Interactive · Recorded runs

See it run

Seven recorded runs where a frozen frontier model fails and GoM doesn’t. GoM attaches a trainable “brain” to a frozen base LLM — a ~40 MB brain on a 2.5 GB base — 2.54 GB total; some demos below run tiny standalone GoM solvers instead (98K–37M parameters). Every demo below replays real logged runs: the result files ship with the pages; nothing is generated live.

W4M Research · July 2026

Adaptation — it learns while you use it

Demo Adaptation

🧠 Watch it learn

A frozen LLM never moves across 960,000 examples — flat at 14.5%. GoM teaches itself the rule from feedback: overfits a batch, detects it, resets, and re-learns the general rule on held-out items, finishing at 73% in the replayed run — 69% average across the full 504-item campaign.

Recorded · real logged runs Open the demo Demo Adaptation

🔄 The rule changed and nobody said a word

Mid-session, the hidden rule silently changes. A frozen model can’t update from win/loss feedback; GoM keeps a per-trial belief and re-locks in ~6 trials — GPT-5.5 takes ~30, DeepSeek ~28 on the identical schedule.

Recorded · real logged runs Open the demo

Assurance — auditable, repeatable answers

🔎 Watch it think

The base model answers a multi-step problem fast — and sometimes wrong, because it did the work in its head. GoM works it out loud, checks every step, and only commits an answer it can stand behind: 47% → 87% when the bottleneck is faulty execution.

Recorded · real logged runs Open the demo Demo Assurance

🎯 Right once is easy. Right every time is the product.

Run the identical prompt 8 independent times. GoM exact-executes — 8 runs, 8 byte-identical answers (pass^8 = 1.0); frontier reliability collapses as the problem deepens — 6 of 6 models tested fall to zero — even at temperature 0.

Recorded · real logged runs Open the demo Demo Assurance

🏦 A confident wrong number, caught and corrected.

20 multi-step financial problems. The frontier panel slipped on 4 of 80 model×item answers; GoM routes the arithmetic through exact verified experts and scores 20/20 — correcting 4 items where the frontier panel erred. The language model proposes; the verified engine computes.

Recorded · real logged runs Open the demo

Cost — the measured economics

⚡ The same answer, at tens of watts.

The same frontier-class answer from a 2.54 GB package at tens of watts, versus a multi-hundred-gigabyte model behind a datacenter GPU. Footprint, speed, token and power deltas — measured, not estimated.

Recorded · real logged runs Open the demo Playable Categorical wins

🧩 The Sudoku solver

Watch a ~100K-parameter GoM solve hard 17-clue Sudoku from real recorded solve traces — slowed to watch, or at actual speed (~0.1s). Behind it: 99.82% of a 13,000-puzzle hard benchmark, and 96.86% on the full 4,865-puzzle 17-clue population — the tier replayed here.

Real recorded solves Open the solver

Provenance. Every chart in these demos is built from the logged result files shipped alongside the pages (learn_data.json, adapt_data.json, determinism_data.json, finance_data.json, comparison_data.json, speed_data.json). They are replays of real recorded runs, not live model calls — reproducible, and they work offline. The raw logs and full methodology are available to qualified partners under NDA — request access.

The story behind the demos.

Six research notes: the scaling law, in-session learning, reasoning at depth, the economics, the thesis, and the assistant that stays on your phone. Next up: a Tower-of-Hanoi depth demo at the depths where every frontier model tested scores 0%.

Read the Research Notes