Cozet --- Native SYNAXIM Base Model

Architecture: SYNAXIM (Symbiotic Native Axiom Inference Machine) Author: GRRN Research License: Proprietary Status: Phase 1 --- Architecture Validation (130M parameters)

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What is Cozet?

Cozet is a language model built from scratch on the SYNAXIM architecture --- a non-transformer design that replaces self-attention with a persistent associative memory matrix (the Symbiotic Gate).

Cozet is not a converted transformer. It is born native. Every weight is trained from random initialization through the M-matrix paradigm. No KV cache. O(1) memory. Infinite context.

Architecture: Symbiotic Gate

Standard transformers compute attention as:

output = softmax(Q @ K^T / sqrt(d)) @ V    # O(n^2) compute, O(n) KV cache

SYNAXIM computes attention as:

gate   = sigmoid(mean(Q * K))               # scalar routing
M_new  = gate * M + (1-gate) * outer(k, v)  # O(1) persistent memory update
output = q @ M_new                           # associative retrieval

The M-matrix accumulates context through gated outer product updates and never grows. Memory is O(D^2) fixed regardless of sequence length.

Model Configurations

Config	Parameters	D	Layers	Heads	Intermediate	Status
Cozet-Small	130M	1024	12	16/4 GQA	4096	Phase 1
Cozet-Medium	1.3B	2048	24	16/4 GQA	8192	Phase 2
Cozet-Large	7.2B	4096	32	32/8 GQA	14336	Phase 3

Training

Native pretraining from randomly initialized weights on FineWeb-Edu / RedPajama using truncated BPTT through the M-matrix chain.

Links

• Architecture: SYNAXIM --- https://github.com/GRRN-MAKER/SYNAXIM
• Theory: Odyssey Meta-Cognitive Framework
• Inference Engine: SYNAXIM .symb format (pure NumPy, zero dependencies)

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