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Skill Optimizers trained through execution feedback can outperform traditional models by over 9 points, revealing a critical gap in agent learning methodologies.
Achieving 100x parameter efficiency, LoopWM redefines how we approach long-horizon world modeling by introducing iterative latent depth as a new scaling axis.
RAG models struggle to use retrieved knowledge even when it's relevant, but GuarantRAG's two-stage generation and joint decoding boosts accuracy by 12% and slashes hallucinations by 16%.
