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The research aims to develop a memory architecture enabling AI systems to retain knowledge across multiple sessions.

April 1, 2026Open Access

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.

Abstract

SMRITI (Sanskrit: memory, recollection) is a cross-session memory architecture for AI systems. The protocol defines a three-tier store (session JSON, compressed SHASTRA/VIVEKA extracts, living index), a session compression pipeline, and an epistemological continuity model that allows AI agents to accumulate knowledge across sessions without context window limitations.

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Anil Kumar Sharma (Mon,) studied this question.

synapsesocial.com/papers/69ccb71716edfba7beb88df2 — DOI: https://doi.org/10.5281/zenodo.19335564

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

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