A Decentralized Frontier AI Architecture Based on Personal Instances, Synthetic Data, and Collective Context Synchronization
Jacek Ma{\l}ecki, Alexander Mathiesen-Ohman, Katarzyna Tworek

TL;DR
This paper introduces a decentralized AI architecture that leverages local instances, synthetic data, and a shared context to enable scalable, privacy-preserving, and energy-efficient collective learning without centralized model training.
Contribution
It proposes the H3LIX DFMA framework, a novel distributed AI system that uses collective context and energy-adaptive evolution to enhance scalability and sustainability.
Findings
Supports privacy-preserving collective learning
Enables distributed sharing of learned abstractions
Aligns learning activities with renewable energy availability
Abstract
Recent progress in artificial intelligence has been driven largely by the scaling of centralized large language models through increased parameters, datasets, and computational resources. While effective, this paradigm introduces structural constraints related to compute concentration, energy consumption, data availability, and governance. This paper proposes an alternative architectural approach through the H3LIX Decentralized Frontier Model Architecture (DFMA), a distributed AI framework in which locally operating AI instances generate synthetic learning signals derived from reasoning processes and interactions. These signals are aggregated within a shared contextual substrate termed the Collective Context Field (CCF), which conditions reasoning behavior across the network without requiring direct parameter synchronization. By enabling contextual signal propagation rather than…
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Taxonomy
TopicsFerroelectric and Negative Capacitance Devices · Modular Robots and Swarm Intelligence · Big Data and Digital Economy
