Building AI that remembers, learns, and grows over time through advanced memory architectures.
Current AI systems suffer from severe memory limitations. Large language models have fixed context windows and no true long-term memory. Each conversation starts from scratch. The system cannot learn from interactions, remember users, or accumulate knowledge over time.
MEGAMIND addresses this fundamental limitation with a multi-system memory architecture inspired by human cognition.
Active maintenance and manipulation of information during reasoning.
Storage and retrieval of specific experiences and contexts.
Long-term storage of factual knowledge and concepts.
Learned skills and behavioral patterns.
Current AI systems have limited context windows and no true long-term memory. They can't learn from past interactions, accumulate knowledge over time, or remember previous conversations. This limits their ability to build relationships, improve from experience, and maintain consistent knowledge.
MEGAMIND implements multiple memory types: working memory for active reasoning, episodic memory for storing experiences, semantic memory for factual knowledge, and procedural memory for learned skills. Each serves different functions, similar to human memory systems.
MEGAMIND uses associative retrieval based on semantic similarity, contextual relevance, and recency. An attention-based retrieval system identifies relevant memories and integrates them into current reasoning. The system also implements forgetting mechanisms to maintain efficiency.