AI Sleep Constructs: Implementing Chronological Intelligence in Stateful Systems

AI Sleep Constructs is a production-ready Python framework that brings temporal awareness and self-maintenance to artificial intelligence models. Inspired by biological sleep, it introduces engineered light and deep sleep cycles that enable language models to optimize performance, consolidate memory, and self-heal between inference sessions. Light sleep modes perform gradient clipping, cache management, and adaptive learning, while deep sleep modes execute semantic consolidation, layer recalibration, and diagnostic repair. Contextual triggers automatically govern these transitions based on model drift, performance anomalies, or workload scheduling. The framework also includes Cache-to-Cache (C2C) fusion, a novel technique allowing KV-cache transfer across models for up to 10× faster inference with minimal quality loss. Integrated with Hugging Face APIs, it provides an extensible foundation for research on stateful, chronologically intelligent AI architectures. Repository URL (Git): https://github.com/electricwolfemarshmallowhypertext/ai-sleep/ Keywords: AI sleep, Chronological Intelligence, Offline Optimization, Adaptive Learning, C2C Fusion, Stateful AI, Antiparty Inc.