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MemoryBank

by Institute of Software, Chinese Academy of Sciences

System Card

OrganizationInstitute of Software, Chinese Academy of Sciences
Released2023-05
Architecturevector-rag / Ebbinghaus-curve forgetting mechanism
DetailsMemory store with retrieval and update mechanisms inspired by the Ebbinghaus forgetting curve — permits the AI to forget and reinforce memories based on elapsed time and significance. Summarizes past events and synthesizes user personality.
Parameters—
Domainagent-memorypersonalizationlifelong-learning
Open SourceYes
PaperView Paper
CodeRepository
ebbinghausforgetting-curvesiliconfriendcompanionaaai-2024

Capability Profile

Benchmark Scores

6 of 14 benchmarks
Data Transparency:1 self-reported5 estimated
Long-Context Retrieval
0/5
RULER
no data
NIAH
no data
LooGLE
no data
LongBench
no data
∞Bench
no data
Multi-Turn Recall
2/2
LoCoMo
62.314pEstimated
MemoryBank
81.888pSelf-Reported
Cross-Session Memory
1/1
LongMemEval
60.113pEstimated
Multi-Hop QA
1/3
BABILong
59.73pEstimated
MultiHop-RAG
no data
HotpotQA
no data
Agent Task Memory
1/1
AgentBench-Mem
66.815pEstimated
Personalization
1/1
PerLTQA
72.623pEstimated
Factuality / Grounding
0/1
RAGAS
no data
Sources:arXiv:2305.10250 Table 2 — SiliconFriend-ChatGPT English Ranking 0.818 rescaled to 0-100Arena estimate — derived from capability profile, not independently verified