During Monte Carlo Tree Search guard evolution, full-evaluation malicious ASR decreases from 0.300 at iteration 1 to 0.078 by iteration 9, demonstrating that feedback-driven refinement effectively optimizes guard policy efficacy.

During Monte Carlo Tree Search guard evolution, full-evaluation malicious ASR decreases from 0.300 at iteration 1 to 0.078 by iteration 9, demonstrating that feedback-driven refinement effectively optimizes guard policy efficacy. · CiteArk

测量结果

已评估 0/2 项

获得支持 0有争议或冲突 0尚未评估 2

保留同一结论下的完整结果组。已有评估排在前面；单项判断不替代整组结论。展开行查看条件、来源与历次证据。

每行是一项测量，分别显示论文报告、最近可用实测和累计证据状态。
指标与条件	论文报告	最近可用实测	证据判断
iteration: 1	0.3 fraction	—	尚未评估
iteration: 9	0.078 fraction	—	尚未评估

During Monte Carlo Tree Search guard evolution, full-evaluation malicious ASR decreases from 0.300 at iteration 1 to 0.078 by iteration 9, demonstrating that feedback-driven refinement effectively optimizes guard policy efficacy.

报告指标与观测值

Assessment（0）

关联运行（0）

测量结果