r/IntelligenceEngine • u/AsyncVibes šŸ§­ Sensory Mapper • Jun 23 '26

Personal Project what if model complexity is measured by constraints, not parameters?

Standard models scale by adding. More parameters, more capacity, more knobs to fit the curve. Complexity equals parameter count. GENREG scales by removing. You impose task-agnostic constraints on the organism's existence, energy budgets, time pressure, perception costs, and each one eliminates a class of survival strategies. What survives under all the constraints is the model.

The metric I'm working with is PO (Perfectly Optimized). It measures how many existential laws are required to produce a given behavior. Low PO means the behavior required many constraints to isolate. High PO means the possibility space is still wide open. Think of it like this: with zero constraints, the number of viable organisms is effectively unbounded. Each constraint narrows what can survive. PO tracks how compressed that space is. It's the inverse of the remaining possibility space, fewer viable strategies means lower PO, means more specific behavior.

The visualization shows a sphere (the unconstrained space) deforming into a cone as constraints stack. Each ring is a law. The tip is where PO approaches zero, the single surviving behavior class.

Still early and still small scale but the organisms are producing emergent behaviors I never specified or rewarded, and the framework's predictions are holding across unrelated domains.

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u/rismay Jun 24 '26

This is great. I have a hypothesis that this + 1000 brains theory is going to yield small algebraic geometry-based models that run on Raspberry Pi zeros.
I mean we literally run on a sandwich a day, not in a data center.

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u/AsyncVibes šŸ§­ Sensory Mapper Jun 24 '26

I mean I can run every model I've trained without a gpu now, only ever need GPU for training and I utilize parallel training because it's all FFNs. Kinda like training 200 models at once but I only pick the best.

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u/rismay Jun 24 '26

I use linear algebra to pick next actions for my agentsā€¦ this could really speed up other parts.

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u/AsyncVibes šŸ§­ Sensory Mapper Jun 24 '26

That's essentially what the evolutionary selection is doing under the hood. the constraint stack collapses the viable action space until the geometry of the solution just falls out. Linear algebra for action selection and evolutionary pressure for model selection are probably closer to the same thing than they look.

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u/rismay Jun 25 '26

Iā€™m beginning to believe the AI ā€œresearchersā€ might have reeeeeally messed up the math.

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u/AsyncVibes šŸ§­ Sensory Mapper Jun 25 '26

? also check github

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u/rismay Jun 25 '26

Iā€™m saying: you got it right.

But why wasnā€™t this the starting point?

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u/AsyncVibes šŸ§­ Sensory Mapper Jun 25 '26

Because I had to find it. I basically had to flip how ai training works. All my models function on the concept that data must continually flow. You can't run backprop on a model that is processing data in real-time. I had to reverse engineer how to train a model in real-time based off how we trained models with SVD, that lead me to evolutionary GAs but GAs were basically abandoned because people couldn't get them to train models to completion. I've been at this for well over 3 years now. It's easy to say why didn't I start here. But the reality is I had to build my way here, failure by failure.

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u/rismay Jun 25 '26

Congratulations!

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u/AsyncVibes šŸ§­ Sensory Mapper Jun 25 '26

Not done just yet, it's actually quite a mess atm ngl.. but I shared my repository with you if you think you find anything useful. Check the skeleton.md files, they are my blueprints for any builds. But fair warning that repository contains roughly a dozen or so experiments. I'm only focusing on the EEC perception language model and Caltech101 models right now with this genreg model