Unverified50% confidenceFactExact time
In the Chat Memory layer, Hy-Memory-30B-A3B handles information extraction and contradiction resolution, identifying when newer statements temporally invalidate old facts, archiving legacy data and retaining currently valid facts.
1
Sources
50%
Confidence
Long-term
Relevance
10/19/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedIf incorrect information is saved to long-term memory, it will contaminate subsequent tasks; only truly valuable, confirmed information should be saved.73% similarUnverifiedIf the memory store is full of duplicate, outdated, or mutually contradictory entries, the retrieval results will contain large amounts of low-value information, crowding out context space and even misleading the model, which is context pollution.71% similarUnverifiedOld memories with a high semantic match are often recalled more readily than newer memories that are less closely matched, thereby overriding the correct answer.71% similarUnverifiedThe memory layering mechanism divides information into three levels: long-term memory, short-term memory, and character settings, with long-term memory generally stored in a vector database and retrieved on demand via semantic search.70% similarUnverifiedMeta's MemoryBench checks whether a model can update facts within a single long conversation, but explicitly does not test persistent memory across sessions.70% similar
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