Abstract
Current AI governance architectures treat accountability as a collection of discrete, static artifacts: audit logs, explainability outputs, and snapshot-based compliance states. This model fails at the boundary -- the moment when a decision crosses from one system, agent, or governance layer to another.
This working paper proposes Recursive Semantic Governance (RSG), a theoretical framework that treats governance not as documentation but as the progressive propagation and stabilization of semantic state across accountability boundaries. RSG introduces five core primitives: Semantic Custody, Governance Vectors, Boundary-Trained Connectors, Recursive Boundary Alignment, and Recursive Evidentiary Governance.
The framework includes formal mathematical notation, four canonical architectural diagrams, eight governance failure mode characterizations, and a complete end-to-end walkthrough. Initial experimental validation runs inside EVIDE Governance Lab through the Epistemic Stabilization Buffer (ESB) architecture.
Keywords: AI governance, boundary engineering, recursive governance, evidentiary computing, accountability boundaries, governance vectors, transformation qualification, semantic custody
RSG Tagline
5 Core Primitives
Semantic Custody
Measurable preservation of governance-relevant meaning across accountability boundaries. Not logging -- active qualification of what survives the crossing.
Governance Vectors
Structured multi-dimensional representations of accountability state: Decision, Authority, Intervention, Threshold, Continuity, Evidentiary.
Boundary-Trained Connectors
Controlled semantic translation mechanisms between governance layers. Enforce lawful transformation -- no governance inflation, no silent authority expansion.
Recursive Boundary Alignment
Iterative stabilization cycles at every crossing. Not just verification -- progressive semantic stabilization before boundary confirmation.
Recursive Evidentiary Governance
Externally anchored, independently verifiable governance chronology. Captures the trajectory of stabilization, not only the final state.
Formal Notation (selected)
Core Mathematical Primitives
Gⁿ(t) = < Dⁿ, Aⁿ, Iⁿ, Tⁿ, Cⁿ, Eⁿ >
Governance vector at layer n, crossing time t
G(tₙ₊₁) = T(G(tₙ), Δs, Cₚ)
RSG state transformation model -- vs. traditional A = Σ(eᵢ)
Δs(Lₙ, Lₙ₊₁) > ε ⇒ SEMANTIC CUSTODY FAILURE
Semantic divergence threshold -- detectable at crossing time, not post-hoc
Cₚ = f(Sₜ, Dₛ, Aₜ) | Cₚ < 0.40 ∧ trend=improving ⇒ SYNTHETIC COHERENCE
Causal persistence score -- detects apparent recovery with unresolved boundary
8 Governance Failure Modes
Semantic Inflation
Downstream layer claims stronger authority than upstream state supports.
Authority Hallucination
Authority inferred from context rather than transmitted via explicit vector.
Synthetic Coherence
State appears recovered without having paid the expected dynamic cost.
Stabilization by Timeout
Buffer closes on timeout, inheriting convergence-level evidentiary weight without warranting it.
Continuity Collapse
Continuity vector degrades silently -- not transmitted, not recorded.
Recursive Drift Amplification
No single layer fails. Δs(L1,L3) = 0.61. The system fails globally.
False Crystallization
Governance state anchored before crossing conditions are met.
Governance Deadlock
Circular confirmation dependency blocks all boundary crossings.
Paper Structure
Table of Contents
- Introduction -- The Failure of Static Governance
- 1b. Why Current Audit Logs Fail: A Mathematical Argument A = Σ(eᵢ) vs G(tₙ₊₁) = T(G(tₙ), Δs, Cₚ)
- Architectural Overview 3 canonical diagrams
- Formal Notation and Primitives
- Governance as Semantic State Propagation
- Governance Vectors 6 types + machine-shape considerations
- Recursive Boundary Alignment stabilization dynamics, split confirmation
- Recursive Evidentiary Governance
- 7b. Boundary Taxonomy 6 boundary types: semantic, authority, evidentiary, execution, continuity, admissibility
- Governance Failure Modes 8 failure modes
- 8b. Recursive Drift Amplification Diagram Figure 4
- End-to-End Walkthrough AI insurance claim scenario with numerical calculations
- Reference Implementation: EVIDE + ESB
- 10b. RSG in the Current Ecosystem MCP, LangChain, AutoGen, EU AI Act, NIST
- Implications, Limitations, Future Research
- Conclusion
🔬 Experimental Validation: EVIDE Governance Lab
Initial experimental validation of RSG primitives is running inside EVIDE Governance Lab through the Epistemic Stabilization Buffer (ESB) -- an observational lifecycle inserted between intake and crystallization that tracks causal persistence, stability trend, and continuity state across multiple observation cycles.
The lab is open to approved researchers. Cross-layer composition tests with external L2 governance primitives are actively running.
Explore EVIDE Governance Lab →Celano, E. (2026). Recursive Semantic Governance: Preserving Accountability Across AI Boundary Transformations. Working Paper v1.0. EVIDE Governance Lab. https://app.certifywebcontent.com/docs/rsg/