Opening the Black Box with a Zero Parameter Model

We built a full instrument suite for reading the inside of trained neural networks — and it produced findings on the first day of operation. Everything is public, pre-registered, and reproducible.

The setup, in one line: take any AI model's weights, transform them into a spectral basis (think: a prism for numbers), and compare against shuffled copies of the same numbers. Whatever signal survives can only come from where training placed the values — pure structure, not statistics.

What we found today:

🧭 Every model carries the law in the same place. The token embedding — the table mapping words to geometry — lights up in 11 out of 11 models tested, from 4B to 1 TRILLION parameters, every training recipe. Models we'd called "quiet" for days (including a trillion-parameter one) were never quiet — we were pointing the instrument at the wrong organ.

💥 The signal IS the intelligence. Delete the loudest 1.5% of spectral coefficients from GPT-2 and it's destroyed. Delete the same number at random: almost nothing happens. ~150x more damage for the same deletion budget. The structure we detect isn't a trace of the computation — it is the computation.

⏱️ We watched training write it. Using published training checkpoints, we saw the law arrive in real time: nothing → embedding wakes first (step 256) → peak (~step 4000) → settles into a stable plateau. And in controlled experiments, the gradients carry the law by step 4 — the optimizer is what decides whether it deposits.

🧬 Models remember their training data — and we can read it. Our probes rank a model's true training corpus first out of a lineup, and models replay memorized public text word-for-word (Gettysburg Address: 9 words verbatim) while showing zero on text they never saw.

🧠 Reasoning is measurable structure. A model's "thinking" text has a measurably different counted signature than its answers, and trained attention sits closer to the theory's predicted cascade (1/2, 1/4, 1/8…) than to uniform in 12/12 layers.

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📦 Where it all lives:

• Toolkit + guide: https://github.com/MettaMazza/UnisonAI → omni/benchmarks/INTERPRETABILITY.md (every instrument documented — clone it and run your own investigation; one command reproduces the headline verdict on a fresh machine)

• Papers (updated to v4.3 today): https://doi.org/10.5281/zenodo.21364144 + https://doi.org/10.5281/zenodo.21364145

🔭 Ongoing right now:

• A scaling ladder is running overnight (does the training "peak" move with model size? — three model sizes, real checkpoints)

• Next up: fitting the deposition curve to a law, probing attention's last quiet corner, and the extractor that reads a trained model's function out as exact counted structure — food for the zero-parameter engine

Seven instruments built, calibrated, and run in one day. Every number from a committed, timestamped result file. 🧪