AI 中文总结
针对工具使用智能体在分布式系统中获取证据时面临的过期和故障域问题，提出保障感知语义调度（AAS）方法，结合整数规划与时间调度，显著提升有效候选数量并减少过期候选，在模拟中验证了有效性。
AI 中文摘要
使用工具的人工智能智能体可以发起具有重大影响的基础设施变更，然而，准入所需的证据可能在其它检查运行期间过期，或者依赖于共享的故障域。我们将证据获取问题形式化为在法定人数、多样性、新鲜度、截止时间和资源约束下的联合见证者选择与调度问题。保障感知语义调度（AAS）结合了整数规划选择、调度感知的时间调度、有界诊断扩展和收据感知修复。形式化结果阐述了调度时新鲜度和有限诊断扩展所需的假设。在三个生成的基础设施工作负载中，AAS 产生了 1,075/1,200 个有效候选，而约束感知的前向调度为 647/1,200；过期候选从 440 个减少到 12 个。配对敏感性研究在参数设置中重复使用相同的实例和操作延迟抽样。修正的超时干预在 18/20 的准入中通过修复或完全重新合成成功，而静态计划为 0/20，并且在收据被重用的情况下降低了承诺成本。在 20 个需要认证分解切割的构造案例中，细化每次都能恢复与预言机匹配的可行计划。这些是受控模拟结果；有界预言机共享一个时间搜索组件，并且向部署系统的迁移仍未经过测试。
英文摘要
Tool-using agents can initiate consequential infrastructure changes, yet evidence required for admission may expire while other checks run or depend on a shared fault domain. We formulate evidence acquisition as joint witness selection and scheduling under quorum, diversity, freshness, deadline, and resource constraints. Assurance-Aware Semantic Scheduling (AAS) combines integer-program selection, dispatch-aware temporal scheduling, bounded diagnostic expansion, and receipt-aware repair. Formal results state the assumptions needed for dispatch-time freshness and finite diagnostic expansion. In three generated infrastructure workloads, AAS produces 1,075/1,200 valid candidates versus 647/1,200 for constraint-aware forward scheduling; stale candidates fall from 440 to 12. Paired sensitivity studies reuse the same instances and operation latency draws across parameter settings. A corrected timeout intervention finds 18/20 admissions with repair or full resynthesis versus 0/20 for a static plan, with lower committed cost when receipts are reused. On 20 constructed cases requiring a certified decomposition cut, refinement recovers an oracle-matching feasible plan every time. These are controlled simulation results; the bounded oracle shares a temporal search component, and transfer to deployed systems remains untested.
Comments30 pages, 5 figures, 3 tables. Extended manuscript with formal proofs and operation catalogue
