RT1RecoveryₐsStructuralProperty

Recovery as Structural Property proposes a structural theory of restoration in adaptive multi-agent AI systems. The paper argues that recovery cannot be adequately defined as performance improvement or error reduction, but must instead be understood as 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 dynamical transition in which exploratory diversity, feedback sensitivity, and self-correction capacity are re-established following instability, coordination failure, or metric lock-in. By defining recovery as an observable structural property rather than the outcome of external intervention, the theory provides measurable indicators applicable to large-scale intelligent and distributed AI systems. These indicators allow recovery processes to be diagnosed independently of task-specific performance metrics. This work complements Self-Consistent Misalignment: Why Intelligent Systems Fail Silently Under Metric Lock-In, which characterizes mechanisms of silent systemic failure. Together, the two papers establish the failure–recovery foundation of the Deficit-Fractal Governance (DFG) research program.