Perseus：基于状态记忆的稀疏监督交互式时间序列分割
Perseus: Interactive Time Series Segmentation with Sparse Supervision via Stateful Memory
AI总结：
针对现有交互式时间序列分割方法无法保留用户交互历史的问题，提出 Perseus 框架，通过写-读架构实现异步状态管理，在多粒度设置中准确率提升最高达 85%。
摘要：
从工业制造到可穿戴医疗保健等现实系统，会产生包含从粗粒度状态到细粒度事件的分层状态的多变量时间序列。与零样本或少样本分割不同，本研究的设定使用密集状态标签进行模型训练。稀疏专家提示提供推理时的修正，可在不重新训练的情况下解决序列特定的歧义。在实践中，这种反馈围绕选定的事件或转换分组，留下时间线的大部分未被提示。此处评估的基于提示的滑动窗口基线对用户交互历史是无状态的：它们仅在当前窗口内使用引导，无法在这些间隙中保留引导。为解决此问题，我们提出 Perseus（Persistent Segmentation with User Supervision，即带用户监督的持久分割），这是一个从同步处理过渡到异步状态管理的框架。Perseus 通过独特的写-读架构将监督与推理解耦：分组的用户线索被异步编码到持久记忆库（写入）中，随后推理引擎主动查询该记忆库（读取）以服务未被提示的窗口。该机制通过基于全局交互历史而非仅基于局部输入来调节预测，从而弥合监督间隙。在六个数据集上进行的大量实验表明，尽管评估的无状态提示基线在分组监督下显著退化，但 Perseus 保持了鲁棒性，并在多粒度设置中实现了高达 85% 的准确率提升。代码和预处理说明可在此 https URL 获取。
英文摘要：
Real-world systems, ranging from industrial manufacturing to wearable healthcare, generate multivariate time series with hierarchical states ranging from coarse regimes to fine-grained events. Unlike zero- or few-shot segmentation, our setting uses dense state labels for model training. Sparse expert prompts provide inference-time corrections that resolve sequence-specific ambiguities without retraining. In practice, this feedback is grouped around selected events or transitions, leaving large portions of the timeline unprompted. The prompt-based sliding-window baselines evaluated here are stateless with respect to user interaction history: they use guidance only within the current window and cannot retain it across these gaps. To address this, we propose Perseus (Persistent Segmentation with User Supervision), a framework that transitions from synchronous processing to asynchronous state management. Perseus decouples supervision from inference via a distinct Write-Read architecture: grouped user cues are asynchronously encoded into a persistent memory bank (Write), which is then actively queried by the inference engine (Read) to service unprompted windows. This mechanism bridges supervision gaps by conditioning predictions on a global history of interactions rather than solely on local inputs. Extensive experiments on six datasets demonstrate that while evaluated stateless prompting baselines degrade significantly under grouped supervision, Perseus maintains robustness and achieves up to 85% accuracy improvement in multi-granularity settings. Code and preprocessing instructions are available at https://github.com/blacksnail789521/Perseus.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
