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arXiv · 2601.17676

GazeSummary: Exploring Gaze as an Implicit Prompt for Personalization in Text-based LLM Tasks

Jiexin Ding·Yizhuo Zhang·Xinyun Liu·Ke chen·Yuntao Wang·Shwetak Patel·Akshay Gadre
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Abstract

Smart glasses are accelerating progress toward more seamless and personalized LLM-based assistance by integrating multimodal inputs. Yet, these inputs rely on obtrusive explicit prompts. The advent of gaze tracking on smart devices offers a unique opportunity to extract implicit user intent for personalization. This paper investigates whether LLMs can interpret user gaze for text-based tasks. We evaluate different gaze representations for personalization and validate their effectiveness in realistic reading tasks. Results show that LLMs can leverage gaze to generate high-quality personalized summaries and support users in downstream tasks, highlighting the feasibility and value of gaze-driven personalization for future mobile and wearable LLM applications.

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Jiexin Ding, Yizhuo Zhang, Xinyun Liu, Ke chen, Yuntao Wang, Shwetak Patel, Akshay Gadre. 2026-01-25. GazeSummary: Exploring Gaze as an Implicit Prompt for Personalization in Text-based LLM Tasks. https://doi.org/10.1145/3789514.3792037

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