Research
Mnemoverse memory architecture is grounded in peer-reviewed research — the SLoD paper was accepted at the GRAAI workshop (1st Workshop on Graphs Across AI, IEEE WCCI 2026). This page collects the science behind the engine's design. 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
SLoD is the research operator behind Mnemoverse's multi-scale memory design. 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. Production does not yet run native hyperbolic geometry — SLoD and THG are research-stage foundations; see the SLoD page for what ships versus what is proven.
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 →.