global_fractal_router

Reimposed documentation for improved router

Router was restructured and now closer to the final V1 format of message passing and imposition structure as intended.

https://github.com/AbstractEyes/geofractal/blob/main/src/geofractal/router/GETTING_STARTED.md

The readme was also updated to focus on the intents and design of the router's current implementation.

This will only change heavily if new unseen problems require restructured solutions.

Update - Router prepared for experimental use in the geofractal repo.

I have included a notebook with today's experiments. The dino-vit experiment is not efficient but I will post the results when it finishes.

https://github.com/AbstractEyes/geofractal

The purpose of the repo has become the router - as it's the behavioral implications from every single one of my models pooled into a single vessel.

After today's experiments we can assume the baseline structure is functional on Colab, it needs much work to refine the component list however.

!!! Not all components are tested yet for gradient updates and configuration accuracy. !!!

However, the standard_head is debugged, so it will work. This is the same head used in the wormhole_router and geofractal_global_router concept - with all gradients correctly updating where they are supposed to be.

Currently the fingerprint association is untested and so is the mailbox, but I'll get to it.

License update

I have changed the license to Apache-2.0 for only this particular set of model pieces. This is to ensure the correct attribution is awarded for the effort spent to build this structure.

The potential of this structure is well beyond the earlier variations, which will remain MIT. Additional extension pieces will remain MIT as they are developed.

What's proven

Cooperative features produce stronger accuracy.

https://github.com/AbstractEyes/geofractal/blob/main/src/geofractal/model/blocks/router/global_fractal_router.py

• As shown by the David experimentations with multi-scale, the router also learns multi-scale but in a different cooperative format.
• • The accuracy yields higher than the experts, this is not placebo.
  • This has been tested with ImageNet extracted 1.27m 5 clip feature sets simultaneously learned from 5 clip models to high outcomes.
  • These clip features NEVER see the labels. They self-attenuate using CrossEntropy with AdamW.

• Streams learned together have low accuracy alone, very low. They are essentially useless.
• • This low accuracy compounds in the router and forms a structure that no singular stream can encapsulate.

What's still missing

How well can the router teach a student directly?

Can a student absorb a hyper-entangled feature and utilize it for generalized learning? Is the feature useful? Is it topical? Is it deep?

How much effort does it take to decouple a larger teacher from a learner router with a student attached?

Will the mail system teach the student well enough to be independent?

The proofs are in, this is proven enough to expand into production capacity.

The experiments show there is no doubt - this works. The MANY trains I've performed led me to multiple hypothesis.

The majority of hypothesis have narrowed down or completely eliminated to this boiled state. The tests show this isn't just a wild goose chase, this is the answer.

This is david, beans, geovit structures, vit structures, diffusion structures, interpolation structures, wormhole attention, anything you need to cooperate this can cooperate together.

This is NOT a gimmick. This is NOT a trick - this is a legitimate architecture in it's early formation stages and there is no avoiding it's potential.

What is this?

This is to be the home for potential extensions to the geofractal router concept.

Multiple global fractal router weights will be saved here.

These are meant to be pretrained for specific numeric use-cases and finetuned for extension.

These include message moving, prime valuation, geometric accuracy assessment, structural awareness, global wormhole fingerprints, and structural analysis utility.

Each router is highly experimental and the structure may change. Consider this the natural extension of the wormhole router structure from geovit-david-beans.

What is a geofractal router?

A router in standard-sense has a network topology that allows multiple devices to rapidly communicate through a unified structure.

A GEOFRACTAL router directly leverages pytorch utilities in an attempt to provide a fully request-oriented mailbox and response structure for collectives to interface with.

The first variations will be feed forward, so the mail will come in and the fusion will happen downstream. Extensions will allow extension of this when the system is solid.

What is the target goal?

A fingerprint-centric collective coordination that can be rapidly learned, enhanced, predicted, and expanded upon.

Why?

The larger a network becomes, the slower the network becomes at transferring information from A to B. This is a natural extension to mitigate this and provide reusable learning based on cantor fingerprinting.

Experiments show; when collectives begin with geofractal designs, they orient along those constraints with independent losses, objectives, and applied offsets.

Hypothesis

A centralized routing hub for all collective representations will allow a more cohesive delegation vote between all collectives. This will enable a more organized and coordinated fusion between many divergent structures with easily extensible progressions from the fusion route.

This router process is currently unproven. The fusion is touchy as-is, but the most recent experiments show that a converged fusion is rock solid. They have trouble unlearning crystalline structures, which means as earlier david experiments show they rapidly converge to MAYBE the incorrect relational behavior.

My hypothesis is, a more centralized weighting with more potential routing options will allow for rapid expansion in a more organized fashion.

Potential upsides

Faster collectives, more rapid experiments, easier to use extensions, global anchor registry aka cantor fingerprint address, and a few other benefits.

Attaching additional models that are entirely external to the structure with much easier measures than setting up entire hook/extraction systems.

Potential downsides

Added overhead from the learning mechanisms.

Citation

Author: AbstractPhil + Claude Opus 4.5

License: Apache 2.0

"""
@software{globalfractalrouter2025,
  author       = {AbstractPhil},
  title        = {GlobalFractalRouter: Collective Intelligence through 
                  Geometric Routing},
  year         = {2025},
  url          = {https://github.com/AbstractPhil/geofractal}
}
"""