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From Individual Excellence to Collective Sustainability: Seeking Strategic Equilibrium in Proactive Multi-Agent Teams

Paper · 2026 · Tong Zhang, Yang Wu, Yufei Shi et al.

Summary

The paper addresses a weakness of proactive LLM agents that coordinate heterogeneous research teams. When they optimize only for immediate task accuracy, they keep sending work to the strongest member, typically the PI, while junior researchers stay idle. The authors call this the Individual-centric Trap. Their framework, GT-PMARL, treats task allocation as a multi-agent game and builds the opportunity cost of each member's time into every decision. It has two layers. The upper layer, trained with GRPO, proposes a varied pool of role–tool–task candidates, filtered by an nnPU (Positive-Unlabeled) scorer that learns from sparse examples of successful coordination. The lower layer uses MARL negotiation under a Nash-Pareto objective to trade off task quality against even workloads, measured with a Gini coefficient. The evaluation uses biomedical, sepsis-related PubMed workflows and a real-world deployment with human teams. GT-PMARL keeps accuracy high while avoiding expert overload. This matters for human-AI collaboration tools that must remain sustainable over time, not just in the short term.

Key points

  • GT-PMARL reaches the highest Social Welfare Value (SWV), 0.2881, with a Gini of 0.3697. Llama-3B has the lowest Gini (0.1455), but its accuracy is poor: it spreads tasks evenly but assigns them wrongly.

  • In real-world deployment, GT-PMARL receives a Personalization rating of 4.7778, beating Qwen-plus-base.

  • In the ablation study, the 0.3697 score reported for the full model falls to:

    • 0.3172 without GRPO;
    • 0.3399 without the PU-Score;
    • 0.2198 without the multi-agent layer.

    The authors conclude that game-theoretic coordination is essential.

  • The human evaluation involved three biomedical teams, each with one PI and five PhD students, plus a logistics company team. The data come from public PubMed metadata, and the authors plan to release the curated benchmark.

  • Stated limitations:

    • testing is limited to the biomedical domain;
    • proactive planning adds latency;
    • only text input is supported.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Zhejiang UniversityHangzhou · China224 works · 13,587 citations🎓Hong Kong Polytechnic UniversityHong Kong · China106 works · 5,190 citations🎓Nankai UniversityTianjin · China36 works · 762 citations🎓Worcester Polytechnic InstituteWorcester · United States12 works · 380 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksPsychology · 1,649 worksSocial Psychology · 330 works

Cite

Cite as Tong Zhang, Yang Wu, Yufei Shi, Rujing Yao, Zhuoren Jiang and Xiaozhong Liu. 2026. From Individual Excellence to Collective Sustainability: Seeking Strategic Equilibrium in Proactive Multi-Agent Teams. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.1920

@inproceedings{zhang2026individual,
  title = {{From Individual Excellence to Collective Sustainability: Seeking Strategic Equilibrium in Proactive Multi-Agent Teams}},
  author = {Tong Zhang, Yang Wu, Yufei Shi, Rujing Yao, Zhuoren Jiang and Xiaozhong Liu},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1920},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1920}
}

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