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Nemori

by Nemori AI (independent)

System Card

OrganizationNemori AI (independent)
Released2025-08
Architectureepisodic-buffer / Event-segmentation-aligned episodic + semantic memory
DetailsTwo-step alignment principle from Event Segmentation Theory segments raw conversations into semantically meaningful episodes. Predict-Calibrate principle enables proactive learning from prediction gaps. Produces episodic + semantic memory with a unified search surface.
Parameters—
Domainagent-memoryepisodic-session
Open SourceYes
PaperView Paper
CodeRepository
event-segmentationcognitive-sciencelocomolongmemeval

Capability Profile

Benchmark Scores

6 of 14 benchmarks
Data Transparency:2 self-reported4 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
79.482pSelf-Reported
MemoryBank
77.460pEstimated
Cross-Session Memory
1/1
LongMemEval
74.642pSelf-Reported
Multi-Hop QA
2/3
BABILong
no data
MultiHop-RAG
7250pEstimated
HotpotQA
77.890pEstimated
Agent Task Memory
1/1
AgentBench-Mem
7226pEstimated
Personalization
0/1
PerLTQA
no data
Factuality / Grounding
0/1
RAGAS
no data
Sources:arXiv:2508.03341 results table — LLM-judge score with gpt-4.1-mini backbone; gpt-4o-mini variant scored 74.4arXiv:2508.03341 results table — LongMemEval-S accuracy with gpt-4.1-mini; uses 95-96% less context than full-context baselineArena estimate — derived from capability profile, not independently verified