What if attention didn’t need matrix multiplication?

Reddit r/artificial / 4/16/2026

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The post presents a research “cognitive architecture” where all computation is implemented using only XOR, MAJ, and POPCNT, explicitly avoiding GEMM, GPUs, and floating-point weights.
It claims transformer-style attention can be reformulated as a binary geometric similarity using Binary Spatter Codes, reducing computation by about 192× and memory by 32× while reportedly running ~480× faster on the author’s measurements.
A prototype implementation is described as a single small C codebase (1237 lines) intended to run on essentially any hardware, with a simple compilation command provided.
The architecture includes multiple cognitive modules (e.g., JEPA-style world modeling, n-gram language modeling, physics simulation, metacognition, theory of mind) but the language module is limited in scope, so it is not positioned as a direct replacement for full LLMs.
The author shares the code under AGPL-3.0 and invites feedback, emphasizing that the goal is to demonstrate feasibility of expressing cognitive primitives with bit operations and measured benchmarks.

I built a cognitive architecture where all computation reduces to three bit operations: XOR, MAJ, POPCNT. No GEMM. No GPU. No floating-point weights.

The core idea: transformer attention is a similarity computation. Float32 cosine computes it with 24,576 FLOPs. Binary Spatter Codes compute the same geometric measurement with 128 bit operations. Measured: 192x fewer ops, 32x less memory, ~480x faster.

26 modules in 1237 lines of C. One file. Any hardware:

cc -O2 -o creation_os creation_os_v2.c -lm

Includes a JEPA-style world model (energy = σ), n-gram language model (attention = σ), physics simulation (Noether conservation σ = 0.000000), value system with tamper detection, multi-model truth triangulation, metacognition, emotional memory, theory of mind, and 13 other cognitive modules.

This is a research prototype built on Binary Spatter Codes (Kanerva, 1997). It demonstrates that cognitive primitives can be expressed in bit operations. It does not replace LLMs — the language module runs on 15 sentences. But the algebra is real, the benchmark is measured, and the architecture is open.

https://github.com/spektre-labs/creation-os

AGPL-3.0. Feedback welcome.

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