First-class capability
Signal Integrity.
WITNESS should not only interpret evidence. It should determine how much that evidence deserves to be trusted before AI reasons from it.
The problem
A clean-looking signal can still be structurally unreliable.
AI systems can produce highly coherent conclusions from weak, manipulated, anomalous, incomplete, or contextually mismatched evidence. Signal Integrity exists to evaluate the evidence layer first: source provenance, structural consistency, timing patterns, contradictions, missingness, and other indicators that should change how strongly a signal is trusted.
Invariant Intelligence Engine
L1 — Circular invariants
Detect structural timing concentration and periodic patterns that remain meaningful even when timezone or circular-coordinate representation changes.
L2 — Hu moments
Measure the shape of joint distributions, such as time-of-day × completion depth, in ways that are resilient to ordinary geometric transformation.
L3 — Zernike moments
Capture higher-order shape information so subtle distributional patterns can be compared without reducing everything to a single average or score.
Reasoning consequence
Evidence quality changes the strength of the conclusion.
Signal Integrity is not a decorative confidence badge. Its purpose is to alter downstream reasoning: lower trust when provenance is weak, surface anomalies when structure is inconsistent, preserve conflicting evidence, and prevent an AI-generated explanation from outrunning what the underlying signal can support.
WITNESS boundary
Trust is evidence-sensitive, not model-sensitive.
A more capable model does not repair weak evidence. WITNESS treats evidence quality as an independent layer so reasoning remains answerable to what was actually observed.
The WITNESS Standard · Creator intelligence · For AI · Public knowledge map