Adversarial Fragmentation as a Mechanism for Continual Learning: The Osiris‑Set‑Isis Cycle in Persistent Stateful Agents

his work introduces the Osiris‑Set‑Isis (OSI) cycle, a continual‑learning architecture for persistent stateful agents that prevents convergence stagnation through adversarial self‑perturbation.

As agents approach high predictive accuracy, the informational deficit driving adaptation collapses, causing learning to freeze. The OSI cycle resolves this by fragmenting internal state (Osiris), applying entropy‑scaled perturbations (Set), and reconstructing coherent state from corrupted fragments (Isis)

Empirical results from over 53,700 continuous operational ticks demonstrate stable emergence (≈0.50), bounded chaos (≈0.21–0.29), and autonomous stagnation detection with perturbation escalation (⚡SET+)

this architecture provides a lightweight, thermodynamically grounded mechanism for long‑running agents on commodity hardware.