Recovery as Structural Property

Recovery as Structural Property proposes a structural theory of restoration in adaptive multi-agent AI systems. The paper argues that recovery cannot be defined merely as performance improvement or error reduction, but must instead be characterized by the restoration of system-level adaptive capacity. The framework introduces operational criteria for determining when recovery is complete, distinguishing transient stabilization from genuine structural restoration. Recovery is modeled as a transition in system dynamics in which exploratory diversity, feedback sensitivity, and self-correction capacity are re-established following periods of instability or metric lock-in. By defining recovery as an observable structural property rather than an external intervention outcome, the theory provides measurable indicators applicable to large-scale intelligent systems and distributed AI environments. This paper complements Self-Consistent Misalignment: Why Intelligent Systems Fail Silently Under Metric Lock-In, which characterizes the generative mechanisms of silent systemic failure. Together, the two works establish a failure–recovery framework within the Deficit-Fractal Governance (DFG) research program.