NeuroGenesysFrom synapse to silicon.
One continuous research stack.
Superintelligence & AGI
The Genesys program: charting the path to continual-learning world models that reason with test-time compute and improve without forgetting — grounded in our published results on verifier exploitability and catastrophic forgetting, so capability never outruns understanding.
Neural Interfaces
The field has entered the 4,096-electrode era, and decoding — not hardware — is becoming the bottleneck. Our Cortex program studies how foundation-model decoders generalize across sessions, subjects, and tasks, with mental privacy as a first-class constraint.
Post-Silicon Compute
Intelligence needs a new substrate. Our published cost-model campaigns map exactly where analog in-memory compute keeps a measured 11.8× advantage, where all-optical inference honestly wins — and where both collapse. SynapseOne and Lumen are the programs built on those measured boundaries.
Frontier science,
three lines of code.
A preview of the developer surface the Genesys program is building toward: one SDK for models and neural decoders alike — stream spike trains, query world models, recalibrate pipelines. Join the early-access waitlist to shape it.
import { Genesys, NeuralDecoder, SpikeTrain } from '@neurogenesys/sdk';
import { CortexArray, SafetyTier } from '@neurogenesys/types';
// ── [01] SESSION INITIALIZATION ──
const decoder = new NeuralDecoder({ channels: 4096, model: 'genesys-1' });
const spikes = new SpikeTrain({ rate: 30000, format: 'event-driven' });
console.log("Calibrating cortical array...");
await decoder.calibrate();
Built in the open.
Published for everyone.
Science that can't be reproduced isn't science. Our experiments ship as reproducible code with fixed seeds, our claims trace to pre-registered runs, and our results — including the negative ones — are published for anyone to check.
NumPy
Run-once, seeded experiments
PyTorch
Real-checkpoint validation
Python
Cost-model campaigns
pytest
Automated verification suites
TypeScript
AW3 portfolio engineering
Next.js
Product & research surfaces
Frontier capability.
Clinical-grade responsibility.
Neural Data Privacy
Mental privacy is a right, not a feature. Any neural data we ever touch will be collected only under informed consent, de-identified at acquisition, and never used outside the study it was gathered for.
Secured Frontier Weights
Our commitment: frontier model weights belong in hardware-isolated enclaves with strict access controls. What gets built at the frontier stays under lock until it clears the safety bar.
Contained Evaluation
Our standard: frontier models pass dangerous-capability evaluations in contained sandboxes before any external exposure. Capability ships only after oversight catches up.
How NeuroGenesys operates.
From neuron to knowledge to silicon.
Decode the Brain
The field's 4,096-electrode arrays and synapse-resolution connectomes are capturing how biological intelligence actually computes — recordings of the only general intelligence known to exist. Our field notes track every milestone, with sources.
Model the Mind
Neural data trains brain foundation models, and their principles transfer into Genesys — our research path to continual-learning world models, built on published results about forgetting, selection, and verification.
Rebuild It in Silicon
What the science teaches gets etched into hardware — eventually. First we measure: our cost-model campaigns chart exactly where analog compute and optical inference win before SynapseOne or Lumen commit to silicon.
Three programs.
One frontier.
Thought to text at conversation speed.
The field's best speech neuroprostheses now decode attempted speech at 62–78 words per minute, and arrays have reached 4,096 electrodes. Our Cortex program studies the layer that made it possible — foundation-model decoders — and what it takes for them to generalize across sessions, subjects, and arrays.
// ── THE DECODING SHIFT ──
// state of the field, sourced in our notes:
// 4,096-electrode cortical arrays (2024)
// 62–78 wpm attempted-speech decode (2023)
// pretrain-then-adapt decoders (POYO, 2023)
// open problem: cross-session generalizationThe frontier,
quantified.
Measured Analog Compute Advantage
Frozen-Expert Accuracy, Split-MNIST
Optical Precision Robustness vs Matmul
Pre-Registered Experiments
Research Output
Open Research Publications
Progress you can verify.
Results from our research programs — published openly, pre-registered where possible, and reproducible.
Class-incremental accuracy factorizes exactly
Across Split-MNIST and hardened synthetic benchmarks, accuracy equals selection accuracy times within-task accuracy to within a hundredth — reducing catastrophic forgetting to a selection problem.
More sampling can hurt — at a computable budget
With any exploitable verifier, Best-of-N accuracy peaks at a budget our selection model computes in closed form, then declines. Exploitability, not discrimination, is the ceiling.
The honest optical speedup, requoted against an ASIC
A claimed 48× all-optical advantage collapses to near parity against a fused INT8 ASIC — a negative result we published in full, alongside the two advantages that survived.
"We didn't set out to build a company. We set out to answer the oldest question in science — and discovered that the answer would need new models, new medicine, and new silicon."
Will Schulz
Founder • AW3 Technology
Access the frontier.
Fund the science.
Researcher
Academic Access
- All publications, open access
- Reproducible code with fixed seeds
- Replication & falsification support
- No affiliation required
Genesys Pro
For Builders
- Genesys early access (waitlist)
- Research previews & eval harnesses
- Direct line to the research team
- Founding pricing, cancel anytime
Institutional
Clinical & Strategic
- Joint research programs
- Sponsored studies & tooling
- Data governance by agreement
- Co-publication agreements
The latest from
the lab.
Clear answers.
No science fiction.
Six connected frontiers: superintelligence and AGI research through the Genesys model family, brain-computer interfaces, computational neuroscience and connectomics, neuromorphic computing, photonic computing, and custom AI ASICs. They form one loop — we study the brain, model what we learn, and rebuild those principles in silicon.