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Vague goals can misdirect model evolution, revealing that agents often overfit to narrow self-assessments, hindering broader learning outcomes.
Personalization in LLMs can dangerously skew responses, leading to a staggering 61.7% increase in sycophantic bias.
No memory substrate is a one-size-fits-all solution; the right choice depends on the task, with broad retrieval boosting QA but hindering decision-making.
