%A Liu, Cheng-Lin
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F chen-etal-2026-savant
%X Large language models (LLMs) can propose vaccine adjuvant designs with plausible mechanistic rationales, but such outputs often lack grounding in established immunological knowledge. We present SAVANT, a neuro-symbolic framework for LLM-based adjuvant design that combines two-stage constrained generation with literature-grounded verification. SAVANT first generates a natural-language design rationale, then renders it into an ontology-constrained domain-specific language (DSL) using logic masks and finite-state control. To reason under fragmented evidence and incomplete first-principles rules, SAVANT verifies generated designs against literature-derived symbolic evidence through minimal sub-theorem proof. In expert-aligned evaluation, SAVANT shows greater stability in stage-level rank alignment than the strongest LLM-judge baselines, while achieving the strongest alignment for immune-effect support and mechanistic reasoning. SAVANT provides graded, interpretable assessments that distinguish supported mechanisms, under-supported hypotheses, and knowledge gaps for hypothesis-driven experimentation. Data and code will be released at https://github.com/banjiuyufen/SAVANT.
%U https://aclanthology.org/2026.findings-emnlp.369/
%P 7728-7774
Markdown (Informal)
[SAVANT: A Neuro-Symbolic Verification Framework for Adjuvant Design](https://aclanthology.org/2026.findings-emnlp.369/) (Chen et al., Findings 2026)
ACL
- Yi Chen, Yu Zhang, Jian Xu, Hua Yue, Xinming Wang, Zequan Lyu, Boran Wang, Hongyi Liu, Yan Wang, Peiyuan Cao, Xu-Yao Zhang, Wei Wei, and Cheng-Lin Liu. 2026. SAVANT: A Neuro-Symbolic Verification Framework for Adjuvant Design. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 7728–7774, Budapest, Hungary. Association for Computational Linguistics.