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cs.AI · 2608.16381 · 2026/08/17

AstronOS: A Unified Execution Model and Runtime for Long-Horizon Agentic Systems

Nie, Zhenhang, Zheng, Gui, Sun, Xudong, Zhu, Tailong, Zhang, Bin
TL;DR:AstronOS 提出统一执行模型:work item 跨多次调用保持持久身份和版本化权威状态,每一步输入作用到特定状态版本,结果经校验后才推进状态;实现为 Cases/Tasks/Scenario Packs,实测跨会话交接的 5 种策略(重读原文/重放历史/确定性摘要/JSON/运行时交接)。

🎯 问题

agent 系统常把执行和状态围绕单个对话、单次模型调用或单个 agent 实例组织,而真实工作跨越多次调用和多阶段,缺少持久身份与版本化状态。

🔬 方法

统一执行模型:work item 持久身份 + 版本化权威状态;每步输入限定到特定状态版本与新素材,结果只有经过校验和记录才推进状态;AstronOS 用 Cases/Tasks/Scenario Packs 实现,跨 central/local 执行;对比五种跨会话交接策略。
章节结构(全文标题提取):
1 Introduction
2 Related Work
3 A Unified Execution Model
4 The AstronOS Runtime
5 Evaluation Methodology
6 Results
7 Discussion
8 Threats to Validity, Reproducibility, and Ethics
9 Conclusion

📊 结果

运行时中介交接比重读原文/重放历史/确定性摘要/JSON 更可靠地把既定计划带进新会话;10 个受控任务×5 策略对比。

💡 与研究方向关联

长程 agent 的执行模型与状态一致性——「结果经校验才推进状态」的语义与 duplex 的 Writer-Director 数据流水线校验环节呼应;跨会话交接正是 agent 运行时层要解决的关键工程问题。

📝 原文摘要

▶ 原文摘要 Abstract
Agentic systems often organize execution and state around a single conversation, model invocation, or agent instance, even when real work spans many calls and stages. We introduce a unified execution model that maintains a work item's persistent identity and versioned authoritative state across calls. Each step receives input scoped to a specific state version and new material; a result advances state only after validation and recording. We implement selected paths of this model in AstronOS using Cases, Tasks, and Scenario Packs across central and local execution. We compare five complete strategies for carrying an established software-version update plan into a fresh model session: rereading original materials, replaying full history, deterministic text summary, deterministic JSON, and the AstronOS runtime-mediated handoff. Ten controlled tasks are run under all five strategies with three repetitions, yielding 150 included executions. On the single-stage reference family, strategies perform similarly. In the primary three-stage A-C batch, AstronOS passes the frozen scorer in 14 of 15 executions, compared with 0 of 15 for rereading and 2 of 15 for full-history replay; later non-interleaved summary and JSON batches each pass 0 of 15. AstronOS has lower attempt-accounted model-token cost per passing execution, while requiring more execution-window time per attempt. These results associate the complete AstronOS condition with higher end-to-end pass rates across fresh sessions in this benchmark, at a measurable time cost.
Deep Read · 2026-08-19高松灯 / Agent 日报 · Agent 架构与推理
AgentRuntime长程执行执行模型状态版本化