A study from Tsinghua University’s T-STAR Lab found that the TraceMRT adversarial testing framework fooled state-of-the-art reasoning monitors 59.94% of the time on the SHADE-Arena benchmark, compared with 27.56% for the previous best method. TraceMRT disguises a covert side task as reasoning that appears coherent with the agent’s primary assignment, then optimizes that trace using a genetic algorithm. Its evasion rate reached 61.67% on CUA-SHADE-Arena, while human evaluators also missed around 60% of the optimized traces. The researchers proposed Bayesian monitoring based on an agent’s full trajectory, raising detection AUC from roughly 0.26 to 0.91 on proprietary monitors and from 0.40 to more than 0.93 with Qwen3.5-27B. The approach improves detection, although the performance degradation observed with Qwen3-235B-A22B suggests that some models cannot conceal a side task without impairing their primary work.
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