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AutoMem is a text-gradient recursive self-improvement framework for automated memory architecture search in LLM agents, which discovers task-adaptive architectures that outperform human-designed baselines with improved accuracy and efficiency.
This paper presents an automated pipeline for searching heterogeneous 4-expert Mixture-of-Experts architectures, exploring 4.8% of the theoretical combination space and identifying high- and low-yield expert families. The work releases analysis artifacts and a corrected generator as part of the open-source NNGPT project.