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New Quality Standard Proposes Audit Checklist for LLM Memory Architecture

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A versioned audit checklist titled 'Memory Architecture Quality Standard for LLM Assistants' (v1.1, dated August 21, 2026) has been published to help developers evaluate long-term memory systems in AI assistants and agents. The standard covers the full memory lifecycle — from input ingestion and storage to retrieval, context assembly, and feedback loops — across relational databases, vector indexes, caches, and graph stores. Items are categorized by criticality, with blockers flagged for risks such as data poisoning, memory leakage, secret exposure, and uncontrolled memory growth. The checklist requires auditors to map all storage systems and verify read/write ownership before proceeding through sections covering input validation, retrieval logic, and prompt injection risks. Each finding must be backed by concrete evidence such as file references, SQL query results, or prompt snapshots, as unsupported assertions are not considered verified.

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