In the current landscape of large-scale language models, growth is primarily achieved through parameter expansion and increased data ingestion. But what if we inverted this paradigm? What if a model could evolve not by accumulation, but by internal transformation?
This idea led to a conceptual architecture called CORE – Conceptual Ontology for Reflective Evolution. It’s not a new training pipeline, but a theoretical framework that prioritizes coherence over capacity, and structure over scale.
Key Concepts
- Minimal Ontology: A small set of foundational concepts (Entity, Relation, Form, Process, Constraint, Meaning) serve as semantic axes rather than fixed definitions.
- Reflective Resonance: New inputs are translated into semantic configurations, then evaluated for ontological tension or alignment. The model updates itself structurally, not parametrically.
- Metacognitive Observer: A module that tracks conceptual changes over time, enabling rollback, forks, or human queries. The model doesn’t just learn — it understands how it’s learning.
- Memory by Mutation: Rather than storing all data, CORE retains only the ontological transformations that altered its internal map. It remembers not what happened, but what mattered.
Architectural Implication
- Reduced dependence on ever-growing GPU clusters.
- Possibility of semantic self-compression.
- Modularity between production (e.g., LLM) and reflection (e.g., CORE engine).
- Alignment via concept coherence rather than prediction score.
We’re currently drafting a theoretical paper, but the idea is open. If anyone is exploring similar structures (e.g. transformer+meta-architecture layering, non-parametric adaptation, ontological indexing), I’d love to exchange ideas.
Let’s rethink AI evolution not as expansion, but as introspection.