We utilize Structural Causal Models (SCMs) combined with a high-speed vector layer. The vector layer identifies "Thematic States" from raw telemetry, which are then fed into our causal engine to verify directional relationships using do-calculus. This hybrid approach allows for sub-millisecond inference without losing mathematical rigor.
The ACM is a portable, JSON-based schema that defines the structural mechanisms of a specific domain (e.g., physiological vital links). It decouples the Logic from the Data, allowing an institution to audit and update their "Reasoning Engine" without re-training a massive neural network.
Every intervention suggested by ACS is indexed in a vector-based Historical Memory. When the system encounters a new state, it cross-references the current logic against past successful (and unsuccessful) interventions. This "Experience Replay" acts as a guardrail, ensuring institutional reasoning remains consistent and auditable.
By design, yes. Unlike camera-based systems, radar telemetry captures sub-millimeter chest displacement without identifying features. All processing occurs within our Edge Sidecar Connectors, meaning no PII (Personally Identifiable Information) is transmitted to the cloud.
Absolutely. We are actively seeking researchers to lead the Aura-Py Reference Implementation. We provide the hardware telemetry and the manifest spec; you lead the development of the structural kernels and signal deconvolution logic.
Curiosity is a prerequisite.
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