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Current VLA models fail to benefit from "How-level" instructions, with enriched training data even decreasing task success.
LLM agents waste memory on transient interactions because they lack personalized memory policies, and this benchmark shows just how much better things could be.
Forget hand-tuning recommender systems: Self-EvolveRec uses LLMs and a novel directional feedback loop to automatically evolve better architectures.