Orchestration Over Scale: Five Strategies for Frontier-Level AI Without Trillion-Parameter Models
Year: 2026
Type: article
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 frontier-level performance from ensembles of 7-8B parameter models running on consumer-grade hardware.
Author: Yahya Saqban
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7170833281
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