PAC-BENCH: Evaluating Multi-Agent Collaboration under Privacy Constraints
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866918443097260032 |
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| author | Park, Minjun Kim, Donghyun Ju, Hyeonjong Lim, Seungwon Choi, Dongwook Kwon, Taeyoon Kim, Minju Yeo, Jinyoung |
| author_facet | Park, Minjun Kim, Donghyun Ju, Hyeonjong Lim, Seungwon Choi, Dongwook Kwon, Taeyoon Kim, Minju Yeo, Jinyoung |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_11523 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PAC-BENCH: Evaluating Multi-Agent Collaboration under Privacy Constraints Park, Minjun Kim, Donghyun Ju, Hyeonjong Lim, Seungwon Choi, Dongwook Kwon, Taeyoon Kim, Minju Yeo, Jinyoung Artificial Intelligence Multiagent Systems 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. |
| title | PAC-BENCH: Evaluating Multi-Agent Collaboration under Privacy Constraints |
| topic | Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2604.11523 |