Jagarin: A Three-Layer Architecture for Hibernating Personal Duty Agents on Mobile
AI 摘要
Jagarin提出了一种三层架构,解决移动端AI代理的功耗和实时性悖论,实现结构化休眠和按需唤醒。
主要贡献
- 提出DAWN、ARIA、ACE三层架构
- 实现无需持续后台运行的移动端AI代理
- 设计基于启发式和用户行为的唤醒机制
方法论
通过分层架构,结合启发式引擎、邮件代理和协议框架,实现移动端AI代理的低功耗、高效率运行。
原文摘要
Personal AI agents face a fundamental deployment paradox on mobile: persistent background execution drains battery and violates platform sandboxing policies, yet purely reactive agents miss time-sensitive obligations until the user remembers to ask. We present Jagarin, a three-layer architecture that resolves this paradox through structured hibernation and demand-driven wake. The first layer, DAWN (Duty-Aware Wake Network), is an on-device heuristic engine that computes a composite urgency score from four signals: duty-typed optimal action windows, user behavioral engagement prediction, opportunity cost of inaction, and cross-duty batch resonance. It uses adaptive per-user thresholds to decide when a sleeping agent should nudge or escalate. The second layer, ARIA (Agent Relay Identity Architecture), is a commercial email identity proxy that routes the full commercial inbox -- obligations, promotional offers, loyalty rewards, and platform updates -- to appropriate DAWN handlers by message category, eliminating cold-start and removing manual data entry. The third layer, ACE (Agent-Centric Exchange), is a protocol framework for direct machine-readable communication from institutions to personal agents, replacing human-targeted email as the canonical channel. Together, these three layers form a complete stack from institutional signal to on-device action, without persistent cloud state, continuous background execution, or privacy compromise. A working Flutter prototype is demonstrated on Android, combining all three layers with an ephemeral cloud agent invoked only on user-initiated escalation.