{
  "abstract": "Current generative AI alignment relies on a myopic paradigm that prioritizes immediate conversational satisfaction. By relying on short-horizon feedback, this approach ignores the profound, longitudinal externalities of persistent human-AI interaction, which can lead to adverse outcomes such as cognitive atrophy, social erosion, and diminished human agency. To address this, we propose a paradigm shift toward long-horizon alignment that accounts for how AI reshapes human experience over time. To this end, we map the tensions between near-term utility and long-term costs of AI use across four facets of human well-being to reveal outcomes that current reward models fail to capture. Ultimately, we advocate for moving beyond simple preference optimization toward alignment strategies that actively foster long-term human well-being by navigating these complex temporal scales.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.06830",
  "author_total": 6,
  "authors": [
    {
      "name": "Remi Denton"
    },
    {
      "name": "Aida Davani"
    },
    {
      "name": "Cynthia L Bennett"
    },
    {
      "name": "Michael Madaio"
    },
    {
      "name": "Roma Patel"
    },
    {
      "name": "Himabindu Lakkaraju"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/92pr3m8swwqzm.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.06830",
  "fulltext_url": null,
  "id": "92pr3m8swwqzm",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.06830",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.CY"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.ML"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.06830",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-04",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/92pr3m8swwqzm.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T15:21:06Z",
      "native_id": "2611.06830",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.06830"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Alignment Myopia in Generative AI",
  "title_text": "Alignment Myopia in Generative AI",
  "type": "preprint",
  "updated_at": "2026-11-09T00:20:32Z",
  "url": "https://research.draconitic.com/paper/92pr3m8swwqzm",
  "venue": "arXiv",
  "versions": []
}