Bounded Neuroplasticity: Self-Calibrating Organizational Memory Without Self-Authorizing Knowledge

Persistent organizational memory should not remain structurally frozen. As an enterprise accumulates AI-assisted work, its memory system should learn acronyms, project aliases, domain-specific meanings, retrieval patterns, workstream boundaries, compaction failures, and useful relationships while preserving the evidence behind what it knows.

That adaptability creates a reflexivity risk: a system can mistake its own analyses for new observations, count descendants of one source as independent corroboration, amplify confidence through repetition, rewrite graph structure without preserving history, or optimize a convenient metric at the expense of fidelity and access control.

This paper proposes bounded neuroplasticity: organizational memory may recalibrate retrieval, language, compaction, routing, and graph structure while identity, permissions, provenance, authority, lifecycle, derivation depth, and action remain outside self-modification. High-order reasoning returns through a Reflexive Admission Gate, and internally generated objects remain system-derived and grounded in their external evidence roots.

A Plasticity Controller turns memory telemetry into versioned proposals evaluated through historical replay, held-out cases, shadow execution, and bounded canary rollout. Telomere and human governance determine whether candidate state may acquire or retain influence, making adaptation governable, observable, and reversible rather than self-authorizing.