auto-revision-epistemic-engine

ORGAN-I: Theory · organvm-i-theoria / auto-revision-epistemic-engine

ORGAN-I: Theory Python CI License

Auto-Revision Epistemic Engine v4.2

CI Coverage License: MIT Organ I Status Python

A self-governing orchestration framework that formalizes how autonomous AI/ML pipelines should audit, constrain, and justify their own execution.

The Auto-Revision Epistemic Engine (ARE) is not another pipeline runner. It is a working formalization of a question that sits at the intersection of epistemology and systems engineering: How should a computational process govern itself when its outputs carry real-world consequences?

The answer this framework proposes is structural. Eight sequential phases, four human review gates, an append-only cryptographic audit chain, and an ethical axiom framework that can block execution — not merely log warnings — when normative constraints are violated. Every state transition is hashed, every resource allocation is tracked, and every decision point where a human must intervene is explicitly modeled with SLAs and escalation chains.

This is theory made executable. The governance model is not bolted on after the fact; it is the architecture itself.

Table of Contents

Problem Statement

Most AI/ML pipeline frameworks treat governance as an afterthought — a logging layer, a compliance checkbox, a post-hoc report. The result is systems that can explain what they did but not why they were permitted to do it, and that offer no structural guarantees about human oversight, ethical constraint enforcement, or auditability under adversarial conditions.

The gap is epistemic. When a pipeline processes data through ingestion, analysis, synthesis, and finalization, each transition represents an epistemic commitment: the system asserts that the prior phase’s output is valid input for the next. But who or what validates that assertion? Under what constraints? With what recourse if the assertion is wrong?

The Auto-Revision Epistemic Engine addresses this by making governance a first-class architectural concern:

  1. The oversight problem: Autonomous systems need structured human intervention points, not ad-hoc monitoring. ARE formalizes four Human Review Gates with SLAs, escalation chains, and timeout policies — making oversight a contractual obligation rather than a cultural norm.

  2. The auditability problem: Logs can be edited; databases can be rewritten. ARE uses a BLAKE3-hashed append-only audit chain where each entry references the hash of its predecessor. Tampering with any record invalidates the entire chain, and this is cryptographically verifiable.

  3. The ethics problem: Ethical constraints in ML systems are typically advisory. ARE’s Axiom Framework assigns enforcement levels — BLOCK, WARN, or LOG — to each axiom. A fairness violation configured at BLOCK level will halt pipeline execution, not merely emit a warning.

  4. The reproducibility problem: Non-deterministic ML pipelines produce results that cannot be independently verified. ARE pins model versions, manages random seeds per phase, and produces BLAKE3-hashed immutable state snapshots that allow exact reproduction of any pipeline run.

These are not feature requests. They are structural requirements for any system that claims to operate responsibly in high-stakes domains — healthcare, finance, criminal justice, public policy.

Core Concepts

1. The Eight-Phase Sequential Pipeline

ARE models computation as an eight-phase state machine where each phase has explicit preconditions, postconditions, and status tracking (PENDING, RUNNING, COMPLETED, FAILED, BLOCKED, SKIPPED):

Phase 1: INGESTION        ──→ [HRG Gate 1] ──→
Phase 2: PREPROCESSING    ──→
Phase 3: PROCESSING       ──→ [HRG Gate 2] ──→
Phase 4: ANALYSIS         ──→
Phase 5: VALIDATION       ──→ [HRG Gate 3] ──→
Phase 6: SYNTHESIS         ──→
Phase 7: REVIEW           ──→
Phase 8: FINALIZATION     ──→ [HRG Gate 4]

The sequential constraint is intentional. Parallelism introduces non-determinism; non-determinism undermines reproducibility; unreproducible systems cannot be audited. ARE trades throughput for epistemic guarantees. Each phase transition is logged, hashed, and subject to ethical axiom evaluation.

Execution metrics (duration, resource consumption, status) are captured per phase, enabling both retrospective analysis and real-time governance decisions.

2. Human Review Gates (HRGs)

The HRG subsystem formalizes human-in-the-loop oversight as a protocol, not a suggestion. Four gates are positioned at strategic inflection points in the pipeline:

Each gate operates under configurable SLAs:

The escalation chain follows a four-level hierarchy — Team Lead, Manager, Director, Executive — ensuring that no gate can be silently ignored. If all four levels fail to respond within their SLAs, the pipeline blocks rather than proceeding without oversight.

This is the critical design decision: the system fails closed, not open. An unreviewed gate does not auto-approve; it halts execution.

3. BLAKE3 Cryptographic Audit Chain

The audit subsystem implements an append-only log where each entry contains the BLAKE3 hash of the previous entry, forming a cryptographic chain analogous to a simplified blockchain:

Entry N: { event, timestamp, data, hash(Entry N-1) }  →  hash(Entry N)
Entry N+1: { event, timestamp, data, hash(Entry N) }  →  hash(Entry N+1)

BLAKE3 was chosen over SHA-256 for its superior performance on modern hardware (particularly SIMD-optimized architectures) while maintaining cryptographic security. The chain provides:

This is not defensive programming — it is an epistemic commitment. The audit chain asserts: this is exactly what happened, in exactly this order, and you can prove it.

4. The Axiom Framework (Ethical Governance)

ARE ships with eight default ethical axioms drawn from established bioethics and AI ethics literature:

Axiom Enforcement Domain
Fairness BLOCK Bias detection and mitigation
Transparency WARN Explainability of decisions
Accountability BLOCK Clear responsibility chains
Privacy BLOCK Data protection and minimization
Safety BLOCK Harm prevention
Beneficence WARN Positive outcome optimization
Non-maleficence BLOCK Harm avoidance
Autonomy LOG Respect for human agency

Axioms are evaluated at two points: pre-phase (normative audit before execution begins) and post-phase (normative audit after execution completes). An axiom configured at BLOCK enforcement level will halt the pipeline if its evaluation fails. WARN axioms log violations and continue. LOG axioms record the evaluation for audit purposes only.

The framework supports meta-commentary — reflexive observations about the ethical evaluation process itself. This enables the system to flag situations where axiom conflicts arise (e.g., transparency requirements conflicting with privacy constraints) rather than silently resolving them.

Custom axioms can be added at runtime via add_ethical_axiom(), enabling domain-specific ethical constraints without modifying the framework.

5. ROL-T Resource Optimization

The Resource Optimization Layer-Tracking (ROL-T) subsystem monitors six resource categories across the pipeline lifecycle:

ROL-T tracks allocation versus actual usage per phase, calculates waste percentages and efficiency ratios, and feeds these metrics into governance assessments. The inclusion of “human time” as a tracked resource is deliberate — it treats human attention as the scarce, non-renewable resource it is, and ensures that HRG SLAs are informed by actual review durations.

6. Reproducibility Through Immutable State

The State Manager subsystem ensures that any pipeline run can be exactly reproduced:

ARE draws on and extends ideas from several domains:

Installation & Usage

Requirements

Install

# Clone the repository
git clone https://github.com/organvm-i-theoria/auto-revision-epistemic-engine.git
cd auto-revision-epistemic-engine

# Install with dependencies
pip install -e .

Dependencies

blake3>=0.4.1
pydantic>=2.0.0
pyyaml>=6.0
python-dateutil>=2.8.2

Quick Start

from auto_revision_engine import AutoRevisionEngine

# Initialize the engine
engine = AutoRevisionEngine()

# Pin a model version for reproducibility
engine.pin_model("classifier-v2", version="2.1.0")

# Add a domain-specific ethical axiom
engine.add_ethical_axiom(
    name="DataSovereignty",
    description="Data must not leave its jurisdiction of origin",
    enforcement="BLOCK"
)

# Execute the full 8-phase pipeline
result = engine.execute()

# Inspect results
print(engine.get_status())              # Phase-by-phase status
print(engine.get_audit_trail())          # Full cryptographic audit chain
print(engine.get_reproducibility_info()) # Seeds, versions, snapshots
print(engine.get_resource_report())      # ROL-T resource utilization
print(engine.get_ethics_report())        # Axiom evaluations per phase
print(engine.get_hrg_report())           # Human review gate outcomes

Examples

Configuring HRG Escalation

from auto_revision_engine import AutoRevisionEngine

engine = AutoRevisionEngine(
    hrg_config={
        "response_sla_hours": 2,       # Tighter response window
        "resolution_sla_hours": 12,    # Faster resolution required
        "auto_escalation_hours": 4,    # Escalate sooner
        "escalation_chain": [
            "team-lead@org.com",
            "manager@org.com",
            "director@org.com",
            "executive@org.com"
        ]
    }
)

Verifying Audit Chain Integrity

# After pipeline execution, verify no tampering occurred
trail = engine.get_audit_trail()
is_valid = trail.verify_chain()

if not is_valid:
    raise SecurityError("Audit chain integrity compromised")

# Generate compliance attestation document
attestation = trail.generate_attestation()

Reproducing a Previous Run

# Load a state snapshot from a previous run
engine = AutoRevisionEngine()
engine.restore_snapshot("/path/to/snapshot-phase-3.blake3")

# Re-execute from phase 4 onward with identical seeds and model versions
result = engine.execute(from_phase=4)

Downstream Implementation

ARE provides the theoretical governance layer for ORGAN-I. Downstream organs consume its patterns:

The flow direction is strict: I → II → III. ARE provides primitives; downstream organs instantiate them for their domains. ARE never imports from downstream.

Validation

Test Suite

The repository includes 16 unit tests covering:

CI Pipeline

GitHub Actions runs on every push and pull request:

# Run tests locally
python -m pytest tests/ -v --cov=auto_revision_engine

Governance Documents

Roadmap

Priority Item Status
1 Async phase execution (opt-in, with determinism guarantees) Planned
2 Plugin system for custom phase implementations Planned
3 Web dashboard for HRG review workflows Planned
4 Integration with MLflow for experiment tracking Planned
5 Formal verification of phase transition invariants Research
6 Multi-pipeline orchestration with shared audit chains Research

Cross-References

Within ORGAN-I (Theory)

Across Organs

External

Contributing

Contributions are welcome. This is a theory-first project — code changes should be grounded in clearly articulated governance or epistemic principles.

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/your-feature)
  3. Ensure all 16 tests pass (python -m pytest tests/ -v)
  4. Write tests for new functionality
  5. Submit a pull request with a clear description of the theoretical motivation

See CONTRIBUTING.md if available, or open an issue to discuss proposed changes.

License

MIT License. See LICENSE for full text.

Author & Contact

@4444J99

This repository is part of ORGAN-I: Theory (organvm-i-theoria) — the epistemological foundation of the eight-organ creative-institutional system.

For questions about the governance model, ethical axiom framework, or integration with downstream organs, open an issue or reach out via the org profile.

Portfolio · System Directory · ORGAN I · Theoria · Part of the ORGANVM eight-organ system

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