Beyond expert users: agents should help users construct preferences, not just elicit them
Traditional agents assume users have well-formed preferences, but often users lack domain knowledge. This paper proposes CoPref model and CoShop benchmark, finding that top agents achieve only 56% accuracy after 5 turns, failing to expand users' understanding of their own needs.
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[Submitted on 29 Jun 2026]
Title:Beyond expert users: agents should help users construct preferences, not just elicit them
View a PDF of the paper titled Beyond expert users: agents should help users construct preferences, not just elicit them, by Irena Saracay and 2 other authors
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Abstract:Agents typically assume an expert user -- one with well-formed preferences about what they want -- and default to clarifying questions whenever the task is underspecified. We argue this assumption is unrealistic. Users often lack the domain knowledge to have completely specified preferences; if asked about their preference on some feature, the user may be unable to answer without the agent helping the user to learn some domain knowledge needed to form a preference for that feature, e.g., via examples or explanations. To formalize these principles, we draw on the Search-Experience-Credence framework from Information Economics to introduce CoPref, a model of how users construct preferences based on agent dialog actions. We then study these ideas concretely in agentic recommender systems, proposing CoShop, an interactive benchmark. In CoShop, an agent converses with and makes recommendations for a CoPref user. The agent's performance depends on whether it can help the user gain the knowledge needed to specify the task well. Evaluating five frontier models, we find that no agent exceeds 56% accuracy on CoShop despite five turns of interaction. Failures stem not from agents' ability to find items, but from how little the interaction expands what users know about what they want.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.30863 [cs.AI]
(or arXiv:2606.30863v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.30863
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Irena Saracay [view email] [v1] Mon, 29 Jun 2026 19:50:16 UTC (2,270 KB)
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View a PDF of the paper titled Beyond expert users: agents should help users construct preferences, not just elicit them, by Irena Saracay and 2 other authors
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