Constitutional Context Management (CCM)

Contemporary AI systems rely on context windows as ephemeral substrates for reasoning. These windows are finite, mutable, and opaque, leading to silent drift in meaning, loss of invariants, and unobservable degradation of system behavior. While Governed Context & Memory (GCM) establishes persistence, provenance, and hash-bound memory, it does not govern what is active at any given moment. This paper introduces Constitutional Context Management (CCM), a framework for governing working state. CCM transforms context from a transient token buffer into a typed, hash-bound, admissible set of active commitments. It defines context artifacts, context lanes, and relevance as a governed admission function. It introduces context hashing for drift detection, amendment semantics for controlled evolution, and refusal as a first-class response to context insufficiency. We formalize a separation: GCM governs persistence across time CCM governs activation in the present Together, they produce continuity without drift. We argue that reliable reasoning, long-horizon interaction, and safe agent execution require governance not only of memory, but of attention itself.