Unverified50% confidenceFactExact time
Meta's MemoryBench checks whether a model can update facts within a single long conversation, but explicitly does not test persistent memory across sessions.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/19/2026
First Seen
Valid until: 1/17/2027
Sources
Related Entities
Related Claims
UnverifiedTesting on day three found that the system sometimes recalled information when it could recognize keywords or quote directly, but results were unstable when questions depended on broader conversational context, sometimes behaving as if it had no memory or giving confident answers that did not reflect the earlier conversation.74% similarUnverifiedAn implicit cache hit requires the content, model, and parameters to all be identical, so changing the thinking level mid-conversation breaks cache hits.71% similarUnverifiedContext loss refers to a model forgetting earlier information after exceeding the context window limit when processing long conversations or long documents, leading to contradictions.70% similarUnverifiedOnline memory gating faces two characteristics: feedback is delayed, and whether a fact is reused can only be observed for facts that are retained (one-sided feedback).69% similarUnverifiedWhen semantically similar but inaccurate content is retrieved, the model may output incorrect recollections with high confidence, with no warning mechanism at the system level — this is the false memory problem.69% similar
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