// RESEARCH_VALIDATION_
> MemoryGraph's architecture is validated by leading AI research
Evo-Memory (Google DeepMind)
$ KEY_FINDING:
"Conversational recall ≠ Experience reuse" — vector-based memory (like Mem0) fails at learning from past sessions
> MemoryGraph Relevance:
[Read Paper] →
Our typed relationships (SOLVES, CAUSES, IMPROVES) enable true experience reuse, not just fact recall
Titans + MIRAS (Google Research)
$ KEY_FINDING:
Deep memory architectures outperform shallow fixed-size vector stores for long-term AI memory
> MemoryGraph Relevance:
[Read Blog] →
Graph-based storage with 35+ relationship types provides deep memory structure
MCR² Theory (UC Berkeley Ma Lab)
$ KEY_FINDING:
Information compression via rate reduction provides principled framework for memory consolidation
> MemoryGraph Relevance:
[Learn More] →
Dreams Agent uses MCR² metrics to measure and optimize memory organization
"
Agents remember what was said but not what was learned.
— Evo-Memory, Google DeepMind
$ See MemoryGraph in Action
> Learn how MemoryGraph implements these research principles in practice
[Explore Documentation] →