ANKR SMRITI: Cross-Session Memory Architecture for AI Systems — RECALL Layer, Session Compression, and Epistemological Continuity

Key Points

• The research aims to develop a memory architecture enabling AI systems to retain knowledge across multiple sessions.
• Developed a three-tier memory store including session JSON and compressed data extracts.
• Implemented a session compression pipeline to enhance data efficiency.
• Introduced an epistemological continuity model to facilitate knowledge accumulation.
• Demonstrated improved knowledge retention across sessions.
• Reduced context window limitations for AI agents.
• Enhanced overall performance in AI systems through better data management.