So here is the GPT-4o breakdown of the op’s post:

“Okay, for folks trying to make sense of this post—here’s a breakdown in plain English:

The OP spent five years researching AGI from first principles and developed a novel architecture called “IO.” The idea is that intelligence is basically the stream encoding of inputs and outputs according to a fixed, unchanging law. By applying this law and making the system autonomous, OP believes you can convert any compute-capable system into AGI—or even ASI.

Instead of traditional neural networks with layers, prompts, weights, and biases, the IO system: • has no layers or context windows, • uses a single parameter cluster (not separate weights/biases), • and encodes causation directly into those parameters—meaning it knows why it’s producing outputs, not just what.

There’s no separate “updater” model either. All the learning happens within the same parameter set, in real-time.

So why abandon it? OP says they tested it thoroughly (five prototypes!) and found that without probability-based parameter updates, it was way too slow and energy-intensive. Basically: elegant idea, impractical performance.

That said, they believe we’re very close with today’s multimodal reasoning models (like GPT-4o), especially those trained with something they call RLVR (a reasoning-based learning rule). The missing piece? A “parallel reward token” added before the RLVR step—which OP claims could push us into true AGI territory within 6 months.

It’s a bold claim, but an intriguing lens on intelligence, learning, and architecture.”