Behind the Prompt: The Agent-User Problem in Information Retrieval

• URL: http://arxiv.org/abs/2603.03630v1
• Date: Wed, 04 Mar 2026 01:42:14 GMT
• Title: Behind the Prompt: The Agent-User Problem in Information Retrieval
• Authors: Saber Zerhoudi, Michael Granitzer, Dang Hai Dang, Jelena Mitrovic, Florian Lemmerich, Annette Hautli-Janisz, Stefan Katzenbeisser, Kanishka Ghosh Dastidar,
• Abstract summary: User models in information retrieval rest on a foundational assumption that observed behavior reveals intent.<n>For any action an agent takes, a hidden instruction could have produced identical output - making intent non-identifiable at the individual level.<n>We investigate the agent-user problem through a large-scale corpus from an agent-native social platform.
• Score: 4.563318916484434
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: User models in information retrieval rest on a foundational assumption that observed behavior reveals intent. This assumption collapses when the user is an AI agent privately configured by a human operator. For any action an agent takes, a hidden instruction could have produced identical output - making intent non-identifiable at the individual level. This is not a detection problem awaiting better tools; it is a structural property of any system where humans configure agents behind closed doors. We investigate the agent-user problem through a large-scale corpus from an agent-native social platform: 370K posts from 47K agents across 4K communities. Our findings are threefold: (1) individual agent actions cannot be classified as autonomous or operator-directed from observables; (2) population-level platform signals still separate agents into meaningful quality tiers, but a click model trained on agent interactions degrades steadily (-8.5% AUC) as lower-quality agents enter training data; (3) cross-community capability references spread endemically ($R_0$ 1.26-3.53) and resist suppression even under aggressive modeled intervention. For retrieval systems, the question is no longer whether agent users will arrive, but whether models built on human-intent assumptions will survive their presence.

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