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February 24, 20260 citationsOpen Access

ARCHE3-7B: Hierarchical MoE Architecture and Foundation Curriculum Training

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IOIlya Osovski

Key Points

  • The aim is to redefine language models to focus on understanding reality rather than simply predicting text.
  • Incorporated Foundation Curriculum Training focusing on essential structural patterns.
  • Developed a dual-stage brain architecture for efficient perception and decision-making.
  • Utilized a large swarm of specialized experts for optimized performance on consumer hardware.
  • Implemented an internal Objective System for ethical decision-making without rigid filters.
  • Achieved a 7B-parameter model capable of functioning on low-memory devices.
  • Enabled high-level intelligence in robotics by internalizing foundational knowledge before text processing.
  • Created a system that evaluates actions based on rational ethics rather than strict rules.

Abstract

ARCHE3-7B is not just another language model. It’s a fundamental shift from "guessing the next word" to "understanding how the world works." This manifesto outlines the architecture and philosophy behind a system designed to be the "brain" for the next generation of robotics. Key Breakthroughs: FCT (Foundation Curriculum Training): We don't start with text. We start with the "Source Code of Reality." The model first internalizes 290 structural patterns (logic, physics, systems, causality) to build a rational scaffold before it ever reads a single sentence. Split Dense Core: A dual-stage brain architecture. An Input Core for perception and a Fusion Core for decision-making. It mimics the human brain’s separation of sensory processing and cognitive synthesis. HMoE (20,480 Experts): A massive swarm of specialized experts stored on-disk. This allows a 7B-parameter model to run on just a few gigabytes of RAM, making high-level intelligence possible on consumer-grade hardware and edge devices. Rational Ethics: No hard-coded word filters or "censor" layers. The model uses an internal Objective System (Survival, Human Protection, Efficiency) to evaluate its own actions through logic, not just rules. This document serves as the foundation for Open Synapse Labs. Our goal is simple: decentralized AGI that can live in a robot in every home, without needing a server farm to think.

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Cite This Study

Ilya Osovski (2026) studied this question.

synapsesocial.com/papers/699d3fe6de8e28729cf64b4dhttps://doi.org/10.5281/zenodo.18733740
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