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

Measurement results

0/2 assessed

0 supported0 disputed or conflicting2 not assessed

The complete result group stays with its claim. Assessed rows come first; their judgments do not replace the whole-claim conclusion. Expand a row for conditions, sources, and evidence history.

Each row is a measurement with its reported value, latest available observation, and cumulative evidence state.
Metric and conditions	Reported	Latest observation	Evidence judgment
iteration: 1	0.3 fraction	—	Not assessed
iteration: 9	0.078 fraction	—	Not assessed

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.

Reported and observed measurements

Assessments (0)

Runs (0)

Measurement results