Activation-Guided Graph Retrieval for Cognitive Memory Reconstruction in Language Model Systems

Abstract

Most retrieval-augmented language models choose context by query relevance. Long-lived AI systems need another signal: whether a memory is available for the current goal. We describe an activation-guided graph retrieval architecture. Request and goal cues start traversal inside an authorized memory graph. Activation and fan effects keep the search bounded. Retrieved entities emit new cues, and the system rebuilds a typed evidence graph before generation. The proposal gives five testable claims about candidate selection, traversal control, graph regularization, evidence reconstruction, and reinforcement. It also defines cost estimates, ablations, failure cases, and a reproducible evaluation protocol.