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The literature reveals a surprising imbalance: while always-on agents excel at accumulating state, they largely neglect essential governance and recovery mechanisms.
Automatic attribution metrics for LLMs can flip rankings across datasets, leading to misleading evaluations that could cost researchers dearly in decision-making.
Exact-match retrieval metrics can mislead assessments of policy utility, as retrieved clauses perform nearly as well as gold-standard ones in decision-making tasks.
Direct token-level self-distillation can backfire, but Sibling-Guided Credit Distillation redefines credit assignment to enhance long-horizon tool-use without amplifying harmful behaviors.
