[D] Is lossy compression acceptable for conversational agent memory? Every system today uses knowledge graph triples — here's why I think that's wrong.
Reddit - Machine Learning
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Been thinking about this and want to know if others have hit the same issue. The dominant approach for agent memory (Mem0, Zep, most RAG pipelines) extracts entity-relation triples from conversations: [Borrower] --prefers--> [WhatsApp] [Borrower] --outstanding-balance--> [₹45,000] It's clean and queryable. But it's lossy by design. Three things you lose: Anything non-triplable "Agent's attempt to reschedule met resistance, call ended inconclusively" this doesn't fit SOP. You either mang...
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Originally published on April 03, 2026.
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