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RMM

by Google / UCSB (2025)

System Card

OrganizationGoogle / UCSB (2025)
Released2025-03
Architectureagentic-workflow / Prospective + retrospective reflection with online RL retrieval
DetailsCombines Prospective Reflection (dynamic multi-granularity summarization into a personalized memory bank) with Retrospective Reflection, which refines retrieval online via reinforcement learning from LLM-cited evidence.
Parameters—
Domainpersonalizationagent-memoryepisodic-session
Open SourceNo
PaperView Paper
acl-2025rlreflectionmulti-granularlongmemeval

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
72.735pEstimated
MemoryBank
74.741pEstimated
Cross-Session Memory
1/1
LongMemEval
70.423pSelf-Reported
Multi-Hop QA
2/3
BABILong
no data
MultiHop-RAG
7579pEstimated
HotpotQA
73.964pEstimated
Agent Task Memory
1/1
AgentBench-Mem
7226pEstimated
Personalization
0/1
PerLTQA
no data
Factuality / Grounding
0/1
RAGAS
no data
Sources:arXiv:2503.08026 Table 1 (ACL 2025) — RMM with GTE retriever; baseline GTE RAG 63.6%. >10% improvement over no-memory baselineArena estimate — derived from capability profile, not independently verified