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AutoMem: A Text-Gradient Recursive Self-Improvement Framework for Automated Memory Architectures Search
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memory architectureautomated searchLLM optimization
2608.14621
Builder Relevance
1h ago80%
Abstract
AutoMem is a framework that optimizes memory architecture for LLM agents by adapting to specific tasks through a guided search process.
Reality Card
Core Claim
AutoMem consistently discovers task-adaptive memory architectures that outperform the strongest human-designed memory baselines, improving accuracy by 2.8 points on average.
Method / Result
Achieves a 14.3% reduction in token cost over the strongest accuracy baselines.
Limitations
The paper does not specify the authors or provide detailed experimental setups, which may hinder reproducibility.
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