ARIADNE: Neo4j Working Memory Specification for Cognitive AI Agents with Bi-Temporal Edges and Trust-Gradient Fact Promotion

ARIADNE is a Neo4j-based working memory specification for cognitive AI agents, designed as part of the DAEDALUS research project. The specification defines a three-tier graph schema for agent memory: Tier 1 — Episodic Memory: Episode nodes storing raw interactions (conversations, documents, API responses) with transaction-time immutability and multi-channel source tracking (WhatsApp, Telegram, Discord, CLI). Tier 2 — Semantic Memory: Entity nodes (canonical representations of people, projects, concepts) and Edge nodes (facts stored as first-class nodes rather than relationships, enabling rich metadata). Edges implement bi-temporal tracking with four temporal fields: created_at and expired_at (transaction time), valid_at and invalid_at (real-world validity time). This enables point-in-time queries such as "What did I believe about X in October?" Tier 3 — Community Memory: Leiden-clustered entity groups for global context retrieval, with hierarchical organization and LLM-generated summaries. Additional node types include Reflection nodes implementing the Reflexion pattern (Shinn et al., 2023) for failure-indexed learning with outcome tracking. Key architectural contributions: 1. Bi-temporal edge modeling distinguishing real-world fact validity from system recording time, validated by convergence analysis across three independent AI-assisted literature reviews. 2. Three-factor retrieval scoring (recency × importance × relevance) with configurable exponential decay, implemented as a complete scoring algorithm with formal mathematical specification. 3. Hybrid retrieval combining vector similarity search, BM25 keyword search, and graph traversal with Reciprocal Rank Fusion (RRF) for result merging. 4. Trust-gradient fact promotion pipeline connecting working memory to a curated knowledge layer (ATHENA) with confidence thresholds: >0.95 auto-promote, 0.85-0.95 batch review,