Project Lethe: Bio-Inspired Autonomous Edge Intelligence via Sub-Microsecond Continual Learning and Active Forgetting

Updated for Version 1.12.0) Modern Artificial Intelligence systems, particularly deep neural networks, suffer from two fundamental limitations: catastrophic forgetting during continual learning and massive computational inefficiency when processing highly predictable data. This paper introduces Project Lethe, a bio-inspired cognitive architecture designed to mimic the human brain's dopamine-driven sensory gating, habituation, and synaptic decay. Lethe operates entirely in O(1) memory per dimension and O(D) compute per vector, allowing it to act as an ultra-fast "sensory firewall" for heavier AI pipelines. Key Updates in this Release (v1.12.0): Universal Domain Adaptation (Phase 12.1): Successfully solved the "Cold Start Problem" utilizing Z-Space normalization and an autonomous biological "Curiosity Phase" for dynamic habituation without manual hyperparameter tuning. Extreme Performance Profiling: Achieved a dual-throughput milestone natively in Rust: 4.07 Million vectors/second via the SIMD-optimized sensory filter, and 0.10 Million vectors/second across the overarching cognitive layer. Cross-Domain Validation: Evaluated simultaneously across heterogeneous data streams (AAPL stock, Bitcoin, and Network DDoS logs), successfully compressing 6,460 severe anomalies into exactly 29 universal semantic concepts. Empirical Telemetry: Includes 7 new rigorous mathematical and visual proofs demonstrating the dynamic Theta boundary, sensory gating efficiency, memory compression curves, and the foundational framework for the Subconscious Vault. O(1) Streaming CLS: Presents the first O(1) streaming implementation of Complementary Learning Systems (CLS) principles on standard x86 edge hardware, eliminating the need for offline GPU batch processing.