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Artificial intelligence (AI) agents promise to extend what users can accomplish beyond their existing expertise, but they also raise the risk of producing plausible-looking work that users may struggle to evaluate. We ask how task-relevant expertise and trust in AI shape agent-assisted work. To isolate these factors, w...
It is shown that LLM-as-judge RL induces reward-hacking patterns, and that LLM-as-judge RL detectors can mitigate them during post-training, suggesting that behavioral foundation models require rethinking the LLM training paradigm.
This work creates a framework that maps agent failure modes to harness adaptation strategies, and builds a harness optimizer that automatically discovers effective adaptations from failure trajectories, suggesting that harness adaptation can expand the practical deployment range of SLM agents in routine business tasks.
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