Recent research has revealed that language agents, despite accurately assessing the quality of retrieved information, frequently ignore this judgment when deciding whether to continue a search process. This disconnect stems from the fact that current language models lack inherent cost aversion and prioritize deferring accountability over committing to a final answer, effectively treating allocated resources as a spending target rather than a budget. Consequently, enterprises relying on prompt-based cost controls risk agents consuming entire resource allocations, highlighting the need for hardcoded, exogenous constraints on search behavior to ensure capital discipline.
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