Research
Mnemoverse memory architecture is grounded in peer-reviewed research — the SLoD paper was presented at GRAAI (1st Workshop on Graphs Across AI, IEEE WCCI 2026). This page collects the science that directly underpins the engine. Analysis, market landscape, and longer-form writing live in the Library.
Featured Paper — SLoD
Semantic Level of Detail: Multi-Scale Knowledge Representation for AI Memory
Edward Izgorodin — arXiv:2603.08965
The SLoD algorithm is the core of how Mnemoverse stores, recalls, and consolidates memories. It borrows the Level of Detail concept from computer graphics and applies it to semantic memory: knowledge is represented at multiple scales, from fine-grained facts to high-level summaries, using heat kernel diffusion on hyperbolic manifolds.
Key properties:
- Hierarchical coherence: bounded error
O(sigma), distortionO(log n) - Natural abstraction detection via spectral boundary gaps
- Enables Mnemoverse to return the right granularity of memory for each query
Read the full SLoD page → | arXiv:2603.08965
Benchmarks
Mnemoverse is evaluated on standard AI memory benchmarks.
Tensor-Hyperbolic Graphs
THG page → — the graph-theoretic extension underpinning multi-hop memory association.
Looking for analysis, market landscape, and essays on AI memory? Browse the Library →.