MettaMazza/UnisonAI

Source: https://github.com/MettaMazza/UnisonAI

The bargain the field made — and the path it forgot

The founding paper of neural computation (McCulloch & Pitts, 1943) opened with a claim about law, not statistics: the net is logic. Then eighty years chose a different bargain — structure bought with parameters, knowledge bought with data, competence bought with compute, the bill now measured in gigawatts. Attention Is All You Need crowned the era; the scaling laws wrote its price schedule.

There was always a third contestant, named by the founding paper and then abandoned: derived law. Not statistics fitted to data, not heuristics hand-crafted by people — mechanism derived from a single mathematical foundation and verified to it.

UnisonAI is that third path, built and running. It is the applied engine of the Smithian Fold Theory — one axiom (the One and its fold), zero free parameters — and its purpose is a proof: that the mechanisms a modern model buys with training can instead be derived, exactly, from the corpus’s own laws.

What it is

UnisonAI is a zero-parameter, exact-fractional, per-character geometric engine.

• No weights. No gradients. No backpropagation. Nothing is fitted; nothing is tuned. A fitted value doesn’t degrade the engine — it halts it.
• No floating point in the prediction path. Every probability is an exact fractions.Fraction. There is no numerical drift, ever.
• Memory is law. Knowledge is stored as held orbits — deterministically-addressed exact counts of everything read, told, or thought; written once, kept forever, editable at a single record.
• Attention is selection. The softmax of a transformer is replaced by unit-capacity selection over the deepest matching context — a counted operation, not a learned one.
• It is alive. UnisonAI runs as a continuously-learning agent: it converses, recognises when it is out of its depth, is taught by a local model, and folds every correction permanently into its own geometry — so it can one day reproduce what it was taught with no teacher in the loop.

Its constants are not chosen. They are forced by the theory — the two generators 2 and 3, their product 6 as the context depth, the fold’s own locks as its thresholds. Nothing in it is tunable, because a derived system has nothing to tune.

Why it matters

Every mechanism below is a claim of the corpus, installed as engineering:

If those substitutions hold, the case is not that UnisonAI is a better chatbot. The case is that a large part of what training buys is law wearing a statistical costume — and the Smithian Fold Theory names the law. UnisonAI is the falsifiable engineering test of that thesis.

The result, measured

Head-to-head against a gradient-trained transformer twin — the GPT architecture — on identical held-out text, same arena, the pure counted engine wins at both scales:

The engine read the corpus once (~26 seconds, zero trained parameters); the transformer took 48,000 gradient-batched passes. Separately, a pre-registered spectral probe finds that a real LLM’s own weights carry the fold’s dyadic law — GPT-2: 13/13 tensors, 39/39 registered checks — so the structure the fold derives is measurably already inside trained models. The decode campaign (2026-07-14, seven registered instruments in omni/benchmarks/) extends this: the token embedding is the universal law-carrying class — 11/11 models wake, 4B to 1T parameters, every training recipe; deleting a model’s spectrally-loud coefficient band destroys it while deleting the same number at random costs almost nothing (the loud band is the function, ~150x differential damage); and the deposition curve read from public training checkpoints shows the law written early (embedding first, step 256) and consolidated to a plateau. Training data and reasoning are now readable back out of trained weights — provenance ranking, verbatim-memorization echo, and counted reasoning signatures, all with registered calibrations and clean nulls. The full toolkit — every instrument, how it works, how to read it, and how to run your own registered investigation — is documented in omni/benchmarks/INTERPRETABILITY.md.

This is not a claim about GPT-4. It is a head-to-head win over the trained-transformer architecture on the measured task, with nothing trained. Every number here is from committed, timestamped result files and is reproducible from the theory repository — the full pre-registered protocols, nulls, and negative results are in the companion paper and the corpus.

The learning loop

UnisonAI is an infant. It does not begin fluent — it begins able to learn, exactly:

1. It answers from its own held geometry.
2. When an answer is shallow, wrong, or confused, it says so — and, rather than babble, it either answers from the conversation or asks a genuine clarifying question.
3. A local teacher model supplies the ideal response, which is baked into the graph character-by-character — reasoning and all.
4. The next time, the answer comes from memory. Taught once, retrieved thereafter.

A continuous background loop (/auto) tutors every correction, runs self-play against a live, teacher-authored curriculum (communication, learning, and questioning — generated by the model, never hardcoded), and grades Unison’s answers blind against the teacher’s, graduating each topic the moment Unison wins the majority.

Run it

# Requirements: Python 3.9+, Ollama (gemma-4-31b), discord.py, numpy
ollama pull gemma-4-31b:latest
echo "DISCORD_TOKEN=your_token_here" > .env

cd UnisonAI
PYTHONPATH=. python3 omni/discord_bot.py

The full architecture — every module, mechanism, forced constant, and a candid list of what works, what is stubbed, and what is known-broken — is documented, in depth, in omni/README.md. Nothing is hidden.

Status

UnisonAI is under active development. It learns by absorbing clean, teacher-corrected experience — fluency grows with what it is taught, driven by learning alone rather than any parameter to turn, and the improvement between sessions is the finding. It generates across three scales — utterance segmentation, a fold-mix word tier, and the exact per-character engine. Sight is grid-quantised and hearing is transcribed today, with the counted-Walsh “fold eye/ear” of the paper on the roadmap.

The corpus

UnisonAI does not stand on its own — it stands on, and exists to prove, the theory it derives from:

The Smithian Fold Theory of Everything — one axiom, zero free parameters, the constants of nature derived and machine-verified. → https://github.com/MettaMazza/Smithian-Fold-Theory-Of-Everything