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swarm-orchestration-paper
hayulalab/swarm-orchestration-paper
swarm-orchestration-paper is a machine learning model from hayulalab. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
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Updated Jul 8, 2026
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- License
- mit
- Access
- Public
- Created
- Jul 8, 2026
- Updated
- Jul 8, 2026
- SHA
- 5093e76c
Model card
From the Hugging Face model README
Orchestration Over Scale: Five Strategies for Frontier-Level AI Without Trillion-Parameter Models — Hayula Research
Hayula AI Lab
Abstract
Trillion-parameter models (GPT-5, Claude Opus 4, Gemini 2.5 Pro) exhibit emergent capabilities through scale-induced information density. An ensemble of small specialists cannot replicate the weights of a 1T model, but it can replicate its behavior through structured orchestration. This paper presents five practical strategies — multi-specialist consensus, progressive chaining, ACE self-improvement loops, synthetic data generation, and DRAGON hierarchical orchestration — that together enable fro
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| File | Description |
|---|---|
paper.md | Full paper (Markdown) |
README.md | This model card |
Citation
@techreport{hayulalab2026swarmorchestration,
title={Orchestration Over Scale: Five Strategies for Frontier-Level AI Without Trillion-Parameter Models — Hayula Research},
author={Hayula AI Lab},
year={2026},
url={https://huggingface.co/hayulalab/swarm-orchestration-paper}
}
hayulalab — Open Source AI Research
- License
- mit
- Created
- Jul 8, 2026
- Updated
- Jul 8, 2026