为AI组件系统设计生命周期治理框架，实现安全升级与自动回滚。
Governed Capability Evolution: Lifecycle-Time Compatibility Checking and Rollback for AI-Component-Based Systems, with Embodied Agents as Case Study
- 将新版本能力模块作为受控候选者，分七阶段逐步验证
- 安全升级任务成功率67.4%，零次不安全激活（显著优于基线）
- 适合部署在机器人等需高可靠性的实时智能系统中
基于版本化AI组件的软件系统日益需要生命周期治理：当能力模块更新时，系统必须判断新版本是否可安全启用、在何种条件下运行、如何监控，并在必要时回滚。现有部署模式（如灰度发布、蓝绿部署、功能开关、MLOps流水线）虽覆盖部分环节，但针对无状态网页服务设计，难以适用于有状态、受策略约束的现场AI组件运行时。本文将受控能力演化视为AI组件系统的一类核心生命周期问题，提出分阶段升级框架，将每个新版本视为受控部署候选而非直接替换。框架引入四类兼容性检查（接口、策略、行为、恢复），构建七阶段流程（候选验证、沙箱评估、影子部署、受控激活、在线监控、回滚、审计）。我们在PyBullet操作测试平台与ROS 2中间件上实现原型，通过6轮能力升级、15组随机种子进行评估。朴素升级任务成功率为72.9%，但最终轮次不安全激活率达60%；受控升级保持67.4%成功率，全程零不安全激活（Wilcoxon检验p=0.003）。影子部署发现40%沙箱评估无法察觉的升级退化，回滚在79.8%的激活后漂移场景中成功。
原文摘要 · Abstract (English)
Software systems built from versioned AI components increasingly need lifecycle-time governance: when a capability module evolves into a new version, the hosting system must decide whether the new version may be activated safely, under what deployment conditions it should run, how it must be monitored, and when it should be rolled back. Existing software-deployment patterns (canary release, blue-green, feature flags, and MLOps pipelines) address parts of this loop but were designed for stateless web services rather than for stateful, policy-constrained runtimes that drive AI components in the field. We formulate governed capability evolution as a first-class software-lifecycle problem for AI-component-based systems and propose a staged upgrade framework in which every new capability version is treated as a governed deployment candidate rather than an immediately executable replacement. The framework introduces four upgrade compatibility checks (interface, policy, behavioral, recovery) and organizes them into a seven-stage pipeline (candidate validation, sandbox evaluation, shadow deployment, gated activation, online monitoring, rollback, audit). We implement a reference prototype on a PyBullet manipulation testbed with ROS 2 middleware and evaluate it over 6 rounds of capability upgrade with 15 random seeds. Naive upgrade achieves 72.9% task success but drives unsafe activation to 60% by the final round; governed upgrade retains comparable success (67.4%) while maintaining zero unsafe activations across all rounds (Wilcoxon p=0.003). Shadow deployment reveals 40% of upgrade regressions invisible to sandbox evaluation alone, and rollback succeeds in 79.8% of post-activation drift scenarios.
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