The central risk in long-running AI-assisted workflows is not merely forgetting, but uncontrolled remembering. Existing systems often treat memory, transcript replay, and compressed summaries as benign continuity mechanisms, yet these mechanisms can silently carry forward obsolete assumptions, unstable context, or distorted intent. The Descartes-10 Continuity Capsule Protocol (D10-CCP) proposes a governed continuity-capsule architecture that separates stable context, active state, discarded history, operator profile, and continuity invariants, allowing prior context to support resumption without silently mutating present action. This document publicly discloses the protocol’s architecture, central principle, and intended scope in order to establish authorship and priority, while expressly withholding the canonical schema, validator logic, conformance suite, domain-pack specification, implementation evaluation notes, and broader Descartes-10 governance spine.
Maxime Obongono (Tue,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: