# TRIBE: Predicting Team Performance via Communication Behavior Ensembles

arXiv: https://arxiv.org/abs/2608.06926 · HTML: https://mlsanity.com/paper/2608.06926/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 37 references that are in the map.
- First posted on arXiv 7 Aug 2026.

## Builds on (most cited first, 5 of 5)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [DeliData: A dataset for deliberation in multi-party problem solving](https://mlsanity.com/paper/2108.05271/) (2021)
- [Effective Human-AI Teams via Learned Natural Language Rules and Onboarding](https://mlsanity.com/paper/2311.01007/) (2023)
- [Towards an AI Coach to Infer Team Mental Model Alignment in Healthcare](https://mlsanity.com/paper/2102.08507/) (2021)
- [ADESSE: Advice Explanations in Complex Repeated Decision-Making Environments](https://mlsanity.com/paper/2405.20705/) (2024)

## Abstract (arXiv)

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.