Why a physics kernel?
Modern AI often optimizes for prediction accuracy under a training distribution. Mission-critical systems often fail when the distribution shifts. Physics-based invariants are valuable because they retain meaning under scale change and context change. QEIv15™ is built to compute invariants and stability signals that can be inspected, logged, and audited—not merely “believed.”
What QEIv15™ consumes
QEIv15™ is fundamentally a signal engine: it expects a numerical sequence (or multiple coupled sequences) with a well-defined sampling logic (per second, per lap, per tick, per step). It is intentionally domain-agnostic; domain engines are responsible for preprocessing and canonicalization.
Input examples:
- EEG: channel amplitudes over time
- Climate: CO₂ concentration over months
- Markets: prices/returns over time
- LLM governance: trace telemetry sequences (margins, NLL, entropy proxies)
- ML governance: confidence/margin/drift per inference batchWhat QEIv15™ produces
QEIv15™ produces structural fields and scalars. In human terms:
- Entropy families capture intrinsic disorder and uncertainty in the signal.
- Δ-dynamics capture how structure changes, including regime transitions.
- Curvature/stiffness captures how quickly the system responds to perturbations.
- φ-field structure captures higher-dimensional organization—useful for detecting stress accumulation and phase shifts.
- λ-style stability captures divergence and instability (where small changes blow up).
Kernel vs engine: the boundary that matters
QEIv15™ is the kernel. Engines are “lenses.” The lens chooses:
- how the domain is normalized (units, detrending, filtering, sampling)
- which invariants matter most for that domain
- how outputs map into domain language (neuromarkers, regimes, envelopes)
This is not “non-physics.” It is the equivalent of unit calibration, instrument design, and controlled interpretation in classical measurement systems.
How QEIv15™ enables X-40™ governance
In X-40™, QEIv15™ is used as an independent evidence channel. The first channel reads behavioral traces (e.g., uncertainty dynamics). The second channel computes structural anchors (Φ, κ, ΔS families) over those trace sequences. This creates dual evidence: the governance decision is not hostage to a single signal family.
Where this goes next
QEIv15™ is designed to grow. As new invariants are validated and new data sources are integrated, domain engines can adopt them without rewriting the platform. This is how a physics kernel becomes a long-term deep-tech asset: a shared structural substrate across markets, biology, climate, machines, and governance.