发表机构
Griffith University; CSIRO（格里菲斯大学; 澳大利亚联邦科学与工业研究组织）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有时间序列语言模型无法在交互中实时处理新观测的问题，提出TimeInteract，通过双视角编码、响应控制和解耦推理实现流式交互，在StreamTSI-34K数据集上显著超越现有模型。
AI 中文摘要
现实世界中的时间序列持续演化，有意义的变动可能在任何时刻出现。然而，现有的时间序列语言模型（TSLMs）本质上是静态的。它们要么接收完整序列进行离线处理，要么在流式输入与响应生成之间交替进行，这导致在交互过程中无法处理新观测数据。我们提出了一种新的范式——时间序列交互（Time-Series Interaction）：模型持续感知传入的时间序列观测和用户意图，自主决定何时保持沉默或做出响应，并在响应生成期间继续处理新的观测数据。为实现这一目标，我们开发了TimeInteract，其包含三个关键设计：一个双视角流式时间序列编码器，用于捕捉局部变化和历史动态；一个响应控制机制，用于学习何时触发响应；以及一个解耦的流式推理机制，将控制与响应生成分离，以避免阻塞后续观测。我们进一步构建了一个交互能力层次结构，从理解（Understanding）逐步提升到适应性（Adaptivity）。基于该层次结构，我们构建了StreamTSI-34K，一个大规模流式时间序列交互数据集，包含34,588个会话和77,505个响应，覆盖合成和真实世界时间序列，并支持单轮和多轮设置。在所有四个交互层级上，TimeInteract均持续优于现有的LLMs、VLMs和TSLMs，在具有挑战性的任务上性能提升高达23.92个百分点。同时，它改善了响应触发机制，实现了接近零的流停滞，并获得了高达2.15倍的推理加速。
英文摘要
Real-world time series evolve continuously, with meaningful changes potentially emerging at any moment. However, existing time-series language models (TSLMs) remain inherently static. They either receive complete sequences for offline processing or alternate between streaming input and response generation, which prevents processing of new observations during interaction. We introduce a new regime, Time-Series Interaction: a model continuously perceives incoming time-series observations and user intent, autonomously decides when to remain silent or respond, and continues processing new observations during response generation. To realize this, we develop TimeInteract with three key designs: a dual-view streaming TS encoder that captures local variations and historical dynamics, a response control mechanism that learns when to trigger a response, and a decoupled streaming inference mechanism that separates control from response generation to avoid blocking subsequent observations. We further formulate a hierarchy of interaction capabilities, progressing from Understanding to Adaptivity. Based on this hierarchy, we construct StreamTSI-34K, a large-scale streaming TS interaction dataset with 34,588 episodes and 77,505 responses across synthetic and real-world time series in single- and multi-turn settings. Across all four interaction levels, TimeInteract consistently outperforms existing LLMs, VLMs, and TSLMs, with gains of up to 23.92 points on challenging tasks. It also improves response triggering while achieving near-zero stream stall and up to $2.15\times$ inference speedup.
