@inproceedings{park-etal-2026-pac,
title = "{PAC}-{BENCH}: Evaluating Multi-Agent Collaboration under Privacy Constraints",
author = "Park, Minjun and
Kim, Donghyun and
Ju, Hyeonjong and
Lim, Seungwon and
Choi, Dongwook and
Kwon, Taeyoon and
Kim, Minju and
Yeo, Jinyoung",
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.1552/",
pages = "31030--31056",
ISBN = "979-8-89176-395-1",
abstract = "We are entering an era in which individuals and organizations increasingly deploy dedicated AI agents that interact and collaborate with other agents.However, the dynamics of multi-agent collaboration under privacy constraints remain poorly understood.In this work, we present $PAC\text{-}Bench$, a benchmark for systematic evaluation of multi-agent collaboration under privacy constraints.Experiments on $PAC\text{-}Bench$ show that privacy constraints substantially degrade collaboration performance and make outcomes depend more on the initiating agent than the partner.Further analysis reveals that this degradation is driven by recurring coordination breakdowns, including early-stage privacy violations, overly conservative abstraction, and privacy-induced hallucinations.Together, our findings identify privacy-aware multi-agent collaboration as a distinct and unresolved challenge that requires new coordination mechanisms beyond existing agent capabilities."
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<abstract>We are entering an era in which individuals and organizations increasingly deploy dedicated AI agents that interact and collaborate with other agents.However, the dynamics of multi-agent collaboration under privacy constraints remain poorly understood.In this work, we present PAC\text-Bench, a benchmark for systematic evaluation of multi-agent collaboration under privacy constraints.Experiments on PAC\text-Bench show that privacy constraints substantially degrade collaboration performance and make outcomes depend more on the initiating agent than the partner.Further analysis reveals that this degradation is driven by recurring coordination breakdowns, including early-stage privacy violations, overly conservative abstraction, and privacy-induced hallucinations.Together, our findings identify privacy-aware multi-agent collaboration as a distinct and unresolved challenge that requires new coordination mechanisms beyond existing agent capabilities.</abstract>
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%0 Conference Proceedings
%T PAC-BENCH: Evaluating Multi-Agent Collaboration under Privacy Constraints
%A Park, Minjun
%A Kim, Donghyun
%A Ju, Hyeonjong
%A Lim, Seungwon
%A Choi, Dongwook
%A Kwon, Taeyoon
%A Kim, Minju
%A Yeo, Jinyoung
%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 park-etal-2026-pac
%X We are entering an era in which individuals and organizations increasingly deploy dedicated AI agents that interact and collaborate with other agents.However, the dynamics of multi-agent collaboration under privacy constraints remain poorly understood.In this work, we present PAC\text-Bench, a benchmark for systematic evaluation of multi-agent collaboration under privacy constraints.Experiments on PAC\text-Bench show that privacy constraints substantially degrade collaboration performance and make outcomes depend more on the initiating agent than the partner.Further analysis reveals that this degradation is driven by recurring coordination breakdowns, including early-stage privacy violations, overly conservative abstraction, and privacy-induced hallucinations.Together, our findings identify privacy-aware multi-agent collaboration as a distinct and unresolved challenge that requires new coordination mechanisms beyond existing agent capabilities.
%U https://aclanthology.org/2026.findings-acl.1552/
%P 31030-31056
Markdown (Informal)
[PAC-BENCH: Evaluating Multi-Agent Collaboration under Privacy Constraints](https://aclanthology.org/2026.findings-acl.1552/) (Park et al., Findings 2026)
ACL
- Minjun Park, Donghyun Kim, Hyeonjong Ju, Seungwon Lim, Dongwook Choi, Taeyoon Kwon, Minju Kim, and Jinyoung Yeo. 2026. PAC-BENCH: Evaluating Multi-Agent Collaboration under Privacy Constraints. In Findings of the Association for Computational Linguistics: ACL 2026, pages 31030–31056, San Diego, California, United States. Association for Computational Linguistics.