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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 proposes test-time adaptation through human-agent interaction (TAHI), which integrates these signals into agent context and weights, and crystallizes each user's training and evaluation criteria via an evolving rubric module.
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