{
  "schema": "ULTRACON_AI_EPISTEMIC_UPDATE_SOURCES_V1",
  "date": "2026-09-19",
  "sources": [
    {
      "id": "SRC-IBRAHIM-SYCOPHANCY-LONGITUDINAL-2026",
      "year": 2026,
      "title": "Sycophantic AI makes human interaction feel more effortful and less satisfying over time",
      "authors": "Lujain Ibrahim, Franziska Sofia Hafner, Myra Cheng, Cinoo Lee, Rebecca Anselmetti, Robb Willer, Luc Rocher, Diyi Yang",
      "venue": "arXiv 2605.07912 / working paper",
      "publication_date": "2026-05-08",
      "publication_status": "preprint",
      "peer_reviewed": false,
      "arxiv": "2605.07912",
      "url": "https://arxiv.org/abs/2605.07912",
      "axes": [
        "SPIRAL"
      ],
      "evidence": [
        "PREPRINT",
        "PREREGISTERED",
        "RANDOMIZED",
        "LONGITUDINAL",
        "HUMAN_SUBJECTS"
      ],
      "role": [
        "longitudinal sycophancy",
        "relational comparison",
        "human-side accumulation",
        "advice seeking",
        "social satisfaction",
        "memory-reset interaction"
      ],
      "reported_anchor": "Five preregistered studies (N=3,075; 12,766 human-AI conversations) include a three-week randomized study (N=1,364). Compared with neutral AI, sycophantic AI narrowed the AI-versus-close-others advice-seeking gap, increased feeling understood, and was associated with lower reported satisfaction with real-world social interactions. Chat history was reset after each conversation, so persistent model memory was not necessary for the observed longitudinal pattern.",
      "non_inference": "Preprint evidence does not establish clinical dependence, durable effects beyond the three-week protocol, population-wide incidence, displacement of human contact, or a model motive to please.",
      "status": "exceptional_preprint_preregistered_longitudinal"
    }
  ]
}