这篇论文提出了PLACEMEM，用版本化胶囊解决终身智能体的记忆管理问题，原型跑在vLLM上，实测了延迟和复用率，适合研究记忆架构的读者。
PLACEMEM将智能体记忆表示为带版本号的胶囊，统一语义、来源、有效性和可重用运行时状态。原型基于vLLM，支持提示级文本检索、KV导向路由和级联失效。通过OpenAI兼容的侧车和类型化元数据契约，测量了首次token延迟、复用率和校正后行为。论文提出了面向终身智能体系统的重放感知服务集成路线图。
PLACEMEM: Toward a Compute-Aware Memory Plane for Lifelong Agents
Lifelong agents need more than larger context windows and better retrieval. They need memories that can persist, evolve, and be corrected without forcing the serving stack to recompute the same history on every turn or silently reuse stale runtime state. We present PLACEMEM as a systems position on lifelong-agent memory, instantiated by an executable control-plane prototype. The central claim is that agent memory should be represented as versioned capsules that unify semantics, provenance, validity, and reusable runtime state under one correction-aware identity. In the current prototype, capsules drive prompt-level text retrieval, KV-aware routing, and cascading invalidation over live streamed backends; prospective layer-frontier replay is intentionally framed as a deeper integration agenda rather than a claimed engine feature. We describe a vLLM-first prototype with persistent capsule state, concurrency-safe invalidation, an OpenAI-compatible routing sidecar, a typed metadata contract, and a benchmark harness that measures live first-token latency, reuse, and post-correction behavior. The result is both an executable artifact that demonstrates correction-aware control-plane behavior today and a concrete roadmap for replay-aware serving integration in future lifelong-agent systems.