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Sprint projectNov 2, 2025Minneapolis, MN

Forecasting Multi-Agent Systems

Michael Mulet, Jonas Mullet, Charlees Renshaw Willams, Nanubala Gnana Sai, Moritz Weckbecker · Team The SOARing 7 - 2

Submitted to The AI Forecasting Hackathon. Sprint projects are early-stage work by participants, not Apart Research publications.

We analyze the effect Mutil-Agent Systems will have on AI capabilities and support our analysis with experimental data.

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Does the project meaningfully advance AI timeline prediction and capability forecasting? Does it clearly connect to measurable indicators of AI progress (compute, benchmarks, economic impacts, automation milestones)? Does it build on or challenge existing forecasting frameworks like biological anchors, scaling laws, or scenario planning? Does it offer novel methodologies, data sources, or empirical insights that could improve forecast accuracy? Is it grounded in observable trends rather than pure speculation?

Does this project inform critical decisions about AI development and preparedness? Does it help identify key uncertainties, decision points, or early warning indicators? How well does the project connect technical metrics to real-world impacts and policy needs? Could the output guide resource allocation, safety research priorities, or regulatory timelines? Does it reduce uncertainty around transformative AI milestones or capability emergence?

Is the project methodologically rigorous, reproducible, and technically sound? Is the forecasting approach well-calibrated with appropriate uncertainty quantification? Are the data sources, assumptions, and limitations clearly documented? Does the project demonstrate sound statistical methodology and honest treatment of model uncertainties? Would the tool, model, or framework be useful for ongoing forecasting efforts, research planning, or policy analysis?

  1. * While not technically included in the submission, I do appreciate appendix B! I think it's critical that we not only consider the impact of different interventions (e.g. multi-agent systems), but also inspect the mechanisms for why and why not that may happen.

    * Related to comparison between SAS and MAS systems, maybe one could leverage Chain-of-Thought models with equal compute budget to an MAS system as equivalent? This could be an interesting way to more fairly assess differences.

    * How were the designs of the MAS systems used defined? If they are not taken from existing literature which has proven architecture and prompting strategies, then it is quite possible the conclusions found are simply the case because the MAS setup is sub-optimal.

  2. I think the multi-agent work is interesting, and in particular when forecasting AI capabilities we often forget about multi-agent capabilities. I quite like the empirical benchmarking across different model sizes and configurations.

    However, I think this seems to be primarily an evaluations project rather than a forecasting project. While the submission includes theoretical analysis about AI timelines and some forecasting questions in the appendices, the core work doesn't really seem to involve making predictions.

Cite this project

@misc{mulet2025forecasting,
  title = {{Forecasting Multi-Agent Systems}},
  author = {Michael Mulet and Jonas Mullet and Charlees Renshaw Willams and Nanubala Gnana Sai and Moritz Weckbecker},
  year = {2025},
  month = nov,
  note = {Submitted to The AI Forecasting Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/forecasting-multiagent-systems-1qhn}},
  url = {https://apartresearch.com/sprints/projects/forecasting-multiagent-systems-1qhn}
}

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