Prism: An Evolutionary Memory Substrate for Multi-Agent Open-Ended Discovery

arXiv:2604.19795v1 Announce Type: new
Abstract: We introduce prism (textbfProbabilistic textbfRetrieval with textbfInformation-textbfStratified textbfMemory), an evolutionary memory substrate for multi-agent AI systems engaged in open-ended discovery. prism unifies four independently developed paradigms — layered file-based persistence, vector-augmented semantic memory, graph-structured relational memory, and multi-agent evolutionary search — under a single decision-theoretic framework with eight interconnected subsystems.
We make five contributions: (1)~an emphentropy-gated stratification mechanism that assigns memories to a tri-partite hub (skills/notes/attempts) based on Shannon information content, with formal context-window utilization bounds; (2)~a emphcausal memory graph $mathcalG = (V, E_r, E_c)$ with interventional edges and agent-attributed provenance; (3)~a emphValue-of-Information retrieval policy with self-evolving strategy selection; (4)~a emphheartbeat-driven consolidation controller with stagnation detection via optimal stopping theory; and (5)~a emphreplicator-decay dynamics framework that interprets memory confidence as evolutionary fitness, proving convergence to an Evolutionary Stable Memory Set (ESMS). On the LOCOMO benchmark, prism achieves 88.1 LLM-as-a-Judge score (31.2% over Mem0). On CORAL-style evolutionary optimization tasks, 4-agent prism achieves 2.8$times$ higher improvement rate than single-agent baselines.%