Memory Canonicalization: A Framework and Benchmark for Cross-Model Drift in Persistent LLM Memory

Excerpt

arXiv:2610.05124v1 Announce Type: new Abstract: Persistent memory for Large Language Models (LLMs) has matured rapidly: systems such as MemGPT/Letta, Mem0, and Zep now provide agents with tiered, temporally-aware, model-agnostic external storage, while the Model Context Protocol (MCP) standardizes access to memory servers. A less addressed problem is that an identical stored memory object, retrieved by two different LLMs under otherwise identical conditions, may not be interpreted the same way,