Open Problems

Beyond the actuator categories listed in the architecture, a systematic mapping is needed from estimated state variables to available actuator inputs to predicted effects on state.

Who defines the boundary between neurobiological floor (cannot do executive function without adequate PFC catecholamine balance) and normative judgment (what counts as "degraded enough" for stabilization)?

Can the combination of peripheral physiology, conversational/behavioral signals, and potentially BCI data resolve the state distinctions the architecture requires?

The full system will be a collection of AI agents, adaptive controllers, and data-collecting tools. How multiple specialized agents coordinate, share state estimates, and maintain caring orientation across a distributed system is unspecified.

Without a dynamical model of how psychophysiological state responds to inputs, the observer cannot estimate state and the stabilizer cannot modulate it. Component models exist but have not been integrated or connected to peripheral measurement.

Closing the plant model gap requires a staged identification program with six phases, from minimal latent state definition through iterative validation against decision-relevant outcomes.

How does the machine maintain calibration against the human's actual values as the human grows and changes? A static value model becomes stale and potentially harmful.