PASK: Toward Intent-Aware Proactive Agents with Long-Term Memory
AI 摘要
PASK提出了一个面向现实世界的、具有长期记忆的意图感知主动智能体框架。
主要贡献
- 提出了DD-MM-PAS，一种通用的流式主动AI智能体范式。
- 实现了Pask智能体，包含IntentFlow、混合记忆和PAS基础设施。
- 构建了LatentNeeds-Bench，一个基于真实用户数据的评估基准。
方法论
构建了需求检测、记忆建模和主动智能体系统的三阶段框架，并使用IntentFlow模型进行需求检测，混合记忆实现长期记忆。
原文摘要
Proactivity is a core expectation for AGI. Prior work remains largely confined to laboratory settings, leaving a clear gap in real-world proactive agent: depth, complexity, ambiguity, precision and real-time constraints. We study this setting, where useful intervention requires inferring latent needs from ongoing context and grounding actions in evolving user memory under latency and long-horizon constraints. We first propose DD-MM-PAS (Demand Detection, Memory Modeling, Proactive Agent System) as a general paradigm for streaming proactive AI agent. We instantiate this paradigm in Pask, with streaming IntentFlow model for DD, a hybrid memory (workspace, user, global) for long-term MM, PAS infra framework and introduce how these components form a closed loop. We also introduce LatentNeeds-Bench, a real-world benchmark built from user-consented data and refined through thousands of rounds of human editing. Experiments show that IntentFlow matches leading Gemini3-Flash models under latency constraints, while identifying deeper user intent.