NeuroGenesys.
Publications

What we publish, we measured.

Preprints, design rules, and negative results from our research programs — pre-registered where possible, reproducible always, and published including the experiments that failed.

Jul 27, 2026Silicon & Analog ComputeDefensive publication

Which sequence architecture keeps analog in-memory compute's advantage? A cost-model answer with a design rule

Analog in-memory compute accelerates static-weight matmuls but cannot touch attention's growing KV cache, so its decode advantage dilutes with context length. The attention window policy — sliding vs. full — not the attention fraction, decides whether a hybrid keeps the advantage. Distilled from ~70 pre-registered falsification experiments.

Jul 26, 2026Mathematics & LogicMachine-checked results

The displacement theorem, and the aspect no-go

Two small results, stated precisely and machine-checked (22/22 pre-registered predictions across the project): a pigeonhole-style displacement theorem — the metalanguage, not the primitive, is what cannot be eliminated — and a two-line model-theoretic no-go for aspect. Both are plainly labeled where they are trivial.

Jul 19, 2026MathematicsExpository note

A closed-form spectral response for balanced perturbations of positive recurrences

A tidy, fully explicit special case of the weak-noise Lyapunov-exponent expansion: perturb a positive two-term recurrence so the coefficient sum is conserved, and the growth response has a closed form — with a pleasant golden-ratio corollary. Honestly labeled a worked example, not a new theorem.

Jul 19, 2026MathematicsResearch note

Spectral response of positive recurrences under memory-filtered balanced coefficient perturbations

When an AR(1) memory process perturbs a positive two-term recurrence while conserving total coefficient weight, memory systematically accelerates growth. The main theorem gives the exact spectral response and is numerically confirmed; the proof architecture is presented as a skeleton.

Jun 27, 2026Continual LearningPreprint draft

Catastrophic forgetting in class-incremental learning is a verifier-exploitability problem

Freeze one near-perfect expert per task and all of class-incremental difficulty becomes selection — which expert to trust. Accuracy factorizes exactly as selection accuracy × within-task accuracy, and the selection term is pinned by the selector's exploitability ε, the same quantity that bounds test-time compute.

Jun 27, 2026Test-Time ComputePreprint draft

When more sampling hurts: verifier exploitability, not accuracy, bounds test-time compute

Best-of-N sampling lifts coverage log-linearly, but realized accuracy is ceilinged by the verifier — and the property that matters is not discrimination but exploitability ε: the rate at which wrong candidates score high. Any ε > 0 makes accuracy non-monotone in N, peaking at a computable budget before declining.

Jun 27, 2026PhotonicsCost-model study

Where optics-native inference actually pays: a cost-model study

Speed-of-light inference is real but narrow and conditional: an optics-native state-space model beats a GPU on batch-1 decode latency only inside a tight box — WDM microrings, small-to-mid width, ~5-bit precision, error-tolerant workloads. The model reproduces a published 2025 chip's throughput exactly; the defensible contribution is the cost model itself.

Jun 27, 2026PhotonicsFalsification study

Reverse-engineering a trained network for all-optical inference via random-feature kernels

Can inference on an already-trained network run entirely in light? An envelope exists, bounded by a quantified depth→width feature wall — and an honest requote against a digital ASIC collapses the headline speedup to near parity. Two genuine advantages survive: precision robustness and zero-forgetting frozen features.

Jun 27, 2026FoundationsNegative results

Complex amplitudes are decorative: a pre-registered falsification of the wave/quantum thesis for learning and cognition

Across continual learning, cognition, and compositional binding, the complex/wave degree of freedom is load-bearing nowhere at equal real-parameter budget. The geometric intuition survives in real-valued form; the transferable contribution is the equal-budget falsification methodology itself.

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Leadership

A word from the scientists and engineers leading the mission — on why the brain is the blueprint, and why the next decade of intelligence will be built here.

"The brain is the universe's only proof that general intelligence is possible. We treat it as the blueprint."

From cortical interfaces to photonic silicon, every program at NeuroGenesys exists to answer one question: how does intelligence arise — and how do we build it safely.

Will Schulz

Will Schulz

Founder, NeuroGenesys • AW3 Technology