Faithful user simulation is fundamental to building, evaluating, and improving interactive AI at scale. Yet current simulators often produce plausible individual responses without reproducing the intent evolution and outcomes observed in real interactions. We propose TRACER, a multi-turn user simulator that models evol...
Ge Chen, Ruo-Tong Pan, Zhi-Rui Yang et al.· 0 citations
Sustaining industrial recommendation research requires using the results of one experiment to decide what to investigate next. We present AgentX-Model, the next generation of AgentX's model research framework, which connects proposal development and model experimentation within sandboxes defined by business inputs and...
Shuang Yang, Zi-Jie Zhuang, Chang-Xin Lao et al.· 0 citations
Modern recommendation systems largely infer user preferences from implicit behaviors such as clicks, watch time, and negative feedback, but these signals reveal what users do rather than why they like or dislike content. This work studies articulated user rationales (AURs), i.e., users'natural-language explanations of...
Hao-Ke Xiao, Yue-Yang Liu, Yu-Hui Zhang et al.· 0 citations
Semantic-gradient-based prompt optimization (SGPO) improves agent harnesses using execution feedback, but its local update rule leaves the choice of edit scope and operation unresolved. We introduce RobustSGPO, which specifies the requested edit, constructs and checks the patch, and continues search from either the inc...
Zi-Bo Zhao, Ji-Jun Shi, Mo-Qing Zhou et al.· 1 citation
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