@inproceedings{singh-etal-2026-grammar,
title = "Grammar Search for Multi-Agent Systems",
author = "Singh, Mayank and
Yadav, Vikas and
Malay, Shiva Krishna Reddy and
Nayak, Shravan and
Rajeswar, Sai and
Madhusudhan, Sathwik Tejaswi and
Blanco, Eduardo",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.75/",
pages = "1640--1655",
ISBN = "979-8-89176-390-6",
abstract = "Automatic search for Multi-Agent Systems has recently emerged as a key focus in agentic AI research. Several prior approaches have relied on LLM-based free-form search over the code space. In this work, we propose a more structured framework that explores the same space through a fixed set of composable components. We show that, despite lacking the generative flexibility of LLMs during the candidate generation stage, our method outperforms prior approaches on a majority of evaluated benchmarks across two backbone LLMs and two domains: mathematics and question answering. Furthermore, our method offers additional advantages, including a more cost-efficient search process and the generation of modular, interpretable multi-agent systems."
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%0 Conference Proceedings
%T Grammar Search for Multi-Agent Systems
%A Singh, Mayank
%A Yadav, Vikas
%A Malay, Shiva Krishna Reddy
%A Nayak, Shravan
%A Rajeswar, Sai
%A Madhusudhan, Sathwik Tejaswi
%A Blanco, Eduardo
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F singh-etal-2026-grammar
%X Automatic search for Multi-Agent Systems has recently emerged as a key focus in agentic AI research. Several prior approaches have relied on LLM-based free-form search over the code space. In this work, we propose a more structured framework that explores the same space through a fixed set of composable components. We show that, despite lacking the generative flexibility of LLMs during the candidate generation stage, our method outperforms prior approaches on a majority of evaluated benchmarks across two backbone LLMs and two domains: mathematics and question answering. Furthermore, our method offers additional advantages, including a more cost-efficient search process and the generation of modular, interpretable multi-agent systems.
%U https://aclanthology.org/2026.acl-long.75/
%P 1640-1655
Markdown (Informal)
[Grammar Search for Multi-Agent Systems](https://aclanthology.org/2026.acl-long.75/) (Singh et al., ACL 2026)
ACL
- Mayank Singh, Vikas Yadav, Shiva Krishna Reddy Malay, Shravan Nayak, Sai Rajeswar, Sathwik Tejaswi Madhusudhan, and Eduardo Blanco. 2026. Grammar Search for Multi-Agent Systems. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1640–1655, San Diego, California, United States. Association for Computational Linguistics.