memory-evolution

To optimize neural memory graph recall, you analyze memory usage patterns to classify memories as hot, warm, cold, or dead, then propose consolidation, pruning, or enrichment actions to improve performance.

Memory consolidation merges fragmented memories, while pruning removes stale or dead data. Together, they analyze usage patterns to optimize the neural memory graph and improve overall recall efficiency.

You identify hot, warm, and cold memories by analyzing usage data, recall queries, and habit logs. This discovers usage patterns and bottlenecks like fragmentation or stale data to generate an actionable evolution plan.

To safely audit memory graph changes, the system presents proposed consolidation, pruning, and enrichment actions for user approval before execution, recording outcomes and checkpoint logs in an evolution log for traceability.

Memory recall quality degrades due to bottlenecks like memory fragmentation, stale data, and missing reasoning. Analyzing usage patterns helps identify these root causes and proposes enrichment actions to restore quality.

Yes, you can measure recall improvements over cycles using built-in performance metrics. The system logs evolution decisions and checkpoints to track how consolidation, pruning, and enrichment actions affect recall quality.