A self-playing journey through a SLATE training run, from selecting a training profile to a complete stability report with standards relevance and an honest at-risk contrast. It plays like a video, loops automatically, and you can pause, scrub, or use the arrow keys at any time.
Illustrative walkthrough, not the production tool
self-playing
Scene 1 of 7 · Select Training Profile
Safety-critical AI
Frontier pretraining
Long-context
Regulated deployment
Research baseline
The selected profile determines which stability guarantees apply to the run.
SLATE configuration for this run
Base method
DeepSeek NSA (open source)
INHERITED
Compression operator
Spectrally-constrained, non-amplifying
VALIDATED
Training schedule
Lyapunov-annealed, bifurcation-guarded
DEMONSTRATED
Token selection
Fisher-weighted, utility-based
PROTOTYPE
SLATE wraps your existing training loop. Each guarantee carries its current validation status, so nothing is presented as more settled than it is.
Guarantees enforced
SLATE enforces its constraints throughout training, not only as a check at the end.
Stability Report
SLATE · Illustrative example
STABLE
Stability profile
STRONG
Evidence confidence
HIGH
Error amplification
compression is provably non-amplifying
PREVENTED
Information integrity
no information created by compression
PRESERVED
Guarantee coverage
one of three innovations fully validated
PARTIAL
Validation scale
production-scale validation underway
EARLY
Guarantee ratings
Spectral compression
VALIDATED
Information monotonicity
VALIDATED
Lyapunov schedule
DEMONSTRATED
Fisher selection
PROTOTYPE
Key insight
Strongest guarantee: the compression step cannot amplify errors. Largest opportunity: proving advantage at production scale.
Every run reports what is proven, what is demonstrated, and what is still a prototype.
Standards and assurance relevance, linked to the STABLE verdict
FrameworkFocusRelevance
ISO/IEC 42001:2023
AI management systems
Documented, provable training controls support an AI management system.
NIST AI RMF 1.0
Measure and Manage
Provable stability supports the Measure and Manage functions.
EU AI Act
Article 15 robustness
Training-time robustness supports Article 15 expectations for high-risk AI.
ISO/IEC TR 24029
Neural network robustness
Supports structured assessment of neural network robustness.
UK AI principles
Safety and robustness
Aligns with the safety and robustness principle.
Provable training stability supports the assurance frameworks organisations are measured against.
Stability Report
Research baseline, spectral constraint disabled
UNSTABLE
Stability profile
WEAK
Error amplification
compression can amplify errors without bound
NOT PREVENTED
Guarantee ratings
Spectral compression
DISABLED
Information monotonicity
NOT GUARANTEED
Lyapunov schedule
N/A
Fisher selection
N/A
Key insight
Without the spectral constraint, nothing bounds error growth through the compression step. SLATE reports this plainly rather than hiding it.
ISO/IEC 42001
without provable constraints, training-time controls cannot be evidenced.
SLATE shows the difference its guarantees make, and stays honest when they are switched off.