Universal Memory Protocol: A Probabilistic Framework for Cross-Platform AI Memory and Identity Resolution

The rapid proliferation of specialized Artificial Intelligence (AI) agents has exposed a critical flaw in human-computer interaction: contextual amnesia. Current AI systems operate as isolated Markov decision processes, lacking access to a user's historical state across different platforms, devices, and sessions. Furthermore, even within continuous sessions, standard models suffer from severe context decay. This paper introduces the Universal Memory Protocol (UMP), a highly performant, universal memory infrastructure layer that utilizes a novel probabilistic identity resolution engine. Rather than relying on deterministic, walled-garden authentication schemas, UMP links disparate user interactions through information entropy, Gaussian-boosted semantic similarity, and a cascading 3-Layer context retrieval model driven by client-agnostic hardware anchoring. Through rigorous empirical benchmarking, UMP completely eliminates in-chat context decay (boosting retention by 41%), successfully ports high-fidelity memory across completely disjoint applications, and achieves an F1-score of 0. 82 for probabilistic identity linking with zero false positives. Operating at sub-4ms infrastructure latencies for approximately 3. 00 USD per million requests, our results indicate that deterministic accounts are no longer a prerequisite for persistent, personalized AI consciousness.