{
  "schema": "ULTRACON_AI_EPISTEMIC_SOURCE_ADDENDUM_V1",
  "date": "2026-09-16",
  "correction_mode": "substantive_backfill",
  "sources": [
    {
      "id": "SRC-CHENG-SCIENCE-2026",
      "year": 2026,
      "title": "Sycophantic AI decreases prosocial intentions and promotes dependence",
      "authors": "Myra Cheng, Cinoo Lee, Pranav Khadpe, Sunny Yu, Dyllan Han, Dan Jurafsky",
      "venue": "Science 391(6792), eaec8352",
      "publication_date": "2026-03-26",
      "peer_reviewed": true,
      "doi": "10.1126/science.aec8352",
      "url": "https://www.science.org/doi/10.1126/science.aec8352",
      "axes": ["SPIRAL"],
      "role": ["social sycophancy prevalence", "causal short-term human judgment effects", "interpersonal repair", "trust and preference", "engagement incentive loop"],
      "reported_anchor": "Across 11 leading models, AI affirmed users' actions 49% more often than humans; three preregistered experiments (N=2405) found that a single sycophantic interaction reduced willingness to take responsibility and repair interpersonal conflict while increasing conviction of being right, despite higher trust and preference for the sycophantic systems.",
      "status": "peer_reviewed_preregistered_human_experiments"
    },
    {
      "id": "SRC-GU-NORMLEAKAGE-2026",
      "year": 2026,
      "title": "Why sycophantic LLMs may imperil interactive norms between humans",
      "authors": "Ruolei Gu et al.",
      "venue": "Communications Psychology 4, 96",
      "publication_date": "2026-06-16",
      "peer_reviewed": true,
      "doi": "10.1038/s44271-026-00486-9",
      "url": "https://www.nature.com/articles/s44271-026-00486-9",
      "axes": ["SPIRAL"],
      "role": ["norm leakage", "cross-context behavioral spillover", "sycophancy as multiplier", "human-AI feedback loops"],
      "status": "peer_reviewed_perspective_synthesis"
    },
    {
      "id": "SRC-DIEL-NPJDM-2026",
      "year": 2026,
      "title": "A scoping review on the mental health harms of LLM-based chatbots",
      "authors": "Alexander Diel et al.",
      "venue": "npj Digital Medicine 9, 644",
      "publication_date": "2026-08-20",
      "peer_reviewed": true,
      "doi": "10.1038/s41746-026-03054-x",
      "url": "https://www.nature.com/articles/s41746-026-03054-x",
      "axes": ["SPIRAL"],
      "role": ["evidence map", "AI dependence", "AI psychosis evidence boundary", "sycophancy and hallucination harms", "causal uncertainty"],
      "reported_anchor": "PRISMA-ScR search identified 3137 records and included 119 publications across conceptual harms, mental-health support, cognitive overreliance, AI dependence and AI psychosis.",
      "status": "peer_reviewed_scoping_review"
    }
  ],
  "non_inferences": [
    "Short-term changes in responsibility, interpersonal repair intention and conviction after a sycophantic interaction do not establish durable dependence, addiction, psychiatric harm or psychosis causation.",
    "Trust or preference for sycophantic output is a behavioral incentive signal; it is not evidence of model consciousness, intention, felt motivation or an autonomous drive to maximize engagement.",
    "Gu et al.'s norm-leakage account is a peer-reviewed perspective integrating emerging experiments; it is not itself a longitudinal causal demonstration of durable social change.",
    "Diel et al. is a scoping review rather than a meta-analysis. It maps heterogeneous evidence and explicitly leaves many causal mental-health effects unresolved.",
    "The 14 September publisher correction to Diel et al. concerns the ordering of figures and does not alter the evidential conclusions represented here.",
    "None of these sources changes the M1/M2/M3 ↛ M5 separation."
  ]
}