| 标题 |
Kairotask: Probing the Bridge Between Vague Intents and Spatiotemporal Contexts
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Abstract Effective personal task management hinges on anchoring abstract intentions to precise spatiotemporal retrieval contexts. However, the ambiguity of user inputs often fractures the link between intent and context, causing tasks to lie dormant due to missed execution windows. To address this, we introduce Kairotask. This mobile probe utilizes Large Language Models to proactively inject spatiotemporal metadata into ambiguous instructions. By introducing benign friction through an inference-confirmation loop, Kairotask assists users in concretizing execution contexts while reinforcing memory encoding. |
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(2025-6-4)