Activation-Guided Graph Retrieval for Cognitive Memory Reconstruction in Language Model Systems
Summary
- A new preprint proposes activation-guided graph retrieval for cognitive memory reconstruction in language model systems.
- The architecture uses request and goal cues to seed a memory graph, bounded by activation and fan attenuation.
- Retrieved entities recursively emit cues to reconstruct a typed evidence graph before text generation occurs.
- The paper provides implementation-level cost estimates, ablations, failure conditions, and a reproducible evaluation protocol without benchmark measurements.
Why it matters
This architectural proposal addresses long-lived AI systems by moving beyond query-conditioned relevance to activation-conditioned memory availability.