What question did this study set out to answer?

The primary aim is to evolve a cognitive intelligence engine that adapts autonomously using multi-sensor input.

June 5, 2026Open Access

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

Key Points

The primary aim is to evolve a cognitive intelligence engine that adapts autonomously using multi-sensor input.
Developed a 10-module architecture incorporating various sensors like CPU, Disk, and Network.
Implemented a reinforcement learning system for outcome-based learning and response adaptation.
Introduced cross-session memory maintenance and data-driven calibration to optimize performance.
Achieved independent processing across 6 sensor domains while maintaining a unified intelligence architecture.
Enhanced anomaly response accuracy through a joint multi-sensor model, improving detection rates.
Enabled zero manual configuration through adaptive self-calibration, leading to unique internal representations.

Abstract

Version 4.2.0 — Neurological Brain Complete This version represents a major architectural expansion of Project Lethe, evolving from a single-sensor anomaly filter (V1.12.0) into a complete 10-module autonomous cognitive intelligence engine. WHAT IS NEW IN V4.2.0 Phase 16 — Autonomous RL FeedbackOutcome-based patience learning system. After every anomaly response, the engine classifies the outcome (HIT or MISS) and adapts its response latency autonomously. Learning persists across system restarts via binary serialization. Phase 17-18 — Universal Multi-Sensor OrchestratorArchitecture generalized to support 6 heterogeneous sensor domains simultaneously: CPU, Disk, Network, Temperature, Stock Market, and OS Logs (via Hyperdimensional Computing encoder). Each sensor maintains an independent Welford filter while sharing a unified brain. Phase 20 — Neurological Brain Expansion (5 new modules)RAS (Reticular Activating System): 4-level arousal state machineACC (Anterior Cingulate Cortex): Joint multi-sensor covariance anomaly modelThalamus: Data-driven cross-sensor attention routingCerebellum: Hybrid trend prediction engineBasal Ganglia: Behavioral habit formation and false alarm suppression Phase 21 — Adaptive Self-CalibrationAll developer-specified constants replaced with data-driven equivalents learned from the host machine's own runtime behavior. Two identical machines will develop different internal representations based on their workloads. Zero manual configuration required. Phase 22 — Brain HardeningExtended the joint anomaly model to include 4 sensor dimensions. Implemented cross-session RL persistence so patience adaptation accumulates across indefinite restarts. Introduced periodic memory maintenance to prevent concept vault bloat. Upgraded trend prediction for non-linear signal acceleration. PAPER UPDATESThe research paper now includes 12 empirical figures, 9 academic references, and a full 15-item development completion status covering V1.0 through V4.2.0. The updated abstract describes the complete neurological brain architecture. Four new charts document Phase 20-22 behavior: RAS arousal state timeline, memory tiering architecture, theta decay habituation curve, and cognitive pressure rolling analysis. CODE AVAILABILITYThe implementation remains proprietary and under active development. Open-source release is planned for V5.0+ upon reaching production-grade stability.

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