Learning to Forget: Sleep-Inspired Memory Consolidation for Resolving Proactive Interference in Large Language Models

Researchers developed SleepGate, a biologically-inspired framework that significantly improves large language model memory by mimicking sleep-based consolidation to resolve proactive interference. The system achieved 99.5% retrieval accuracy compared to less than 18% for existing methods in experimental testing.

• →SleepGate framework reduces memory interference in LLMs from O(n) to O(log n) complexity through sleep-inspired consolidation mechanisms.
• →The system uses conflict-aware tagging, selective forgetting gates, and consolidation modules to manage outdated information in context windows.
• →Experimental results show 99.5% retrieval accuracy at depth 5 versus under 18% for all baseline methods including full KV cache and sliding window approaches.
• →The framework addresses a fundamental architectural limitation that cannot be solved through prompt engineering alone.
• →Sleep micro-cycles are triggered adaptively using entropy-based mechanisms during model inference.