@inproceedings{li-etal-2026-prefix,
    title = "{P}ref{I}x: Understand and Adapt to User Preference in Human-Agent Interaction",
    author = "Li, Jialin  and
      Chen, Zhenhao  and
      Luo, Hanjun  and
      Salam, Hanan",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1506/",
    pages = "30110--30149",
    ISBN = "979-8-89176-395-1",
    abstract = "LLM-based agents can complete tasks correctly yet still frustrate users through poor interaction patterns, such as excessive confirmations, opaque reasoning, or misaligned pacing. Current benchmarks evaluate task accuracy but overlook how agents interact: whether they infer preferences from implicit cues, adapt dynamically, or maintain fine-grained interaction quality. We introduce , a configurable environment that evaluates both what agents accomplish and how they interact. Central to {~}is the Interaction-as-a-Tool (IaaT) paradigm, which treats interaction behaviors as structured tool calls, unifying them with existing evaluation frameworks. We define 31 preference settings across 14 attributes and formalize user experience (UX) as a core metric alongside task accuracy. A composite LLM-as-a-Judge mechanism across seven UX dimensions achieves strong aggregate reliability (ICC $>$ 0.79), high internal consistency ($\alpha$ = 0.943), and human correlation ($\rho$ = 0.52-0.78). Preference-aware agents show 7.6{\%} average UX improvement and 18.5{\%} gain in preference alignment."
}