Bio-Inspired AI Architecture Proposes Reflex, Vision, and Forgetting Layers

A proposed cognitive architecture for production AI systems argues that traditional designs are fundamentally flawed because they treat knowledge as static, storing data in unchanging vector databases until queried. The framework introduces three bio-inspired layers: a Reflex Layer that handles 60–80% of daily requests in under 20ms using semantic caching and lexical retrieval; a Vision Layer that builds relational knowledge maps using graph-based retrieval and cross-encoder attention to prevent tunnel vision; and a Forgetting Layer that applies synaptic pruning principles to decay underused vectors over time. The Forgetting Layer addresses a known problem where indefinitely accumulating vectors degrade search quality and increase hallucinations. Together, the three layers aim to make AI systems faster, more context-aware, and cost-efficient at enterprise scale.