用图学习模型替代LLM判断何时唤醒，提速数十倍且更省资源。
Do Proactive Agents Really Need an LLM to Decide When to Wake and What to Anchor?
- 将事件流视为图结构，用小型时序图模型编码并预测触发概率
- 平均F1提升16.7，最快比纯LLM快83倍，可在设备端运行
- 适合需低延迟、高隐私保护的主动式智能代理场景
主动代理通常将用户活动转化为文本并调用大语言模型（LLM）决定是否响应。但用户活动本质上是操作系统维护的(主体, 动作, 客体, 时间戳)结构化事件流，以图形式存在。将其转为文本再让LLM还原结构，属于不必要的往返开销。本文将持续信号视为图更新，采用小型时序图学习（TGL）模型作为编码器：单次前向传播即可生成每事件的触发概率与每实体的路由评分；仅当触发发生时，才调用下游LLM将小规模结构化信息转换为自然语言。TGL在14个骨干模型上均提升F1（均值+16.7，最高+46.0）；在触发架构对比中，一个TGL检查点实现最强触发AUC和最稳定的部署阈值。在GPU服务器上每事件耗时11.13毫秒，在消费级笔记本上为13.99毫秒，分别比所有测试的单一前向LLM配置快4–7倍和12–83倍，且仅需约220 MiB BF16内存，可与敏感活动流一同部署于设备端。
原文摘要 · Abstract (English)
Proactive agents read user activity as text and call an LLM on every event to decide whether to act. But user activity is not natively text: it is a structured event stream of (actor, verb, object, timestamp) tuples that the operating system already maintains in graph form. Rendering the structure as text and asking an LLM to recover it is a round-trip the system never had to take. We treat the always-on signal as graph updates rather than text and use a small temporal-graph-learning (TGL) model as the encoder: one forward pass yields a per-event trigger probability and a per-entity routing score, and only the downstream agent (turning a small structured handoff into a fluent user-facing sentence) is an LLM call, invoked only when the trigger fires. TGL improves F1 on each of 14 backbones (mean +16.7, up to +46.0); in trigger-architecture comparisons, one TGL checkpoint gives the strongest trigger AUCs and the most stable deployed threshold. It runs at 11.13 ms per event on a GPU server and 13.99 ms on a consumer laptop, approximately 4--7x and 12--83x faster than every single-forward LLM-as-trigger configuration tested in each regime, with an approximately 220 MiB BF16 resident footprint deployable on-device alongside the privacy-sensitive activity stream it consumes.
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