@inproceedings{li-etal-2026-chemamp,
title = "{C}hem{A}mp: Amplified Chemistry Tools via Composable Agents",
author = "Li, Zhucong and
Chang, Powei and
Xiao, Jin and
Zhou, Zhijian and
He, Qianyu and
Liang, Jiaqing and
Cao, Fenglei and
Yinghui, Xu and
Qi, Yuan",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.52/",
pages = "1038--1053",
ISBN = "979-8-89176-395-1",
abstract = "Although LLM-based agents are proven to master tool orchestration in scientific fields, particularly chemistry, their single-task performance remains limited by underlying tool constraints. To this end, we propose tool amplification, a novel paradigm that enhances the collective capabilities of specialized tools through optimized, dynamic coordination within individual tasks. Instantiating this paradigm, we introduce ChemAmp, a computationally lightweight framework that dynamically treats chemistry tools (e.g., UniMol2, Chemformer) as composable building-block agents. It constructs task-specialized super-agents that transcend atomic tool constraints with limited data ({\ensuremath{\leq}}10 samples). Our evaluations across four core chemistry tasks molecular design, molecule captioning, reaction prediction, and property prediction demonstrate that ChemAmp outperforms chemistry-specialized models, generalist LLMs, and agent systems with tool orchestration. Critically, this bottom-up construction strategy enables 94{\%} inference token cost reductions versus vanilla multi-agent systems."
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<abstract>Although LLM-based agents are proven to master tool orchestration in scientific fields, particularly chemistry, their single-task performance remains limited by underlying tool constraints. To this end, we propose tool amplification, a novel paradigm that enhances the collective capabilities of specialized tools through optimized, dynamic coordination within individual tasks. Instantiating this paradigm, we introduce ChemAmp, a computationally lightweight framework that dynamically treats chemistry tools (e.g., UniMol2, Chemformer) as composable building-block agents. It constructs task-specialized super-agents that transcend atomic tool constraints with limited data (\ensuremathłeq10 samples). Our evaluations across four core chemistry tasks molecular design, molecule captioning, reaction prediction, and property prediction demonstrate that ChemAmp outperforms chemistry-specialized models, generalist LLMs, and agent systems with tool orchestration. Critically, this bottom-up construction strategy enables 94% inference token cost reductions versus vanilla multi-agent systems.</abstract>
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%0 Conference Proceedings
%T ChemAmp: Amplified Chemistry Tools via Composable Agents
%A Li, Zhucong
%A Chang, Powei
%A Xiao, Jin
%A Zhou, Zhijian
%A He, Qianyu
%A Liang, Jiaqing
%A Cao, Fenglei
%A Yinghui, Xu
%A Qi, Yuan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F li-etal-2026-chemamp
%X Although LLM-based agents are proven to master tool orchestration in scientific fields, particularly chemistry, their single-task performance remains limited by underlying tool constraints. To this end, we propose tool amplification, a novel paradigm that enhances the collective capabilities of specialized tools through optimized, dynamic coordination within individual tasks. Instantiating this paradigm, we introduce ChemAmp, a computationally lightweight framework that dynamically treats chemistry tools (e.g., UniMol2, Chemformer) as composable building-block agents. It constructs task-specialized super-agents that transcend atomic tool constraints with limited data (\ensuremathłeq10 samples). Our evaluations across four core chemistry tasks molecular design, molecule captioning, reaction prediction, and property prediction demonstrate that ChemAmp outperforms chemistry-specialized models, generalist LLMs, and agent systems with tool orchestration. Critically, this bottom-up construction strategy enables 94% inference token cost reductions versus vanilla multi-agent systems.
%U https://aclanthology.org/2026.findings-acl.52/
%P 1038-1053
Markdown (Informal)
[ChemAmp: Amplified Chemistry Tools via Composable Agents](https://aclanthology.org/2026.findings-acl.52/) (Li et al., Findings 2026)
ACL
- Zhucong Li, Powei Chang, Jin Xiao, Zhijian Zhou, Qianyu He, Jiaqing Liang, Fenglei Cao, Xu Yinghui, and Yuan Qi. 2026. ChemAmp: Amplified Chemistry Tools via Composable Agents. In Findings of the Association for Computational Linguistics: ACL 2026, pages 1038–1053, San Diego, California, United States. Association for Computational Linguistics.