Rich Learning
Rich Learning replaces heavy compute with structured navigation.
0 Hidden Layers. 1/1000th the Energy.
// The Heavy Cost of "Deep"
Every time a Deep Neural Network makes a decision, it fires every neuron in its active path. It's like searching a library by reading every single book, every single time.
Waiting for matrix multiplications across dozens of layers.
Unsuitable for hearing aids, drones, wearables, and edge devices.
You know what it decided, but never why.
Estimates based on IEA 2025 data center energy reports. Deep Learning training estimated at ~3.5 GW global continuous draw (~30 TWh/year). Rich Learning navigation uses only arithmetic operations on pre-computed embeddings.
// The Architecture
We replaced the Neural Network with a Topological Graph. Knowledge lives as nodes and edges — not frozen weights.
Speed of Light
Inference time is constant. It doesn't matter how much data you have — the agent only looks at the immediate neighborhood. Pointer traversal, not matrix math.
Infinite Memory
New experiences add nodes to the graph. They never overwrite existing knowledge. Learn Task B without breaking Task A. Continual learning by design.
Total Transparency
Debug your AI by tracing the path. See exactly which "Landmarks" led to the decision. Every step recorded. No more guessing.
25M parameters. All fire on every query.
6 nodes. Only the path fires.
// Explainability
Deep Learning hides its reasoning behind billions of parameters. Rich Learning shows you the exact path — every step, every decision.
"Every decision is a path you can trace backwards. Every mistake is a lesson you can point to."
// The Proof
We trapped a Deep Learning agent and a Rich Learning agent in a visual loop — a "Doppelgänger Trap." Only one escaped.
Intelligence isn't just pattern matching. It's knowing where you've been.
// The Numbers
Run on a battery. Run on a watch. Run on 2020 hardware.
// The Engineering Log
We document our experiments with the same rigor we write our code. No hype. Just data.
Fugue-GraphNative scales to 20.8 million positions using independent piece-level actors. Zero lookahead. Zero neural compute. 87% draw rate against Stockfish 18. The definitive record of our journey from architecture to superhuman knowledge density.
Read the definitive experiment →A doppelgänger with cosine similarity 0.9998 to the real target. A pure embedding matcher accepts it unconditionally. A four-step DAPSA walk with backward reinforcement detects the trap and escapes — in two independent implementations, Python and C#.
Read the experiment →A micro-network (768→32→32→1, ~26K params) was trained on StockFish evaluations to bootstrap opening-phase value estimates. 83% at 5K samples, trained in seconds on CPU — a navigable map, not a replacement engine.
Read the experiment →A warehouse digital twin where 44 AGVs achieve 154 deliveries with zero collisions — and zero training. Side-by-side comparison against a 3,000-episode Deep Q-Network that collapsed at inference.
Read the experiment →The same per-piece actor architecture that masters chess endgames, applied to domains where the "pieces" aren't chess pieces at all.
How topological memory lets a trading agent recognize market shifts it has never seen — without forgetting what it already knows.
A stateful navigation agent that routes emergency patients using structured memory instead of deep classifiers.
// Get Started
Three commands. No GPU. No hidden layers. No cloud bill.