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arXiv Machine Learning By Sheng Pan, Yongli Gu, Yiqing Guo, Warren Jin, Bo Du, Shirui Pan, Ming Jin

TimeInteract: Towards Real-Time Interactive Intelligence for Streaming Time Series

Read the original on arXiv Machine Learning →

TimeInteract introduces a new regime called Time-Series Interaction, enabling models to continuously perceive incoming time-series data and user intent, decide when to respond, and keep processing new observations during response generation. The system employs a dual-view streaming encoder, a response control mechanism, and a decoupled inference pipeline to avoid blocking. Evaluated on the newly created StreamTSI-34K dataset, TimeInteract outperforms existing LLMs, VLMs, and TSLMs across four interaction levels, achieving significant gains in accuracy, response triggering, and inference speed.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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