LLM Wiki v2 Upgrades AI Knowledge Management With Decaying Confidence Scores, Graph Search, and Self-Healing Automation

LLM Wiki v2 revolutionizes AI knowledge management by introducing decaying confidence scores, a typed knowledge graph, hybrid search, and self-healing automation that keeps knowledge bases accurate, scalable, and production-ready with minimal human oversight.

• A new LLM Wiki v2 pattern extends Karpathy's original concept by introducing a memory lifecycle system, where every fact carries a confidence score that decays over time and strengthens with reinforcement, preventing knowledge bases from becoming outdated or noisy.
• The updated framework moves beyond flat wiki pages by implementing a typed knowledge graph with entity extraction and relationship typing, while also introducing hybrid search combining BM25, vector search, and graph traversal to handle knowledge bases that scale beyond a few hundred pages.
• Automation hooks now handle ingestion, linting, and context injection automatically, while new features like contradiction resolution, quality scoring, multi-agent mesh sync, and privacy filtering on ingest make the system production-ready and self-healing with minimal human maintenance.