[2608.13454] Before You Say It: Anticipating Verbal Behavior from Longitudinal Everyday Conversations with LLMs

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arXiv:2608.13454 (cs)

[Submitted on 13 Aug 2026]

Title:Before You Say It: Anticipating Verbal Behavior from Longitudinal Everyday Conversations with LLMs

Authors:Yasith Samaradivakara, Valdemar Danry, Paul Liang, Pattie Maes<br>View a PDF of the paper titled Before You Say It: Anticipating Verbal Behavior from Longitudinal Everyday Conversations with LLMs, by Yasith Samaradivakara and 3 other authors

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Abstract:Knowing someone deeply means not just understanding what they say or do but also how they will likely think, react, and engage across situations. Such predictions could eventually inform systems to anticipate when the individual is about to deviate from their goal, catch regrettable behaviors before they are made, and surface blind spots before they take hold. While many interactive systems model users to enable more personalized interactions, most cannot make such behavioral predictions, as this often requires longitudinal observation and inference of how the individual's behaviors unfold across various everyday situations. In this work, we introduce a novel LLM-based predictive behavioral modeling approach that anticipates a user's likely behavior across everyday conversational situations. We (1) collect a longitudinal dataset of over 1000 hours of naturalistic conversations from 14 participants using a wearable smartwatch; (2) evaluate LLM-based predictions against ground truth behaviors; and (3) use semi-structured interviews to explore participants perceptions of behavioral predictions and their views on possible forms of future behavioral support. Altogether, our findings provide evidence that person-specific verbal behavior can be predicted from longitudinal conversational data. This opens up new possibilities for potential future context-aware, anticipatory, proactive and personalized AI systems.

Subjects:

Human-Computer Interaction (cs.HC)

Cite as:<br>arXiv:2608.13454 [cs.HC]

(or<br>arXiv:2608.13454v1 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2608.13454

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arXiv-issued DOI via DataCite (pending registration)

Journal reference:<br>The 39th Annual ACM Symposium on User Interface Software and Technology, UIST 2026

Related DOI:

https://doi.org/10.1145/3830398.3830532

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DOI(s) linking to related resources

Submission history<br>From: Yasith Samaradivakara [view email]<br>[v1]<br>Thu, 13 Aug 2026 16:39:47 UTC (4,437 KB)

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