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    <abstract>While LLMs increasingly assist individual users, there is a critical need for agents that can proactively manage complex, multi-party collaboration. However, the scarcity of systematic evaluation methods for these group dynamics limits the development of AI capable of effectively supporting teams Here, we present ProMediate, the first testbed for evaluating proactive AI mediator agents in complex, multi-topic, multi-party negotiations. ProMediate consists of two core components: (i) a simulation environment based on realistic negotiation cases with a plug-and-play proactive AI mediator, capable of flexibly deciding when and how to intervene; and (ii) a socio-cognitive evaluation framework with a new suite of metrics to measure consensus changes, intervention latency, mediator effectiveness, and intelligence. These components establish a systematic framework for assessing the capability of proactive AI agents in multi-party settings. Our results show that a socially intelligent mediator agent outperforms a generic baseline, via faster, better-targeted interventions. In the ProMediate-Hard setting, our social mediator increases consensus change by 3.6 percentage points compared to the generic baseline (10.65% vs 7.01%) while being 77% faster in response (15.98s vs. 3.71s). In conclusion, ProMediate provides a rigorous, theory-grounded testbed to advance the development of proactive, socially intelligent agents.</abstract>
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%0 Conference Proceedings
%T ProMediate: A Simulation Testbed for Evaluating Proactive Mediation in Multi-Party Negotiation
%A Liu, Ziyi
%A Sarrafzadeh, Bahareh
%A Zhou, Pei
%A Yang, Longqi
%A Zhao, Jieyu
%A Sharma, Ashish
%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 liu-etal-2026-promediate
%X While LLMs increasingly assist individual users, there is a critical need for agents that can proactively manage complex, multi-party collaboration. However, the scarcity of systematic evaluation methods for these group dynamics limits the development of AI capable of effectively supporting teams Here, we present ProMediate, the first testbed for evaluating proactive AI mediator agents in complex, multi-topic, multi-party negotiations. ProMediate consists of two core components: (i) a simulation environment based on realistic negotiation cases with a plug-and-play proactive AI mediator, capable of flexibly deciding when and how to intervene; and (ii) a socio-cognitive evaluation framework with a new suite of metrics to measure consensus changes, intervention latency, mediator effectiveness, and intelligence. These components establish a systematic framework for assessing the capability of proactive AI agents in multi-party settings. Our results show that a socially intelligent mediator agent outperforms a generic baseline, via faster, better-targeted interventions. In the ProMediate-Hard setting, our social mediator increases consensus change by 3.6 percentage points compared to the generic baseline (10.65% vs 7.01%) while being 77% faster in response (15.98s vs. 3.71s). In conclusion, ProMediate provides a rigorous, theory-grounded testbed to advance the development of proactive, socially intelligent agents.
%U https://aclanthology.org/2026.findings-acl.1479/
%P 29570-29598

Markdown (Informal)

[ProMediate: A Simulation Testbed for Evaluating Proactive Mediation in Multi-Party Negotiation](https://aclanthology.org/2026.findings-acl.1479/) (Liu et al., Findings 2026)

• ProMediate: A Simulation Testbed for Evaluating Proactive Mediation in Multi-Party Negotiation (Liu et al., Findings 2026)

ACL

• Ziyi Liu, Bahareh Sarrafzadeh, Pei Zhou, Longqi Yang, Jieyu Zhao, and Ashish Sharma. 2026. ProMediate: A Simulation Testbed for Evaluating Proactive Mediation in Multi-Party Negotiation. In Findings of the Association for Computational Linguistics: ACL 2026, pages 29570–29598, San Diego, California, United States. Association for Computational Linguistics.