Spectral-Lyapunov Annealed Training Estimator
SLATE
AI Training Methods Misbehave. We Just Solved It.
SLATE replaces the empirical heuristics underlying AI training with provable mathematical bounds — built for regulated industries, defence, medical AI, and safety-critical systems where "it usually works" is not good enough.
Open Source · UK Patent GB2612127.7 · Built on DeepSeek NSA
Three innovation streams converging to stability — move your cursor to perturb
The Problem No One Was Fixing
Almost all AI training today relies on empirical heuristics. Methods that work in practice but offer no mathematical guarantee about what could fail or when.
Loss spikes. Gradient explosions. Multi-million-pound runs that collapse on day 11, and nobody can quite say why. We watch the training curves, hope the loss does not spike, and tell ourselves it usually does not.
What would change if "usually" became "provably"?
That is the question SLATE was built to answer.
Three Innovations
Replacing Heuristics with Proofs
Each innovation replaces one empirical assumption with a mathematical guarantee. Honest about what is validated, what is demonstrated, and what is specified.
1-Lipschitz Compression
Fully ValidatedA compression operator that cannot amplify errors. Provably 1-Lipschitz — meaning no information can be distorted beyond its original magnitude as it passes through the compression layer. Zero violations confirmed.
Lyapunov-Stable Annealing Schedule
Mechanism DemonstratedAn annealing schedule that cannot pass through unstable regions of the training landscape. Grounded in Lyapunov stability theory — the same mathematics used in control engineering to guarantee a system converges and stays stable.
Information-Monotonicity Guarantee
Theoretically SpecifiedEmpirically verified across 16 compression layers with zero violations under two independent estimators. Theoretical validation is ongoing. Honest framing: specification is complete; formal proof is deferred.
Built for the Cases Where "Usually Safe" Is Not Enough
SLATE is designed for environments where training instability has regulatory, financial, or safety consequences.
From the Lab
Where Quantum Information Theory Meets AI Training
The mathematics governing SLATE's stability is the same mathematics governing sub-unitary maps in quantum information theory. The stability conditions are not borrowed metaphors — they are the same operator-theoretic structure applied to a different physical domain.
SLATE is the second public release from a longer programme at Physivitis applying these operator-theoretic methods from quantum information theory to mainstream AI. The first was TCBA, our tensor cryptographic behavioural audit for ML model security. More to come.
Honest Framing
Of SLATE's three innovations, one is fully validated, one's mechanism is demonstrated, and one is theoretically specified with validation deferred. Overclaiming costs more credibility than honest underclaiming.
1-Lipschitz Compression
Fully Validated
Lyapunov Schedule
Mechanism Demonstrated
Information-Monotonicity
Theoretically Specified
Frequently Asked Questions
Access SLATE
Open Source. Patent Filed. Ready to Use.
SLATE is openly available. Explore the code, read the paper, or get in touch to discuss integration for your regulated AI pipeline.
UK Patent GB2612127.7 · DOI Published · physivitis.tech