A biologically-inspired memory architecture for LLM agents combines six mechanisms with synthetic calibration to achieve 97.2% retention precision and 58% store reduction on issue-tracking data while matching raw retrieval accuracy on long-chat benchmarks.
Neuron , volume =
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.AI 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Human-Inspired Memory Architecture for LLM Agents
A biologically-inspired memory architecture for LLM agents combines six mechanisms with synthetic calibration to achieve 97.2% retention precision and 58% store reduction on issue-tracking data while matching raw retrieval accuracy on long-chat benchmarks.