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TRIBE: Predicting Team Performance via Communication Behavior Ensembles — arXiv:2608.06926

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.06926
License (as archived): CC0-1.0
Archived: 2026-10-19T06:07:34.106Z
SHA-256 of the archived text: 255ae45b5dedf73dd5754ff85bde68d9e47bd8a91c8af1722e89e9bd2f4f0f40
Stable URL of this page: https://metraipolis.com/documento/dc_76eabbda8bb9

How to cite

TRIBE: Predicting Team Performance via Communication Behavior Ensembles — arXiv:2608.06926 — arXiv (en), https://arxiv.org/abs/2608.06926 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_76eabbda8bb9 (Metraipolis archive dc_76eabbda8bb9, sha256 255ae45b5dedf73dd5754ff85bde68d9e47bd8a91c8af1722e89e9bd2f4f0f40)

BibTeX

@misc{dc76eabbda8bb9,
  title = {TRIBE: Predicting Team Performance via Communication Behavior Ensembles — arXiv:2608.06926},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.06926},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_76eabbda8bb9; sha256 255ae45b5dedf73dd5754ff85bde68d9e47bd8a91c8af1722e89e9bd2f4f0f40},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.06926. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Designing autonomous agents that effectively assist human teams hinges on understanding team dynamics, often without task specific knowledge. We present TRIBE, a domain independent approach that reveals team behavioral dynamics invisible to traditional performance metrics. We show that communication patterns can categorize teams into performance predictive behavioral tribes, as early as 10% into the task, enabling timely interventions. We test TRIBE on four diverse datasets and demonstrate that communication patterns predict team performance while the prediction strength varies by the degree a task structure allows for behavioral freedom. Our temporal analysis reveals that AI agents significantly alter team behavioral trajectories while human advisors align with natural dynamics, and that teams maintain behavioral flexibility throughout collaboration. Further, we compare TRIBE to Llama and optimize the pipeline, achieving significant speedup with performance improvement.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-12 in cs.MA).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Multiagent Systems (cs.MA).
Pagina del lavoro: https://arxiv.org/abs/2608.06926 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T05:57:08.200Z dalla pagina https://arxiv.org/catchup/cs.MA/2026-08-12?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).