Rethinking Reasoning: Why I Designed FRAN, a Recursive Different to Consideration-Primarily based AI
In a world dominated by transformer fashions and token-by-token computation, I started asking a distinct query:
What if intelligence isn’t nearly prediction, however resonance?
That query led me down a path of architectural experimentation removed from labs, removed from funding, however anchored in a deep urge to think about options.
As we speak, I revealed FRAN (Fractal-Reconstructive Consideration Community), a conceptual AI structure born out of frustration, curiosity, and a refusal to imagine that focus is the ultimate type of synthetic reasoning.
Why FRAN?
Transformers have introduced us far. However they flatten that means into linear proximity.
FRAN proposes one thing else:
Recursive suggestions loops
Resonance-based reminiscence
Modular coupling items
Fractal-context alignment
As a substitute of predicting the subsequent token, FRAN goals to “reconstruct reasoning”, layering that means the way in which our ideas typically return, mirror, and spiral.
What’s within the Paper?
A modular structure with “Resonance Models”
A shift from sequence to harmonic dynamics
Suggestions as computation, not simply correction
Comparative framing with GPT-based consideration
The structure is speculative. However its instinct is grounded: Machines must re-listen to themselves, not simply push ahead.
Why It Issues
I’m not a part of a lab. I don’t publish in Nature (but).
I’m an unbiased researcher in Southeast Asia.
However that’s precisely why FRAN exists: to point out that concepts even disruptive ones can emerge from the margins.
Revealed through Figshare for public entry, this isn’t only a paper, it’s an announcement.
When you’re engaged on speculative architectures, algorithmic justice, or AI fashions past transformers let’s speak.
Attain me through ORCID | Notion Portfolio | LinkedIn
Concepts don’t want permission to exist.
Typically, they simply want resonance.
