Mobile-optimized multi-stage llm with generalized thought caching

Abstract

A system and method for mobile-optimized natural language processing employs a three-tier thought caching architecture comprising a local device cache, a user-specific cloud cache, and a global generalized thought cache. The system processes prompts using a first large language model to generate thoughts, which are then processed with the prompt by a smaller model to produce responses. A thought generalizer identifies common reasoning patterns across users, removes personal information, and creates shareable abstracted thought structures. Mobile-specific optimizations include battery-aware execution scaling and predictive thought pre-caching. When offline, the system adapts existing cached thoughts to address new prompts. Hierarchical thought management organizes information at different abstraction levels, enabling effectively unlimited context while efficiently managing resources. This architecture provides sophisticated language processing on mobile devices with offline functionality while maximizing battery efficiency and maintaining privacy.