@inproceedings{wang-etal-2025-scalability,
title = "Scalability of {LLM}-Based Multi-Agent Systems for Scientific Code Generation: A Preliminary Study",
author = "Wang, Yuru and
Zhang, Kaiyan and
Tian, Kai and
Zeng, Sihang and
Lv, Xingtai and
Ding, Ning and
Qi, Biqing and
Zhou, Bowen",
editor = "Valentino, Marco and
Ferreira, Deborah and
Thayaparan, Mokanarangan and
Ranaldi, Leonardo and
Freitas, Andre",
booktitle = "Proceedings of The 3rd Workshop on Mathematical Natural Language Processing (MathNLP 2025)",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.mathnlp-main.4/",
pages = "50--61",
ISBN = "979-8-89176-348-7",
abstract = "Recent studies indicate that LLM-based Multi-Agent Systems (MAS) encounter scalability challenges in complex mathematical problem-solving or coding tasks, exhibiting issues such as inconsistent role adherence and ineffective inter-agent communication. Moreover, the performance advantages of LLM-based MAS over a single agent employing test-time scaling methods (e.g., majority voting) remain marginal. This raises a critical question: Can LLM-based MAS scale effectively to achieve performance comparable to standalone LLMs or even Large Reasoning Models (LRMs) under optimal test-time compute?In this paper, we conduct a preliminary investigation into the scalability of LLM-based MAS for scientific code generation. We propose a simple yet scalable two-player framework based on iterative critic-in-the-loop refinement. Our experiments demonstrate that a minimalist actor-critic framework based on DeepSeek-V3 can outperform DeepSeek-R1 under equivalent computational budgets. Surprisingly, more complex frameworks fail to yield significant gains. These findings corroborate recent insights into multi-agent system limitations and highlight the importance of scalable workflows for advancing scientific code generation."
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<abstract>Recent studies indicate that LLM-based Multi-Agent Systems (MAS) encounter scalability challenges in complex mathematical problem-solving or coding tasks, exhibiting issues such as inconsistent role adherence and ineffective inter-agent communication. Moreover, the performance advantages of LLM-based MAS over a single agent employing test-time scaling methods (e.g., majority voting) remain marginal. This raises a critical question: Can LLM-based MAS scale effectively to achieve performance comparable to standalone LLMs or even Large Reasoning Models (LRMs) under optimal test-time compute?In this paper, we conduct a preliminary investigation into the scalability of LLM-based MAS for scientific code generation. We propose a simple yet scalable two-player framework based on iterative critic-in-the-loop refinement. Our experiments demonstrate that a minimalist actor-critic framework based on DeepSeek-V3 can outperform DeepSeek-R1 under equivalent computational budgets. Surprisingly, more complex frameworks fail to yield significant gains. These findings corroborate recent insights into multi-agent system limitations and highlight the importance of scalable workflows for advancing scientific code generation.</abstract>
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%0 Conference Proceedings
%T Scalability of LLM-Based Multi-Agent Systems for Scientific Code Generation: A Preliminary Study
%A Wang, Yuru
%A Zhang, Kaiyan
%A Tian, Kai
%A Zeng, Sihang
%A Lv, Xingtai
%A Ding, Ning
%A Qi, Biqing
%A Zhou, Bowen
%Y Valentino, Marco
%Y Ferreira, Deborah
%Y Thayaparan, Mokanarangan
%Y Ranaldi, Leonardo
%Y Freitas, Andre
%S Proceedings of The 3rd Workshop on Mathematical Natural Language Processing (MathNLP 2025)
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-348-7
%F wang-etal-2025-scalability
%X Recent studies indicate that LLM-based Multi-Agent Systems (MAS) encounter scalability challenges in complex mathematical problem-solving or coding tasks, exhibiting issues such as inconsistent role adherence and ineffective inter-agent communication. Moreover, the performance advantages of LLM-based MAS over a single agent employing test-time scaling methods (e.g., majority voting) remain marginal. This raises a critical question: Can LLM-based MAS scale effectively to achieve performance comparable to standalone LLMs or even Large Reasoning Models (LRMs) under optimal test-time compute?In this paper, we conduct a preliminary investigation into the scalability of LLM-based MAS for scientific code generation. We propose a simple yet scalable two-player framework based on iterative critic-in-the-loop refinement. Our experiments demonstrate that a minimalist actor-critic framework based on DeepSeek-V3 can outperform DeepSeek-R1 under equivalent computational budgets. Surprisingly, more complex frameworks fail to yield significant gains. These findings corroborate recent insights into multi-agent system limitations and highlight the importance of scalable workflows for advancing scientific code generation.
%U https://aclanthology.org/2025.mathnlp-main.4/
%P 50-61
Markdown (Informal)
[Scalability of LLM-Based Multi-Agent Systems for Scientific Code Generation: A Preliminary Study](https://aclanthology.org/2025.mathnlp-main.4/) (Wang et al., MathNLP 2025)
ACL
- Yuru Wang, Kaiyan Zhang, Kai Tian, Sihang Zeng, Xingtai Lv, Ning Ding, Biqing Qi, and Bowen Zhou. 2025. Scalability of LLM-Based Multi-Agent Systems for Scientific Code Generation: A Preliminary Study. In Proceedings of The 3rd Workshop on Mathematical Natural Language Processing (MathNLP 2025), pages 50–61, Suzhou, China. Association for Computational Linguistics.