Proposes a universal memory architecture that enhances episodic and semantic memory in AI systems, suggesting improved knowledge stability.
This paper proposes a universal memory architecture for AI systems that supports persistent episodic and semantic memory across multimodal inputs. The Universal Memory Model (UMM) integrates canonicalization, embedding-based encoding, salience filtering, episodic storage, semantic consolidation, contradiction handling, and attention-based retrieval. The model addresses context window limitations, memory drift, and long-term knowledge stability.
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Santosh K. Dasari (2026) studied this question.
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