@inproceedings{yang-etal-2026-20,
title = "When 20 Agents Fail to Sort: The Distributed Sorting Benchmark for Scalable Multi-Agent Systems",
author = "Yang, Xin and
Wang, Junhao and
Tang, Bintao and
Cheng, Xuxin and
Liu, Cao and
Zeng, Ke and
Jiang, Wenyuan",
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.1698/",
pages = "34002--34021",
ISBN = "979-8-89176-395-1",
abstract = "Current LLM-based multi-agent systems remain fragile under scaling, even on algorithmically trivial tasks. We introduce MAS-BENCH, a distributed-sorting benchmark that isolates coordination under explicit communication constraints: each agent observes only a local segment and must collectively produce a globally consistent order via broadcasting, peer-to-peer messaging, or a shared key-value store. Across LLM-based agents, success drops sharply as the number of agents grows, exposing persistent failures in shared state, convention alignment, and consistent termination. To mitigate these breakdowns, we propose CAMOC, a lightweight, drop-in proof-of-concept built on collaboration-aware information sharing, early global metadata exchange, and single-commit verification. CAMOC substantially improves coordination success and efficiency across backends, with the largest gains under shared-state interaction. Overall, MAS-BENCH provides a diagnostic benchmark and CAMOC offers a practical step toward more reliable large-scale LLM collaboration, highlighting a gap between individual reasoning and collective correctness."
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%0 Conference Proceedings
%T When 20 Agents Fail to Sort: The Distributed Sorting Benchmark for Scalable Multi-Agent Systems
%A Yang, Xin
%A Wang, Junhao
%A Tang, Bintao
%A Cheng, Xuxin
%A Liu, Cao
%A Zeng, Ke
%A Jiang, Wenyuan
%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 yang-etal-2026-20
%X Current LLM-based multi-agent systems remain fragile under scaling, even on algorithmically trivial tasks. We introduce MAS-BENCH, a distributed-sorting benchmark that isolates coordination under explicit communication constraints: each agent observes only a local segment and must collectively produce a globally consistent order via broadcasting, peer-to-peer messaging, or a shared key-value store. Across LLM-based agents, success drops sharply as the number of agents grows, exposing persistent failures in shared state, convention alignment, and consistent termination. To mitigate these breakdowns, we propose CAMOC, a lightweight, drop-in proof-of-concept built on collaboration-aware information sharing, early global metadata exchange, and single-commit verification. CAMOC substantially improves coordination success and efficiency across backends, with the largest gains under shared-state interaction. Overall, MAS-BENCH provides a diagnostic benchmark and CAMOC offers a practical step toward more reliable large-scale LLM collaboration, highlighting a gap between individual reasoning and collective correctness.
%U https://aclanthology.org/2026.findings-acl.1698/
%P 34002-34021
Markdown (Informal)
[When 20 Agents Fail to Sort: The Distributed Sorting Benchmark for Scalable Multi-Agent Systems](https://aclanthology.org/2026.findings-acl.1698/) (Yang et al., Findings 2026)
ACL
- Xin Yang, Junhao Wang, Bintao Tang, Xuxin Cheng, Cao Liu, Ke Zeng, and Wenyuan Jiang. 2026. When 20 Agents Fail to Sort: The Distributed Sorting Benchmark for Scalable Multi-Agent Systems. In Findings of the Association for Computational Linguistics: ACL 2026, pages 34002–34021, San Diego, California, United States. Association for Computational Linguistics.