System and method for large language model with integrated memory during inference using manifold traversal architecture

Abstract

A large language model system integrates persistent memory directly into inference operations through geometric manifold traversal rather than external retrieval. The system implements a memory-integrated inference engine that performs token generation with simultaneous memory access by navigating curved regions in a geometric memory manifold. Memories exist as navigable basins of increased curvature that are reinforced through usage rather than stored as discrete objects. An intent conditioning system formulates user queries as utility functions and generates vector fields that guide goal-directed memory traversal. A manifold geometry interface converts geometric memory coordinates into vectors compatible with language model attention mechanisms, augmenting standard key-value caches with memory-derived content. The system performs intentional remembering through path optimization that balances fidelity to prior cognitive trajectories with current intent guidance. Each memory access operation simultaneously retrieves information and strengthens accessed memory regions through bidirectional geometric shaping, enabling persistent cognitive evolution and cross-session memory continuity.