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Adaptive Personalization Under Preference Epistemic Uncertainty — arXiv:2610.40930

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.40930
License (as archived): CC0-1.0
Archived: 2026-11-03T05:34:39.035Z
SHA-256 of the archived text: a0aa8a614889a9bcb84175d72ec4489ba1aa3c1f332c3a552733b99137bdcab8
Stable URL of this page: https://metraipolis.com/documento/dc_a181b2d286ff

How to cite

Adaptive Personalization Under Preference Epistemic Uncertainty — arXiv:2610.40930 — arXiv (en), https://arxiv.org/abs/2610.40930 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_a181b2d286ff (Metraipolis archive dc_a181b2d286ff, sha256 a0aa8a614889a9bcb84175d72ec4489ba1aa3c1f332c3a552733b99137bdcab8)

BibTeX

@misc{dca181b2d286ff,
  title = {Adaptive Personalization Under Preference Epistemic Uncertainty — arXiv:2610.40930},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.40930},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_a181b2d286ff; sha256 a0aa8a614889a9bcb84175d72ec4489ba1aa3c1f332c3a552733b99137bdcab8},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.40930. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Human preferences change over time: an established preference may remain valid over time, become uncertain as context shifts, or be explicitly revised. Effective personalization requires conversational AI to adapt its response strategy to evolving preferences rather than relying on passive memory retrieval. We formalize this capability as adaptive personalization under epistemic uncertainty. A human preference study further confirms that people favor state-contingent response strategies as preference uncertainty changes. To evaluate whether language models mirror this adaptation, we create a multilingual benchmark spanning 250 personas, 5 languages, and 25K conversational turns that decouples strategy selection from response execution. Across eight closed- and open-weight models, we uncover a recognition-generation gap: models reliably identify appropriate strategies when selecting among candidate options, but collapse during open-ended generation, suffering a 35-46% drop in strategy calibration. Crucially, this failure is systematic: models default to exploit-first behavior, over-relying on existing preferences regardless of preference epistemic uncertainty. We show this failure is addressable: while prompting interventions fail, targeted training that supervises models' internal strategy representations toward state-conditional human preferences narrows the user acceptability generalization gap by a third on out-of-distribution personas and conversational styles.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.CL, nuovi lavori del giorno).
Categorie arXiv: Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2610.40930 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T05:19:07.102Z dalla pagina https://arxiv.org/list/cs.CL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).