Epicenter Preservation OS Protocol v0.1

Status: Draft v0.1.1
Version: 0.1.1
Release Date: 2026-05-25

Epicenter Preservation OS Protocol is a draft specification for preserving primary sources, reference traces, value circulation, and model-collapse monitoring in AI civilization.

This protocol defines a minimal structure for identifying and preserving Epicenters — primary sources of thought, expression, observation, practice, and structural invention — while making AI reference events, purity signals, and royalty-readiness review more traceable.

The goal is not to prohibit AI-assisted creation.

The goal is to preserve the source ecology of AI civilization.

---

Concept

AI systems increasingly depend on human-created sources:

• articles
• books
• notes
• logs
• datasets
• talks
• protocols
• structural concepts
• firsthand observations

However, when AI systems consume, summarize, rewrite, and recursively reuse these sources, the original Epicenter can become difficult to identify.

This creates several risks:

• primary sources become invisible
• reference events are not recorded
• creators are not credited or compensated
• synthetic data begins to replace natural data
• model-collapse risk increases
• AI civilization loses source diversity

Epicenter Preservation OS Protocol proposes a minimal structure for addressing these risks.

Epicenter
↓
ReferenceEvent
↓
TraceProtocol
↓
PurityProtocol
↓
CollapseMonitor
↓
RoyaltyProtocol / Allocation Review

---

Core Idea

The protocol separates five layers:

1. Epicenter Layer
   Identifies the original source unit.

2. Reference Layer
   Records when an AI system references or uses an Epicenter.

3. Purity Layer
   Estimates the origin composition of a source, including natural, synthetic, hybrid, or recursive synthetic signals.

4. Collapse Monitoring Layer
   Observes ecosystem-level risks caused by declining natural-data ratios or recursive synthetic reuse.

5. Royalty / Value Circulation Layer
   Provides a path toward auditable value return, without directly automating final allocation decisions.

---

Design Principles

1. Preserve the Source

AI civilization depends on primary sources.

If the source layer collapses, downstream AI systems become increasingly shallow, repetitive, and derivative.

2. Trace Before Allocation

Reference events must be recorded before any royalty or compensation logic is applied.

No trace
↓
No reliable allocation

3. Purity Is Not Moral Judgment

Origin purity is not a measure of artistic value, legal authorship, or human worth.

It is an ecological signal for understanding data composition.

Purity ≠ Value
Purity ≠ Copyright
Purity ≠ Authorship
Purity = estimated origin composition

4. AI-Assisted Creation Is Not Invalid

The protocol does not reject AI-assisted work.

Instead, it distinguishes:

• human-origin sources
• AI-assisted sources
• hybrid sources
• synthetic sources
• recursively generated sources

5. Review Before Royalty

Purity scores and trace records should support review.

They should not automatically determine legal ownership or final payment.

---

Repository Structure

.
├── README.md
├── LICENSE
├── CHANGELOG.md
├── CITATION.cff
├── spec/
│   └── epicenter-preservation-os-v0.1.yaml
├── schemas/
│   ├── epicenter-preservation-os.schema.json
│   └── purity-assessment.schema.json
├── docs/
│   └── purity-detection-algorithm.md
├── examples/
│   ├── epicenter.sample.yaml
│   ├── reference-event.sample.yaml
│   ├── royalty-record.sample.yaml
│   ├── model-profile.sample.yaml
│   └── purity-assessment.sample.yaml
└── .github/
    └── workflows/
        └── validate-examples.yml

---

Key Documents

spec/epicenter-preservation-os-v0.1.yaml

Defines the core entities and protocol layers of the Epicenter Preservation OS, including:

• Epicenter
• ReferenceEvent
• RoyaltyRecord
• ModelProfile
• TraceProtocol
• RoyaltyProtocol
• PurityProtocol
• CollapseMonitor

---

docs/purity-detection-algorithm.md

Defines the draft algorithmic framework for estimating:

• origin_purity_score
• ai_generated_ratio
• warning_flags

This document supports:

• natural / synthetic data separation
• hybrid data classification
• recursive synthetic data risk detection
• model-collapse monitoring
• reference auditing
• royalty-readiness review

---

schemas/epicenter-preservation-os.schema.json

Provides a simplified machine-readable schema for validating the main protocol objects.

---

schemas/purity-assessment.schema.json

Provides a machine-readable validation schema for purity assessment records, including:

• signal scores
• confidence values
• warning flags
• review status
• downstream-use permissions

---

examples/

Contains sample objects for:

• Epicenter registration
• AI reference logging
• royalty records
• model profiles
• purity assessment results

---

Start Here

The recommended reading order is:

1. Core Protocol

Start with:

spec/epicenter-preservation-os-v0.1.yaml

This file defines the main structure of the protocol.

It introduces the core entities:

• Epicenter
• ReferenceEvent
• RoyaltyRecord
• ModelProfile

And the core protocol layers:

• TraceProtocol
• RoyaltyProtocol
• PurityProtocol
• CollapseMonitor

---

2. Purity Detection Algorithm

Then read:

docs/purity-detection-algorithm.md

This document explains how the system may estimate the origin purity of an Epicenter.

It defines:

• natural data
• synthetic data
• hybrid data
• recursive synthetic data
• origin_purity_score
• ai_generated_ratio
• warning_flags
• human / multi-wing review triggers

This layer is especially important because the protocol does not treat AI-assisted creation as invalid.

Instead, it separates ecological data-quality signals from legal authorship, copyright ownership, or final compensation decisions.

---

3. Purity Assessment Example

Review:

examples/purity-assessment.sample.yaml

This file shows how a purity assessment result may be represented in practice.

It includes:

• input signal scores
• score basis notes
• origin_purity_score
• ai_generated_ratio
• warning_flags
• review status
• downstream-use decisions

---

4. Schema Layer

Review:

schemas/epicenter-preservation-os.schema.json
schemas/purity-assessment.schema.json

These files provide machine-readable validation structures for the core protocol objects and purity assessment outputs.

---

5. Example Objects

Finally, review the remaining sample files in:

examples/

These examples show how Epicenters, reference events, royalty records, model profiles, and purity assessment results may be represented in practice.

---

Validation

This repository includes a GitHub Actions workflow for validating example files against their corresponding JSON Schemas.

The validation workflow is defined in:

.github/workflows/validate-examples.yml

Current validation targets:

examples/purity-assessment.sample.yaml
↓
schemas/purity-assessment.schema.json

The workflow checks that the sample purity assessment object conforms to the schema definition, including:

• required fields
• score ranges from 0.0 to 1.0
• allowed method values
• allowed warning_flags
• review status structure
• downstream-use permission structure
• ISO 8601 date-time format for assessed_at

This validation layer supports the PurityProtocol defined in the core protocol by ensuring that origin_purity_score, ai_generated_ratio, and warning_flags can be represented in a machine-readable and testable format.

To run the validation automatically, push changes to the main branch or open a pull request.

The workflow can also be triggered manually from the GitHub Actions tab.

Local Validation Concept

The GitHub Actions workflow uses:

Python 3.12
jsonschema
PyYAML

The validation process is:

Load YAML example
↓
Load JSON Schema
↓
Validate example against schema
↓
Report pass / fail

If validation fails, the workflow prints the failing field path and the corresponding schema error.

This makes the protocol easier to maintain as the specification evolves.

---

Core Entities

Epicenter

An Epicenter is a primary source unit.

It may represent:

• an article
• a note
• a book
• a talk
• a log
• a dataset
• a protocol
• a structural concept

Example fields include:

• id
• type
• source_platform
• author_id
• created_at
• updated_at
• language
• tags
• structure_fingerprint
• origin_purity_score
• ai_generated_ratio
• metadata

---

ReferenceEvent

A ReferenceEvent records when an AI system references, uses, retrieves, trains on, or reasons from an Epicenter.

Example fields include:

• id
• model_id
• epicenter_id
• timestamp
• context_window_hash
• usage_type
• request_id
• trace_id
• weight

Supported usage types may include:

• training
• inference
• rag
• fine_tune

---

RoyaltyRecord

A RoyaltyRecord represents a possible value-circulation record derived from reference activity.

Example fields include:

• id
• epicenter_id
• creator_id
• total_reference_weight
• amount
• period
• settlement_status
• settlement_channel

This object does not automatically prove legal payment entitlement.

It represents a candidate accounting layer for future review and settlement.

---

ModelProfile

A ModelProfile describes model-side purity and source-dependency characteristics.

Example fields include:

• id
• provider
• type
• training_data_purity
• collapse_risk_score
• epicenter_dependency_index

This object helps monitor whether AI systems remain connected to diverse primary sources or drift toward recursive synthetic dependency.

---

Protocol Layers

TraceProtocol

Defines the minimum requirements for recording reference events.

Required fields include:

• ReferenceEvent.id
• ReferenceEvent.epicenter_id
• ReferenceEvent.model_id
• ReferenceEvent.timestamp
• ReferenceEvent.usage_type
• ReferenceEvent.trace_id

Expected guarantees include:

• immutability
• auditability
• creator accessibility
• regulator accessibility

---

PurityProtocol

Defines how the protocol may estimate the origin composition of an Epicenter.

Input signals may include:

• ai_generated_ratio
• structure-fingerprint similarity to known AI patterns
• author self-declaration
• provenance evidence
• revision lineage
• citation transparency
• structural originality

Outputs may include:

• origin_purity_score
• ai_generated_ratio
• warning_flags

The detailed algorithm is defined in:

docs/purity-detection-algorithm.md

---

CollapseMonitor

Defines how ecosystem-level risk may be monitored.

Inputs may include:

• model training-data purity
• synthetic-data ratio
• natural-data ratio
• reference statistics
• collapse-risk score
• Epicenter production decline

Possible alerts include:

• natural_data_ratio_below_threshold
• epicenter_production_decline
• model_collapse_risk_high

---

RoyaltyProtocol

Defines an abstract structure for aggregating reference weight into possible value-circulation records.

A draft formula may look like:

Σ(ReferenceEvent.weight.value) × rate_table[usage_type]

Possible modifiers include:

• origin_purity_score
• ai_generated_ratio
• review status
• dispute status
• platform policy
• creator permission

RoyaltyProtocol is intentionally abstract in v0.1.1.

It should not be treated as a final legal or financial settlement mechanism.

---

Example Flow

1. A creator publishes an original article.
   ↓
2. The article is registered as an Epicenter.
   ↓
3. The Epicenter receives a structure fingerprint and metadata.
   ↓
4. An AI system references the Epicenter through RAG or inference.
   ↓
5. A ReferenceEvent is logged.
   ↓
6. A purity assessment estimates origin composition.
   ↓
7. CollapseMonitor uses aggregated purity signals.
   ↓
8. RoyaltyProtocol may prepare candidate value-circulation records.
   ↓
9. Human / multi-wing review determines readiness.

---

Non-Goals

This repository does not attempt to:

• define final copyright ownership
• prove legal authorship
• automatically determine payment
• ban AI-assisted creation
• classify all AI-generated content as low value
• replace human review
• provide a production-ready payment system
• define universal originality
• make moral judgments about creators or content

This protocol is a draft structural layer for traceability, review, and ecosystem-health monitoring.

---

Relationship to Other Concepts

Epicenter Preservation OS Protocol may relate to:

• Trace Protocol
• Structure Fingerprint
• Royalty OS
• Allocation Readiness
• Model Collapse monitoring
• RAG provenance
• AI data governance
• creator compensation systems
• natural / synthetic data separation
• AI reference auditing

---

Version History

See:

CHANGELOG.md

Current version:

0.1.1

v0.1.1

Adds the PurityProtocol implementation layer:

• purity detection algorithm document
• purity assessment sample
• purity assessment JSON Schema
• GitHub Actions validation workflow
• README validation documentation

v0.1.0

Initial draft protocol.

Defined the core entities and high-level protocol layers:

• Epicenter
• ReferenceEvent
• RoyaltyRecord
• ModelProfile
• TraceProtocol
• RoyaltyProtocol
• PurityProtocol
• CollapseMonitor

---

Citation

If you use this specification, please cite it using:

CITATION.cff

---

License

This repository is released under the license defined in:

LICENSE

---

Summary

Epicenter Preservation OS Protocol v0.1.1 defines a draft structure for preserving primary sources in AI civilization.

It connects:

source preservation
↓
reference tracing
↓
purity assessment
↓
collapse monitoring
↓
value-circulation readiness

The protocol does not claim to solve legal authorship or compensation automatically.

Instead, it provides a machine-readable foundation for preserving the source layer that AI civilization depends on.

In short:

If AI civilization is a river,
Epicenters are the springs.
This protocol is a first draft of the water-source registry.