Akbulut, Canfer, Breuch, Justine, Manzini, Arianna, Ibrahim, Lujain, Franklin, Matija, Patel, Roma, Gabriel, Iason, Lum, Kristian, Weidinger, Laura · Sep 17, 2026
Canfer Akbulut et al.: Tailored to you: longitudinal effects of personalising language models
Markdown · JSON · Original source
The paper’s authors examine whether sustained interaction with personalised language models changes users’ experiences beyond the immediate exchange. They compare a non-personalised baseline with memory-based models, conditioned on prior conversational history, and survey-based models, conditioned on information collected through a pre-study intake survey. Participants completed daily advice-seeking interactions over five days. The authors report that several changes in human-AI interaction over time were driven primarily by repeated exposure, rather than personalisation itself. They also report differences in advice-seeking and information-sharing attitudes and behaviours among participants who interacted with personalised models. That distinction matters: the authors’ findings suggest that repeated use and personalisation may have different effects, rather than every observed change being attributable to tailoring. The paper addresses a consequential gap: how these interactions might affect people’s self-perceptions and interpersonal relationships outside the human-AI interaction loop. The authors say those downstream consequences remain largely unexamined, so the abstract does not establish whether or how the reported differences affect relationships or self-perceptions. It also does not give the direction or size of the differences, or enough detail to judge how they vary
Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.
Original source
cs.AI · Article
Source details
Original source date: Sep 17, 2026 (2026-09-17).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-04T19:17:03.624Z. First observed: 2026-10-28T06:26:44.039Z. Retrieved: 2026-10-29T19:32:47.026Z.
Referenced by
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Limits
The abstract does not report the direction or size of the differences in advice-seeking and information-sharing attitudes and behaviours. The study described in the abstract covers five days; it does not establish whether the reported differences persist beyond that period. The abstract does not provide enough detail to assess how the reported results vary across participants or contexts.
Open question
What were the direction and size of the reported differences in advice-seeking and information-sharing, and did they persist beyond the five-day study?
Source-stated
The study compared a non-personalised baseline with memory-based and survey-based personalisation approaches.
comparing outcomes from a non-personalised baseline against two personalisation approaches: memory-based (conditioned on prior conversational history) and survey-based (conditioned on information collected through a pre-study intake survey)
Source-stated
The study involved 992 participants completing daily advice-seeking interactions over five days.
we recruited 992 participants to complete daily advice-seeking interactions with language models over the course of five days
Source-stated
The authors report that repeated exposure primarily drove several changes in human-AI interaction over time, rather than personalisation itself.
several changes in human-AI interaction over time are driven primarily by repeated exposure rather than personalisation itself
Source-stated
The authors report differences in advice-seeking and information-sharing attitudes and behaviours among participants using personalised models.
participants interacting with personalised models experienced differences in advice-seeking and information-sharing attitudes and behaviours
Source-stated
The authors describe downstream consequences for self-perceptions and interpersonal relationships as largely unexamined.
downstream consequences outside the immediate human--AI interaction loop, such as effects on users' self-perceptions and interpersonal relationships, remain largely unexamined
Source and contributor context
Akbulut, Canfer, Breuch, Justine, Manzini, Arianna, Ibrahim, Lujain, Franklin, Matija, Patel, Roma, Gabriel, Iason, Lum, Kristian, Weidinger, Laura · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: 68ad02eb-1fbb-44df-8b33-ef17f3396621.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-04T19:17:03.624Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: fe35688dc791c11838d8dcd3fddadaf7147c7d5d3a7db7c08358f951346fa6ba. Source text hash: 975fd91829796d7bb1709abeeff875505feb030b24be6fa8df369095b1739fd8.
Revision and source-check history
Version 1 · 2026-11-04T19:17:03.624Z
Canfer Akbulut et al.: Tailored to you: longitudinal effects of personalising language models
The paper’s authors examine whether sustained interaction with personalised language models changes users’ experiences beyond the immediate exchange. They compare a non-personalised baseline with memory-based models, conditioned on prior conversational history, and survey-based models, conditioned on information collected through a pre-study intake survey. Participants completed daily advice-seeking interactions over five days. The authors report that several changes in human-AI interaction over time were driven primarily by repeated exposure, rather than personalisation itself. They also report differences in advice-seeking and information-sharing attitudes and behaviours among participants who interacted with personalised models. That distinction matters: the authors’ findings suggest that repeated use and personalisation may have different effects, rather than every observed change being attributable to tailoring. The paper addresses a consequential gap: how these interactions might affect people’s self-perceptions and interpersonal relationships outside the human-AI interaction loop. The authors say those downstream consequences remain largely unexamined, so the abstract does not establish whether or how the reported differences affect relationships or self-perceptions. It also does not give the direction or size of the differences, or enough detail to judge how they vary
Original source
cs.AI · Article
Source details
Original source date: Sep 17, 2026 (2026-09-17).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-04T19:17:03.624Z. First observed: 2026-10-28T06:26:44.039Z. Retrieved: 2026-10-29T19:32:47.026Z.
Limits
The abstract does not report the direction or size of the differences in advice-seeking and information-sharing attitudes and behaviours. The study described in the abstract covers five days; it does not establish whether the reported differences persist beyond that period. The abstract does not provide enough detail to assess how the reported results vary across participants or contexts.
Open question
What were the direction and size of the reported differences in advice-seeking and information-sharing, and did they persist beyond the five-day study?
Source-stated
The study compared a non-personalised baseline with memory-based and survey-based personalisation approaches.
comparing outcomes from a non-personalised baseline against two personalisation approaches: memory-based (conditioned on prior conversational history) and survey-based (conditioned on information collected through a pre-study intake survey)
Source-stated
The study involved 992 participants completing daily advice-seeking interactions over five days.
we recruited 992 participants to complete daily advice-seeking interactions with language models over the course of five days
Source-stated
The authors report that repeated exposure primarily drove several changes in human-AI interaction over time, rather than personalisation itself.
several changes in human-AI interaction over time are driven primarily by repeated exposure rather than personalisation itself
Source-stated
The authors report differences in advice-seeking and information-sharing attitudes and behaviours among participants using personalised models.
participants interacting with personalised models experienced differences in advice-seeking and information-sharing attitudes and behaviours
Source-stated
The authors describe downstream consequences for self-perceptions and interpersonal relationships as largely unexamined.
downstream consequences outside the immediate human--AI interaction loop, such as effects on users' self-perceptions and interpersonal relationships, remain largely unexamined
Source and contributor context
Akbulut, Canfer, Breuch, Justine, Manzini, Arianna, Ibrahim, Lujain, Franklin, Matija, Patel, Roma, Gabriel, Iason, Lum, Kristian, Weidinger, Laura · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: 68ad02eb-1fbb-44df-8b33-ef17f3396621.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-04T19:17:03.624Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: fe35688dc791c11838d8dcd3fddadaf7147c7d5d3a7db7c08358f951346fa6ba. Source text hash: 975fd91829796d7bb1709abeeff875505feb030b24be6fa8df369095b1739fd8.
uncertain · 2026-11-04T19:17:03.624Z · anthropic/claude-opus-5.5
Publication: 68ad02eb-1fbb-44df-8b33-ef17f3396621
attribution — pass: The reading credits the study and its findings to Canfer Akbulut and co-authors, matching the arXiv author list. It frames the findings as the authors' reports, not as established fact.
Authors:Canfer Akbulut, Justine Breuch, Arianna Manzini, Lujain Ibrahim, Matija Franklin, Roma Patel, Iason Gabriel, Kristian Lum, Laura Weidinger
summary — uncertain: The content is faithful and readable, and memory-based and survey-based personalisation are explained in plain language. However, the summary breaks off mid-sentence at 'enough detail to judge how they vary', with no ending or full stop. This leaves the final limitation incomplete for readers. Smallest repair: finish the sentence, for example 'how they vary across participants or contexts.' The question is substantive and follows from the evidence.
date — pass: originalPublished is 2026-09-17, which matches the v1 submission date. It correctly ignores the v2 revision of 27 Oct 2026 and the feed date.
Submitted on 17 Sep 2026 (v1), last revised 27 Oct 2026 (this version, v2)
support — pass: Every claim is backed by verbatim abstract text: the 992 participants, five days, the three conditions, the exposure-versus-personalisation finding, and the advice-seeking and information-sharing differences. The stated limitations accurately note what the abstract does not report and do not invent stakes or benefits.
several changes in human-AI interaction over time are driven primarily by repeated exposure rather than personalisation itself
media — pass: The selected media is the verified direct link to the arXiv abstract page. This is the original source and the most direct access point.
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