SuperLocalMemory V3.3 implements a cognitive memory taxonomy with mathematical forgetting and multi-channel retrieval, reaching 70.4% on LoCoMo in zero-LLM mode.
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norM-DSGT and norM-ED achieve centralized stochastic proximal-gradient rates for distributed composite objectives, with norM-ED transient time O(n^3/(1-λ)^2).
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SuperLocalMemory V3.3: The Living Brain -- Biologically-Inspired Forgetting, Cognitive Quantization, and Multi-Channel Retrieval for Zero-LLM Agent Memory Systems
SuperLocalMemory V3.3 implements a cognitive memory taxonomy with mathematical forgetting and multi-channel retrieval, reaching 70.4% on LoCoMo in zero-LLM mode.
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Distributed Normal Map-based Stochastic Proximal Gradient Methods over Networks
norM-DSGT and norM-ED achieve centralized stochastic proximal-gradient rates for distributed composite objectives, with norM-ED transient time O(n^3/(1-λ)^2).