Memoria Modular Memory Framework (Sarin et al., 2025)

URL: https://arxiv.org/abs/2512.12686

The paper proposes Memoria, a modular memory framework augmenting LLM-based conversational systems with persistent, interpretable, and context-rich memory. The framework integrates dynamic session-level summarization with a weighted knowledge-graph-based user-modelling engine. The contribution is in the modular composition: separable summarization, user-modelling, retrieval, and update components.

Adopted

Memoria is one node in the MemGPT-to-ClawVM agent-memory lineage; cited in this graph's recent-supporting-evidence section as evidence of the user-space primitive-rebuild trajectory's continued evolution. The weighted knowledge-graph user-modelling engine is structurally similar to the typed-edge graph this DeepContext convention uses; both reassemble runtime-shaped relational structure on top of runtime layers that do not natively carry it.

Not adopted (yet)

Memoria's modularity is a strength against the inadequate-runtime position: by separating concerns, the framework can be tuned per use case. The runtime-LAYER position is that this modularity is unnecessary if the runtime carries the relational structure as a primitive; the components Memoria modularizes are unified in the runtime's persistent state graph.

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