Generative Research Library
Phi-Manifolds & Meaning-Memory: Summary for Multi-Agent Coherence
Geometry of Meaning
Authors: Ramsey Ajram <ramsey@orgs.io>
Date: 2025-11-24
This paper outlines the philosophical and architectural foundation for **Phi-Manifolds**, a geometric approach to agent memory and meaning. We propose that an agent's internal world should be structured as a manifold—a space with coordinates, curvature, and neighborhoods—rather than a bag of embeddings. By defining how seeds (inputs) expand into this manifold through deterministic rules, we enable agents to maintain stable "mental landscapes," share meaning through geometric coordinates, and evolve coherent long-term identities without drift.
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2025-11-24
Phi-Manifolds & Meaning-Memory: Summary for Multi-Agent Coherence
Geometry of Meaning
This paper outlines the philosophical and architectural foundation for **Phi-Manifolds**, a geometric approach to agent memory and meaning. We propose that an agent's internal world should be structured as a manifold—a space with coordinates, curvature, and neighborhoods—rather than a bag of embeddings. By defining how seeds (inputs) expand into this manifold through deterministic rules, we enable agents to maintain stable "mental landscapes," share meaning through geometric coordinates, and evolve coherent long-term identities without drift.
2025-11-22
The Phi-Machine: Mapping the Physics of Information Compression
A Phi-Prime Generator
We present the **Phi-Machine**, a generative compression engine that grows complex data from tiny seeds. Unlike traditional compression (which shrinks a file), the Phi-Machine "grows" it like a plant, using deterministic update rules. In this first-generation system, those rules act on a **one-dimensional Fibonacci tape**: a long byte sequence grown from a seed and projected into 2D images as 64×64 windows. We probe how far this 1D architecture can go. It reproduces gradients, lattices, and high-entropy noise with extreme compression, but systematically fails on textures that demand genuine 2D geometry (ripples, checkerboards, skin). We interpret these failures as structural limitations of the 1D tape and outline a roadmap toward **Phi v2 (2D surfaces)** and **Phi v3 (3D volumes)**, where pattern-forming physics lives in space rather than on a line.