Whitepaper / 04

SETPOINT Runtime

A whitepaper on converting DT4H Twin intelligence and StateK computation into bounded execution loops.

Summary #

SETPOINT is the execution runtime for DT4H intelligence. DT4H builds and calibrates the Twin. StateK computes current state, confidence, trajectory, and readiness. SETPOINT turns bounded state into protocols, practices, check-ins, outcomes, and recalibration loops.

This whitepaper explains why execution feedback matters. Without an execution layer, health intelligence remains passive. SETPOINT closes the loop by making outcomes observable and feeding them back into DT4H calibration.

Runtime diagram #

DT4HTwin Intelligence

Cohorts · priors · calibration · confidence

STATEKState Handoff

Readiness · transition · trajectory · uncertainty

SETPOINTExecution Runtime

Protocols · practices · check-ins · outcomes

FEEDBACKRecalibration

Outcomes · learning · Twin update

Runtime components #

01

Protocol

Structured execution logic selected from bounded state and context.

02

Practice

Executable session or behavior that produces measurable feedback.

03

Outcome

Observed response used to update confidence and calibration.

04

Recalibration

Feedback becomes evidence for future Twin refinement.

LayerResearch Posture
StatusActive Draft
SystemDT4H / StateK / SETPOINT
BoundaryInfrastructure, not diagnosis
System lineageDT4HTwinStateKSETPOINTOutcomesRecalibration
Infrastructure boundaryDT4H models cohorts, Twins, calibration, and runtime state. It does not diagnose, prescribe, or replace licensed clinical judgment.
Document statusInfrastructure draft
Last updatedMay 2026
Applies toDT4H.ai / AvatarK.ai ecosystem