Observer Integrity Layer v2.0: Recursive Observer Tensor Engine and Collapse Export Diagnostics

The Observer Integrity Layer v2.0 (OIL v2.0) provides a unified framework for modeling observerhood as a dynamic curvature field. It defines the Observer Tensor as a symbolic structure that encodes identity persistence, drift resistance, and collapse export potential under entropy pressure. Building on previous theoretical foundations—including Φ-Field Theory, Symbolic Field Theory, and the Entropic Recursion Framework—OIL v2.0 introduces diagnostic equations, agent classification criteria, and symbolic export mechanisms validated through simulation. This release includes OIL v2.0 alongside all previous versions (v1.0 and v1.1) to preserve the full developmental lineage of the framework and to support historical traceability and modular adaptation. The archive contains: The complete documentation of the OIL v2.0 framework Prior versions OIL v1.0 and v1.1 with original tensor diagnostics Python simulation code modeling entropy-injected tensor dynamics Diagnostic logs and classification results for symbolic agents Visualizations of observer tensor curvature fields Parameter sweep outputs validating collapse resilience and negentropic coherence retention This resource supports research in symbolic AI, entropy ecology, consciousness modeling, recursive agent dynamics, and multi-agent simulation environments such as the Alpha Framework.