Recall: A Framework for Long-Term AI Memory

Current AI memory systems often suffer from excessive rigidity (as in traditional databases) or opacity(as in neural network-based approaches), hindering efficient knowledge storage, retrieval, and updating, particularly for logical or dynamic information.

Recall proposes a balanced alternative: a memory framework combining a flexible graph structure with temporal awareness, logic storage capabilities, and trust-based conflict resolution. This design aims to create a more adaptable and transparent system for long-term AI memory.

This paper provides a high-level introduction to the Recall framework, presenting its core concepts and design motivations while avoiding technical implementation details.