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A detailed architectural guide for building long-running AI agents that handle changing user preferences over time by combining a vector store, graph DB, and temporal edges instead of overwriting data.
A developer shares Helix-AGI, a continuously-running cognitive agent using a physics-based memory retrieval system that integrates recency, structural importance, and semantic proximity via an entropic gravity equation and Euler-Lagrange dynamics, without tuning separate weights.
Introduces Stratum, a system-hardware co-design approach utilizing 3D-stackable DRAM to efficiently accelerate Mixture of Experts (MoE) models.
OpenSquilla has launched an open-source AI agent runtime designed to reduce token costs through intelligent routing, caching, and a four-tier memory architecture, claiming 60-80% cost savings.
EvolveMem introduces a self-evolving memory architecture for LLM agents that optimizes retrieval configurations through LLM-powered diagnosis and iterative research cycles, achieving significant performance improvements on benchmarks like LoCoMo and MemBench.
Lyzr Cognis introduces a unified, open-source memory system for conversational AI that fuses BM25 and Matryoshka vector search with version-aware ingestion, achieving SOTA on LoCoMo and LongMemEval benchmarks.