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Memory & Data Standards

Official guidelines governing knowledge persistence, versioning, verification, and archival integrity across the Aevum platform.

● Active Standard 📅 Last Updated: Nov 14, 2025 đŸ‘Ĩ Maintained by: Core Architecture Team

Overview & Purpose

This document establishes the technical and editorial standards for how knowledge is stored, versioned, verified, and preserved within the Aevum Encyclopedia ecosystem. As a platform blending human expertise with AI-driven synthesis, our memory architecture must guarantee immutability, traceability, and contextual fidelity.

All contributors, developers, and automated systems interacting with the Aevum knowledge graph must adhere to these standards to maintain platform integrity and academic rigor.

â„šī¸ Scope

This standard applies to all persistent data structures, revision histories, AI context windows, source citations, and archival exports within the Aevum platform.

Core Memory Principles

1. Immutability

Once committed, knowledge entries are never overwritten. All modifications generate new revision nodes, preserving the complete evolution of each article. This ensures historical accuracy and auditability.

2. Traceability

Every claim, data point, and conceptual link must be anchorable to a primary or verified secondary source. Memory structures include cryptographic hash chains linking assertions to their origins.

3. Context Preservation

Knowledge is never stored in isolation. Each entry maintains relational metadata, disciplinary tags, temporal markers, and cultural context to prevent decontextualized retrieval.

📜 Enforcement

Systems violating immutability or traceability requirements are automatically quarantined and flagged for editorial review before propagation.

Data Structure & Versioning

All knowledge nodes follow a standardized schema optimized for graph traversal and semantic search. The base structure includes:

node_id UUID v7
title string (localized)
content_hash SHA-256
version semver (MAJOR.MINOR.PATCH)
parent_revision UUID v7 | null
commit_timestamp ISO 8601
author_ids array[string]
confidence_score float (0.0–1.0)
relations graph_edges[]

Versioning Rules

  • MAJOR: Structural overhauls, paradigm shifts, or reclassification of core concepts
  • MINOR: Significant content additions, new verified sources, or cross-disciplinary linking
  • PATCH: Typographical corrections, metadata updates, or citation formatting
✅ Best Practice

Always include delta summaries with patch commits. Use atomic transactions for multi-node updates to maintain graph consistency.

Verification & Integrity

The Aevum verification engine operates on a multi-layered confidence scoring system. Memory nodes are continuously audited against trusted source repositories.

Confidence Tiers

  • 1.0 (Verified): Peer-reviewed, cross-referenced by â‰Ĩ3 independent sources, approved by domain experts
  • 0.8–0.9 (High): Strong source alignment, minor contextual gaps, awaiting secondary review
  • 0.5–0.7 (Moderate): Emerging topics, limited sourcing, or disputed interpretations
  • <0.5 (Low): Hypothetical, contested, or insufficiently sourced — flagged for caution
âš ī¸ Critical Rule

AI-generated synthesis must never override verified human-reviewed content. Confidence scores below 0.5 require explicit editorial sign-off before public indexing.

AI & Editorial Memory

Aevum employs a hybrid memory architecture separating transient AI context from persistent editorial knowledge.

Context Windows vs. Persistent Storage

AI agents operate within bounded context windows optimized for query synthesis. However, all persistent knowledge resides in the immutable graph database. AI may propose revisions, but only verified human reviewers or consensus algorithms commit structural changes.

// Example: AI Suggestion vs Commit Flow
ai_agent.suggest_revision(
  node_id: "aeum_8f92a1b",
  delta: {"add_section": "Quantum Decoherence", "confidence": 0.84},
  metadata: {"suggested_by": "aevum-synth-v3", "requires_review": true}
)

// Human or consensus gate must approve
editorial_board.approve(
  suggestion_id: "sug_9921",
  action: "merge",
  audit_log: true
)
â„šī¸ Transparency

All AI-assisted modifications are watermarked in the revision history. Users can toggle "AI-Assisted" filters in search results.

Archival & Preservation

To ensure long-term accessibility and resistance to platform decay, Aevum maintains multiple archival layers:

  • Hot Storage: Optimized graph database for active querying and AI synthesis
  • Cold Storage: WARC & JSON-LD exports, cryptographically signed, updated weekly
  • Offline Mirrors: Distributed across partner academic institutions and Internet Archive nodes

Preservation Standards

All archival exports conform to the OAIS reference model and include manifest files with SHA-256 checksums for every knowledge node. Metadata follows DCAT and BIBFRAME specifications for interoperability.

📜 Retention Policy

Knowledge nodes are retained indefinitely. Deprecated or superseded content is archived with is_superseded_by relations rather than deleted.

Compliance & Ethics

Memory management at Aevum adheres to global data protection frameworks while prioritizing open knowledge access.

  • GDPR-compliant contributor data handling with right-to-erasure for personal metadata (content remains under open license)
  • Zero third-party tracking in knowledge graphs
  • Bias mitigation protocols requiring cross-cultural source validation for sociopolitical entries
  • Transparent licensing: All persistent content defaults to CC BY-SA 4.0 unless restricted by jurisdictional requirements
✅ Ethical Commitment

We believe knowledge should outlast institutions. Our memory standards are designed for permanence, accessibility, and intellectual honesty.

📩 Questions or Proposals?

Contribute to this standard via the Community Governance Portal. All RFCs are reviewed monthly by the Architecture Council.