Unverified50% confidenceFactExact time
This problem is essentially an online constrained optimization problem whose goal is to minimize the frequency of expert auditing while ensuring that the proportion of unaudited AI errors does not exceed a user-specified threshold.
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50%
Confidence
Long-term
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10/23/2026
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Related Claims
UnverifiedExperimental results show that the method achieves effective error control in practice while significantly reducing the need for manual auditing compared with baselines.72% similarUnverified通过LoRA等参数高效微调方法，恶意行为体仅需少量计算资源和数据即可移除模型内置的安全对齐，使其无条件服从任何指令70% similarUnverifiedThe level of automation should be decided based on the severity of the consequences of errors: catastrophic errors require human review before action; annoying but recoverable errors can be automated but require hard retry limits and rollback; imperceptible errors can be fully automated and allowed to proceed.70% similarUnverifiedThe breaking points of automated evaluation include: LLM evaluators are lenient, judge partially correct answers poorly, are sensitive to prompt wording, miss small errors, and their agreement rates can mask low recall.70% similarUnverifiedSetting explicit rate limits and request caps on agent calls to external APIs can prevent the agent from falling into an unrestrained trial-and-error loop.69% similar
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