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An overview of the current state and future outlook of continual learning in mid-2026, covering memory approaches including external memory, in-state memory, and weight updates, with analysis of various models like TTT, Titans, and Dragon Hatchling.
This thread discusses best practices for building unified memory layers with knowledge graphs, emphasizing the separation of entity resolution (naming) from deduplication (identity) to avoid graph corruption. It also highlights using orchestration tools like PrefectIO to manage expensive LLM extraction pipelines with checkpointing and caching.
A reflection on the hidden costs of switching memory tools in AI agent systems after months of production, compared to the triviality of swapping models.