Peer-reviewed papers defining the physics, mathematics, and philosophy of sovereign intelligence.
This paper formalizes a simulation framework for evaluating learning systems under the constraints imposed by the Non-Archival Law of Cognition. Artificial Intelligence is employed exclusively as a simulation substrate, not as an autonomous cognitive agent, to observe learning dynamics under enforced instantaneous recomputation and irreversible informational reduction.
Within this framework, learning is defined as an irreversible transformation of a system’s present-state structure. No internal representation may function as preserved experiential content.
No internal state may act as a retrievable record, buffer, or memory trace across temporal boundaries.
All transformations must occur at Δt = 0. Learning occurs through instantaneous state shift.
Complexity must strictly decrease (dI/dt < 0). Abstraction emerges through loss of noise.
Artificial systems within this framework are not treated as cognitive entities but as controlled substrates for observing learning behavior under non-archival constraints. Assessment is performed via: