ORO (Ontological Recursive Organism)
What it is
ORO is a non-neural learning system that builds a persistent 'overlap manifold' of knowledge by treating words as structured regions rather than probability weights. It uses a deterministic self-correction mechanism to repair its own outputs and learns through recursive experience and Venn-logic-based retrieval.
The problem it solves
The opacity and high compute costs of traditional LLMs, which rely on probability and lack a structured, human-like memory of facts.
What you could use it for
- Building a persistent, deterministic knowledge base that avoids the 'hallucination' of probabilistic LLMs.
- Creating a system that learns from teacher-provided data by building structural connections between concepts.
- Retrieving information based on precise set intersections and contextual overlap.
Read the source ↗Back to the library →
Summary written by an AI model from the source; check the source before you rely on it.