Preference-Driven Online Adaptation for Personalized Interaction Initiation in Proactive AI Assistants
3.40T1 sourcearXiv cs.HC
Source record
Published by arXiv cs.HC (T1 source). The original is at https://arxiv.org/abs/2608.04416.
Pipeline notes
The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.
SummaryPaper proposing EOPA, a method for proactive AI assistants to decide when to initiate interactions versus remain silent. It uses temporal anchors and activity prototypes as evidence carriers, adapting from online feedback without LLM retraining. Reported gains: +19.8 F1 over baseline, adaptation time cut from 11.41s to 0.39s.
Why it mattersConcrete method and measured results for the interaction-timing problem in proactive assistants, useful for anyone building agent-driven workflows that need to decide when to speak up.
Cited by
No citations on record.
