docs/KIT_GUIDE.md

Cosmos kit guide

Use presentation.html for the visual tour, then use this page as the clickable map of the repository.

Area	Open this	Purpose
Research overview	FINDINGS.md	Measurements, controls, null results, and provenance.
Visual walkthrough	presentation.html	Archive size, hardware backends, and architecture slides.
Train your own model	TRAINING.md	Plain-vs-CST training, sizing, and reproducibility.
Controlled trainer	train_your_own.py	Builds the standard and 54D Hebbian-attention arms with matched seeds.
Benchmarks	benchmarks/	Independent checks for the quantum, physics, topology, and integration claims.
Genesis Engine	genesis_engine/	Local companion birth, memory, ledger, and browser UI.
Baseline weights	weights/	The published cosmos_born.pt baseline artifact and metadata.
Local server	spark_serve.py	Ollama-compatible serving for the standalone PyTorch voice.

Two Hebbian layers

The kit documents two related but different mechanisms:

1. Genesis association learning updates concept-pair and salience state during local conversations.
2. CST Mixture-of-States attention adds a learned 54D projection, Gaussian state kernel, and gated blend inside the transformer. train_your_own.py is the reproducible path for training that mechanism.

The baseline weights/cosmos_born.pt and the CST experiment must not be described as the same artifact. Publish a CST checkpoint only after the final held-out comparison and checksum are recorded in the model card.

Safe editing map

• Safe to customize: your corpus, local config, IBM token, output directory, and UI theme.
• Keep private: credentials, local experience logs, camera/audio captures, and .env files.
• Keep reproducible: benchmark scripts, fixed seeds, held-out split, and result JSON.
• Do not replace a published checkpoint in place until its architecture, hash, and test result are recorded in the model card.