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RecallM

by Cisco Research / independent (Kynoch & Latapie)

System Card

OrganizationCisco Research / independent (Kynoch & Latapie)
Released2023-07
Architecturehybrid / Graph + vector with temporal belief updating
DetailsHybrid memory combining graph databases and vector stores to support temporal reasoning, integrating structured relational data with embedding-based retrieval to enable dynamic belief updating and sequential knowledge tracking.
Parameters—
Domainlifelong-learningknowledge-graphepisodic-session
Open SourceYes
PaperView Paper
CodeRepository
temporal-reasoningbelief-updatehybridgraph

Capability Profile

Benchmark Scores

6 of 14 benchmarks
Data Transparency:6 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
78.276pEstimated
MemoryBank
73.935pEstimated
Cross-Session Memory
1/1
LongMemEval
76.755pEstimated
Multi-Hop QA
2/3
BABILong
78.176pEstimated
MultiHop-RAG
no data
HotpotQA
77.486pEstimated
Agent Task Memory
1/1
AgentBench-Mem
7226pEstimated
Personalization
0/1
PerLTQA
no data
Factuality / Grounding
0/1
RAGAS
no data
Sources:Arena estimate — derived from capability profile, not independently verified