Community Registry

Modern AI systems evolve continuously through retraining, fine-tuning, policy revision, memory editing, tool replacement, and changes in operational context. These changes create an operational version of the Ship of Theseus problem: when should an updated system still count as the same governed entity, and when does it fracture into a categorically new one? This paper introduces the two-layer Intent-Aware Continuity Framework. The first layer models observable system behavior as a trajectory in a metric space over architecture, weights, policy, context or memory, and tools. This Observable Configuration State Space supports the precise measurement of Configuration Drift, stability monitoring, repair analysis, and controlled amendment of the system baseline. The second layer introduces a typed continuity judgment that evaluates persistence relative to an entity kind and decision context. It utilizes hard invariants, cryptographically authenticated lineage (e.g., C2PA manifests), branch structure, and governance constraints to adjudicate identity. We mathematically demonstrate that scalar configuration distance alone is insufficient as a universal identity criterion, whereas the combined framework yields context-sensitive continuity judgments of SAME, REVIEW, or NEW. The resulting architecture translates abstract identity disputes into an operational, provenance-aware governance framework for enterprise AI lineage tracking, liability transfer, and intent-aware autonomous agent verification.

Volume III is the closure and safeguarding foundation of the Ethical AI Series — Conservation of Intent trilogy. Building on the doctrinal foundations of Volume I and the ontological foundations of Volume II, it defines the limits required to preserve the trilogy’s lawful meaning under pressure. It formalizes saturation points, overclaim taxonomies, misread safeguards, failure versus refusal distinctions, citation discipline, semantic altitude irreversibility, verification-register limits, canon-usage constraints, and controlled revision boundaries. The purpose of this volume is to protect the trilogy against interpretive laundering, implementation overreach, and unauthorized expansion of claim-scope. It makes explicit how lawful closure operates when readers attempt to convert doctrinal or ontological authority into mechanism, mandate, enforcement, prediction, or inference claims. As the third foundational volume, it completes the trilogy by securing its limits, preserving refusal terminality, and ensuring cross-volume coherence, interpretive discipline, and canon integrity.

Volume II is the ontological foundation of the Ethical AI Series — Conservation of Intent trilogy. Building directly on the doctrinal architecture established in Volume I, it formalizes the intent-domain as a non-operational representational object and defines the authorized ontological relations required to reason about transformation without collapsing into implementation. These include representation classes, admissibility conditions, domain equivalence, interpretation, transformation space, drift relations, drift bounds, and trace-representability structures. This volume clarifies how lawful classification can be extended into a rigorous ontological framework while preserving the trilogy’s non-operational and classification-only posture. It explicitly rejects mechanism capture, interface capture, enforcement capture, and inference capture, and it preserves refusal as a terminal boundary rather than a recoverable implementation state. As the second foundational volume in the trilogy, it supplies the formal ontology necessary to make doctrine structurally usable without permitting unauthorized operationalization.

Volume I is the doctrinal foundation of the Ethical AI Series — Conservation of Intent trilogy. It establishes the law-altitude basis of the framework by defining the governing primitives of intent, intent-domain, transformation, conserving versus non-conserving classification, trace representability, semantic altitude irreversibility, and refusal as a terminal jurisdictional boundary. From these foundations, the volume formalizes the trilogy’s axioms, invariants, admissibility conditions, and conformance clauses, creating the canonical doctrinal base upon which the later ontology and closure volumes depend. The volume is explicitly classification-only and non-operational. It does not authorize mechanisms, interfaces, enforcement schemas, behavioral inference, identity inference, mental-state inference, or outcome guarantees. Its purpose is to preserve lawful interpretive structure, prevent semantic overreach, and stabilize governance reasoning under pressure. As the first volume in the trilogy, it provides the primary doctrinal anchor for all later formalization, ensuring that subsequent developments remain constrained by admissibility, lawful scope, and refusal integrity.

This manuscript is Volume IV of The Unified Field Theory of Autonomous Governance , a series formalizing the thermodynamic constraints of artificial intelligence. This manuscript presents a formal framework for trace-representable governance in autonomous agent systems. The paper defines agent constitutions as explicit constraint sets over admissible executions, introduces a secure bilateral handshake protocol for pre-commit validation, and specifies safe refusal semantics as a first-class terminal outcome rather than an exception path. The framework is designed to support policy-constrained coordination, provenance-preserving commitments, and auditable refusal under explicit assumptions about authentication, policy evaluation, and trace observability. Version 2 substantially revises and matures the earlier manuscript by narrowing the contribution to a refereeable protocol paper, replacing broader manifesto-style framing with a formal model, protocol state machine, theorem-style guarantees, and a prototype evaluation design. The manuscript also expands its related-work positioning across formal methods, secure protocols, runtime verification, and multi-agent governance. This Zenodo record contains the Version 2 manuscript source and compiled manuscript files for archival citation and versioned reference.

This manuscript is Volume II of The Unified Field Theory of Autonomous Governance , a series formalizing the thermodynamic constraints of artificial intelligence. THE THERMODYNAMIC COST OF TRUTH. If Volume I established the physics of control, Volume II audits the financial viability of that control. We posit that governance is not merely a compliance function but a currency exchange mechanism. Every autonomous action requires a thermodynamic expenditure of work (W_Gov) to align the system with human intent. Consequently, alignment has a non-zero financial cost. This manuscript reclassifies "Hallucinations" as counterfeit currency and "Drift" as compounding technical debt. We demonstrate that the current "Zero-Shot" governance model is thermodynamically equivalent to counterfeiting, leading inevitably to an Insolvency Horizon where the cost of verification exceeds the value of the token generated. We introduce the Solvency Index (S_Idx) and the Governance Processing Unit (GPU-G) as the necessary instruments to prevent the bankruptcy of the autonomous enterprise. By defining Governance as a measurable thermodynamic unit of work (W_Gov), this manuscript formally bridges Information Theory and Microeconomics, providing the first rigorous mathematical proof that Zero-Shot Governance is functionally insolvent.

This manuscript is Volume III of The Unified Field Theory of Autonomous Governance , a series formalizing the thermodynamic constraints of artificial intelligence. If Volume II established the Micro-Economics of the individual agent, Volume III establishes the Macro-Topology of the swarm. In distributed autonomous systems, "Alignment" is not a property of the node; it is a property of the lattice. We demonstrate that a network of "Solvent" agents (S_Idx > 1) can still produce a systemic hallucination (The "Flash Crash of Truth") if the topology of their consensus mechanism allows for the amplification of entropy (dS > 0). This manuscript introduces the Governance Processing Unit (GPU-G) , a theoretical hardware architecture that physically separates Generation (The Compute Die) from Verification (The Governance Die). By embedding the Constitution (G_E) into a silicon photonic lattice, we achieve "Speed-of-Light Governance," eliminating the latency tax of software-based alignment. We further define the Consensus Equation (C_Sys), proving that governance mass must scale quadratically (N²) with network size to prevent topological collapse. Volume III transitions the series from the Transactional to the Structural , defining the physical geometry required to house the Sovereign Cloud.

[GOLD MASTER] - DEFINITIVE EDITION v4.0 This manuscript establishes the Unified Field Theory of Autonomous Governance , creating a physics-based framework for the safety engineering of high-dimensional artificial agents. It argues that governance is not a policy challenge, but a thermodynamic one. Core Axioms Derived: The Conservation of Governance: (GT = GE + GI) proving that in the absence of Explicit Governance, a system does not become free; it becomes wholly governed by Implicit Drift. The Horizon Limit: Proving that information leakage is inevitable when Observability exceeds Authority (O > A). The Jeans Instability of Context: Demonstrating the quadratic collapse of identity in massive context windows. The Gibbs Governance Criterion: A phase diagram separating stable alignment from spontaneous hallucination. Note on Versioning: This artifact is the Gold Master consolidation of the project. It supersedes and deprecates all previous "Draft" or "Beta" uploads related to Volume I or The Principia . It contains the fully hydrated mathematical proofs for the "Physics of Governance" appendix.