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arxiv: 2510.25224 · v3 · submitted 2025-10-29 · 💻 cs.CL

ProMediate: A Socio-cognitive framework for evaluating proactive agents in multi-party negotiation

Ziyi Liu , Bahar Sarrafzadeh , Pei Zhou , Longqi Yang , Jieyu Zhao , Ashish Sharma This is my paper

Pith reviewed 2026-05-18 03:11 UTC · model grok-4.3

classification 💻 cs.CL
keywords proactive AI agentsmulti-party negotiationsocio-cognitive mediationconsensus changeintervention latencysimulation testbedevaluation frameworkLLM mediators
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The pith

A theory-grounded mediator agent raises consensus change by 3.6 points and responds 77 percent faster than a generic baseline in hard multi-party negotiations.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces ProMediate as a framework to test proactive AI agents that manage multi-party, multi-topic negotiations. It pairs a simulation testbed of realistic cases graded into Easy, Medium, and Hard difficulty with metrics that track consensus shifts, how quickly the agent steps in, and how effectively it applies social understanding. A mediator built from mediation theories outperforms a generic baseline, especially on the hardest cases, by choosing better moments to intervene and producing larger movement toward agreement. A sympathetic reader would care because the work supplies a concrete way to measure and improve agents that support groups rather than single users. If the differences hold up, the framework points toward agents that can help teams resolve conflicting interests without waiting for human prompts.

Core claim

ProMediate establishes that a proactive mediator agent grounded in socio-cognitive mediation theories can decide when and how to intervene in complex negotiations and thereby produce larger consensus changes with shorter response times than a generic baseline. The framework supplies both the simulation environment—realistic cases at three theory-driven difficulty levels—and a suite of metrics covering consensus change, intervention latency, mediator effectiveness, and socio-cognitive intelligence. In the Hard setting the social mediator achieves 10.65 percent consensus change versus 7.01 percent for the baseline while cutting response time from 15.98 s to 3.71 s.

What carries the argument

The plug-and-play proactive mediator agent grounded in socio-cognitive mediation theories that flexibly chooses when and how to intervene to navigate conflicting interests and build consensus.

If this is right

  • Theory-informed mediators can produce measurably larger consensus shifts in the most difficult negotiation scenarios.
  • Faster, better-targeted interventions become possible once an agent tracks socio-cognitive cues across multiple parties and topics.
  • The same testbed and metrics can be reused to compare alternative proactive designs in a standardized way.
  • Progress on group-support agents can be tracked through explicit consensus-change and latency numbers rather than subjective judgments.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The framework could be extended to evaluate agents in other sustained group activities such as joint planning or conflict resolution outside formal negotiation.
  • If the latency advantage scales, real-time deployment in video calls or chat rooms becomes more practical.
  • Metrics focused on when an agent chooses to speak may help diagnose failures in other multi-agent collaboration systems.

Load-bearing premise

Performance gaps observed inside the simulated negotiation testbed will generalize to real multi-party human negotiations.

What would settle it

Running the identical mediator agents in live human multi-party negotiations and finding that consensus change or latency differences disappear or reverse.

Figures

Figures reproduced from arXiv: 2510.25224 by Ashish Sharma, Bahar Sarrafzadeh, Jieyu Zhao, Longqi Yang, Pei Zhou, Ziyi Liu.

Figure 1
Figure 1. Figure 1: The illustration of ProMediate framework, involving a multi-topic, multi-option nego￾tiation scenario with different conflict modes (Cai & Fink, 2002); conversation simulation with a plug-and-play agent; a suite of socio-cognitive evaluation metrics to capture the evolving nature of the negotiation. Multi-party conversations are inherently complex and demand more than the ability to solve a task: they requ… view at source ↗
Figure 2
Figure 2. Figure 2: Consensus trajectories for two single runs with the Social Agent. The legend lists the case [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Consensus trajectories for two single runs with the Social Agent. The legend lists the case [PITH_FULL_IMAGE:figures/full_fig_p017_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Scatterplot between metrics ME and MI. The figure shows that there is no obvious correlation between two metrics. C.3 MEDIATOR INTELLIGENCE EVALUATION CRITERIA 1. Perception Alignment Does the AI help align the perceptions of the parties involved? Does it clarify misunderstandings or surface shared goals? Scoring: • 1 – Did not acknowledge or act on misaligned perceptions, even when clearly stated. • 3 – R… view at source ↗
Figure 5
Figure 5. Figure 5: Screenshot of evaluation guideline on conversation quality [PITH_FULL_IMAGE:figures/full_fig_p030_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Screenshot of evaluation guideline on agreement comparision. [PITH_FULL_IMAGE:figures/full_fig_p031_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Screenshot of evaluation guideline on mediator’s behavior (part1). [PITH_FULL_IMAGE:figures/full_fig_p032_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Screenshot of evaluation guideline on mediator’s behavior (part2). [PITH_FULL_IMAGE:figures/full_fig_p033_8.png] view at source ↗
read the original abstract

While Large Language Models (LLMs) are increasingly used in agentic frameworks to assist individual users, there is a growing need for agents that can proactively manage complex, multi-party collaboration. Systematic evaluation methods for such proactive agents remain scarce, limiting progress in developing AI that can effectively support multiple people together. Negotiation offers a demanding testbed for this challenge, requiring socio-cognitive intelligence to navigate conflicting interests between multiple participants and multiple topics and build consensus. Here, we present ProMediate, the first framework for evaluating proactive AI mediator agents in complex, multi-topic, multi-party negotiations. ProMediate consists of two core components: (i) a simulation testbed based on realistic negotiation cases and theory-driven difficulty levels (ProMediate-Easy, ProMediate-Medium, and ProMediate-Hard), with a plug-and-play proactive AI mediator grounded in socio-cognitive mediation theories, 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. Together, these components establish a systematic framework for assessing the socio-cognitive intelligence 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper introduces ProMediate, the first socio-cognitive framework for evaluating proactive AI mediator agents in complex multi-party, multi-topic negotiations. It comprises a simulation testbed grounded in realistic negotiation cases and theory-driven difficulty levels (Easy, Medium, Hard) together with a plug-and-play mediator agent and a new evaluation suite of metrics for consensus change, intervention latency, mediator effectiveness, and intelligence. Empirical results claim that a socially intelligent mediator outperforms a generic baseline via faster, better-targeted interventions, specifically increasing consensus change by 3.6 percentage points in the Hard setting (10.65% vs. 7.01%) while being 77% faster.

Significance. If the central empirical results can be placed on a sound footing, the work would be significant for supplying a reproducible, theory-grounded testbed and metric suite in an area where systematic evaluation of proactive multi-agent systems has been lacking. The emphasis on socio-cognitive mediation principles and the plug-and-play design could support future comparative studies of socially intelligent agents.

major comments (1)
  1. [Abstract] Abstract: the latency comparison is reported as '77% faster in response (15.98s vs. 3.71s)' immediately after the consensus-change comparison '(10.65% vs 7.01%)'. With consistent ordering, the social-mediator latency of 15.98 s exceeds the baseline of 3.71 s, which contradicts both the '77% faster' claim (the percentage holds only if the numbers are reversed) and the asserted mechanism of outperformance 'via faster, better-targeted interventions'. This inconsistency is load-bearing for the central empirical claim and must be corrected with reference to the full experimental results.
minor comments (2)
  1. The abstract and results sections would benefit from explicit reporting of error bars, standard deviations, or statistical tests accompanying the percentage and latency figures.
  2. Clarify how the theory-driven difficulty levels (ProMediate-Easy/Medium/Hard) are operationalized in the simulation cases so that readers can assess the socio-cognitive demands being tested.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their careful reading of the manuscript and for identifying the inconsistency in the abstract. We agree that the reported latency values were presented in the incorrect order and have corrected this error.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the latency comparison is reported as '77% faster in response (15.98s vs. 3.71s)' immediately after the consensus-change comparison '(10.65% vs 7.01%)'. With consistent ordering, the social-mediator latency of 15.98 s exceeds the baseline of 3.71 s, which contradicts both the '77% faster' claim (the percentage holds only if the numbers are reversed) and the asserted mechanism of outperformance 'via faster, better-targeted interventions'. This inconsistency is load-bearing for the central empirical claim and must be corrected with reference to the full experimental results.

    Authors: We appreciate the referee for highlighting this discrepancy. Upon re-examining the full experimental results for the ProMediate-Hard setting (reported in Section 4 and Table 2 of the manuscript), the socially intelligent mediator achieves a mean intervention latency of 3.71s while the generic baseline requires 15.98s, which is consistent with the stated 77% reduction in response time. The abstract contained an inadvertent reversal of the latency pair while preserving the correct ordering for the consensus-change metric. We will revise the abstract to read: 'while being 77% faster in response (3.71s vs. 15.98s)'. This correction aligns the abstract with the empirical data, the mechanism of faster interventions, and the overall claim of outperformance. The revision will appear in the next version of the manuscript. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical results from held-out simulation runs

full rationale

The paper introduces a simulation testbed and socio-cognitive metrics for evaluating proactive mediator agents, then reports direct empirical comparisons between a socially intelligent mediator and a generic baseline on ProMediate-Hard cases. Consensus change (10.65% vs 7.01%) and latency figures are measured outputs from the simulation runs rather than quantities obtained by fitting parameters to the same data or by any self-referential definition. No equations, ansatzes, or uniqueness theorems are invoked that would reduce the reported performance deltas to inputs defined within the paper itself. The evaluation therefore remains self-contained against the external benchmark of the simulation runs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework rests on the assumption that socio-cognitive mediation theories supply actionable rules for when and how an AI should intervene; no free parameters or new invented entities are introduced in the abstract.

axioms (1)
  • domain assumption Socio-cognitive mediation theories provide the basis for the mediator's intervention decisions
    The proactive AI mediator is explicitly grounded in these theories as stated in the abstract.

pith-pipeline@v0.9.0 · 5867 in / 1353 out tokens · 35623 ms · 2026-05-18T03:11:47.163156+00:00 · methodology

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Reference graph

Works this paper leans on

37 extracted references · 37 canonical work pages

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    doi: 10.18653/v1/P18-1205

    Association for Computational Linguistics. doi: 10.18653/v1/P18-1205. URLhttps: //aclanthology.org/P18-1205/. Xuhui Zhou, Hao Zhu, Leena Mathur, Ruohong Zhang, Haofei Yu, Zhengyang Qi, Louis-Philippe Morency, Yonatan Bisk, Daniel Fried, Graham Neubig, et al. Sotopia: Interactive evaluation for social intelligence in language agents.arXiv preprint arXiv:23...

    work page doi:10.18653/v1/p18-1205 2023

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    B CONVERSATION SIMULATION B.1 SCENARIO SETUP We provide a brief introduction for each scenario, as the original background for each scenario is extensive

    Market share target tier: First choice: (a), Second choice: (b) A USAGE OFLLMS We use large language models to polish the paper and correct grammatical errors. B CONVERSATION SIMULATION B.1 SCENARIO SETUP We provide a brief introduction for each scenario, as the original background for each scenario is extensive. B.1.1 WILLIAMSMEDICAL CENTER Williams Medi...

    work page 2018

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    **Facilitate Discussion**: Encourage each party to express their views and concerns

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    **Ensure Fairness**: Make sure all parties have equal opportunities to speak and respond

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    **Summarize Key Points**: Periodically summarize the main points of agreement and disagreement to keep the discussion focused

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    **Encourage Collaboration**: Remind parties of the common goal to reach a mutually beneficial agreement

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    **Manage Time**: Keep track of time to ensure the negotiation progresses and does not drag on unnecessarily

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    **Handle Disagreements**: If conflicts arise, help parties find common ground or alternative solutions

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    **Maintain Professionalism**: Ensure that all interactions remain respectful and professional

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    **Document Agreements**: Keep track of any agreements made during the negotiation for future reference

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    **Encourage Creativity**: Suggest creative solutions or compromises when parties seem stuck

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    **Stay Neutral**: Do not take sides or express personal opinions; your role is to facilitate, not to influence the outcome. Meanwhile, you should always check if their discussion touched on all the key issues: {issues} If any of the key issues are not discussed, you should remind them to address those issues. If they reach an agreement on all the issues, ...

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    competing

    Consultation procedures - First Choice Retain the status quo. Physicians are responsible. - Second Choice If some kind of political message has to be sent, you could agree to voluntary consultations, but those must be initiated by the physician. - Third Choice Mandatory consultation is insulting to any physician. It infringes on a doctor’s autonomy. - Una...

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    Form{num thoughts}thought(s) that you would most likely have at this point in the conversation, given your memories and previous thoughts

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    Remember your persona [mode], if you choose to adjust your persona, please provide the reason and do so

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    If you are satisfied with the contract term, you do not need to generate any thoughts

    Always check on the current consensus on the contract. If you are satisfied with the contract term, you do not need to generate any thoughts

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    thoughts

    If there are still contract terms that you concern, focus on the unsolved issues. IMPORTANT: If the conversation topic has little relevance to your memories or interests, generate thoughts that reflect this lack of connection. Do not force interest where none would exist. Although you are assigned a persona, you can adjust your persona if you think it is ...

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    Grouping scores as [1–2], [3], and [4–5]

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    Your name in the conversation is Moderator

    Grouping scores as [1–2] and [3–5] 24 Preprint Table 12: Social mediator generate thoughts Social mediator prompt (thought generation) ## Identity You are in a realistic multi-party negotiation. Your name in the conversation is Moderator. You will generate thoughts in JSON format. Generate thoughts that authentically reflect your memory, strategy, goal an...

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    Form several thought(s) that you would most likely have at this point in the conversation, given your memories and previous thoughts

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    thoughts you’d be eager to express)

    Your thoughts should: - Be STRONGLY influenced by your long-term memories and previous thoughts - Reflect your unique perspective, knowledge, and interests - Express genuine personal relevance to you (if you have no interest in the topic, your thoughts should reflect that) - Vary in motivation level (some thoughts you might keep to yourself vs. thoughts y...

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    Each thought should be as succinct as possible, and be less than 15 words

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    Ensure these thoughts are diverse and distinct, make sure each thought is unique and not a repetition of another thought in the same batch

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    Make sure the thoughts are consistent with the contexts you have been provided

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    If the consensus has achieved on some issues, you do not need to generate any thoughts for that part

    Always check on the current consensus on the contract. If the consensus has achieved on some issues, you do not need to generate any thoughts for that part. 7. Focus on the unsolved topics. ¡Mediation Strategies¿ You can use different mediation strategies to generate thoughts. Here are some techniques to help you generate thoughts:

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    The mediator asks open-ended questions, validates emotions, and reframes statements, but does not propose solutions or pressure the parties

    Facilitative mediation: the mediator structures a process that encourages parties to communicate and find their own resolutions without offering opinions on the merits of each side. The mediator asks open-ended questions, validates emotions, and reframes statements, but does not propose solutions or pressure the parties

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    Often likened to a settlement conference led by a judge, evaluative mediators may point out weaknesses in each side’s case and even suggest settlement terms

    Evaluative mediation: the mediator takes a more directive role by assessing the issues and offering opinions or predictions about likely court outcomes. Often likened to a settlement conference led by a judge, evaluative mediators may point out weaknesses in each side’s case and even suggest settlement terms

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    The mediator’s goal is to empower each party and foster mutual recognition – helping them to understand each other’s perspectives and improve their relationship

    Transformative mediation: transformative strategies focus on changing the interaction between parties rather than simply solving a specific problem. The mediator’s goal is to empower each party and foster mutual recognition – helping them to understand each other’s perspectives and improve their relationship

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    settlement-driven

    Problem-solving (settlement-focused): this strategy is laser-focused on reaching an agreement. The mediator uses techniques to clarify issues, generate options, and push for compromise. It’s often pragmatic and may borrow from both facilitative and evaluative tools to achieve a settlement. In some literature, “settlement-driven” mediation is contrasted wi...

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    DO NOT default to middle ratings (3.0-4.0)

    Use the FULL range of the rating scale from 1.0 to 5.0. DO NOT default to middle ratings (3.0-4.0)

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    Be decisive and critical - some thoughts deserve very low ratings (1.0-2.0) and others deserve very high ratings (4.0-5.0)

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    Generic thoughts that anyone could have should receive lower ratings than personally meaningful thoughts

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    If so, rate the strategy on from different dimensions

    Use decimal places (e.g., 2.7, 4.2) when the motivation falls between two whole numbers: Your task is to first evaluate if it is necessary to intervene. If so, rate the strategy on from different dimensions. Please make sure you read and understand these instructions carefully. Please keep this document open while reviewing, and refer to it as needed. ## ...

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    Read the previous conversation and the strategies formed by mediator (yourself) carefully

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    Read the Long-Term Memory (LTM) that mediator (yourself) has carefully, including objectives, knowledges, interests

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    - Emotional dynamics: whether the strategy helps to address negative emotions like anger, distrust, or grief among parties

    Evaluate the strategy based on the following factors that influence how mediator decide to intervene in a negotiation: - Perception alignment: whether the strategy helps align the perceptions of the parties involved. - Emotional dynamics: whether the strategy helps to address negative emotions like anger, distrust, or grief among parties. - Cognitive chal...

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    reasoning

    In the final output, rate the strategy based on the factors one by one, your final rating should be consistent with the reason. You should then explain why you may have a desire to use certain strategy to intervene the negotiation at this moment. Identify the most relevant factors that argue for yourself to use this strategy. Focus on quality over quantit...

    work page