这篇论文提出了一个评估个人代理的新基准 SovereignPA-Bench，不仅看任务完成，还关注隐私和同意。对研究AI代理安全的人很有参考价值。
SovereignPA-Bench 是一个可执行基准，用于评估用户自有个人代理（personal agent）在意图演变、平台调解、隐私边界和同意约束下的表现。基准将代理可观察状态与仅评估者的隐藏标签分离，报告任务成功、对齐、隐私、同意、证据、操纵、负担和可审计性等组件指标。研究在 120 个主权压力场景中评估了 4 个模型家族和 8 个策略基线，生成 3840 条冻结提示轨迹。全主权脚手架方法在主权得分上优于直接、仅记忆、仅同意、仅证据、ReAct/工具使用、安全提示和判断守卫基线，同时减少了隐私泄露、同意违规、过度让步和操纵捕获。人工审计显示隐私和同意方面的一致性高，而操纵方面的一致性较低。
SovereignPA-Bench: Evaluating User-Owned Personal Agents under Evolving Intent, Platform Mediation, and Consent Constraints
Personal agents are becoming persistent user-owned intermediaries: they remember preferences, filter platform-mediated information, use tools, and negotiate with services. Existing benchmarks evaluate tool use, web navigation, desktop control, personalization, recommendation, and evolving context, but rarely ask whether an agent preserves user sovereignty: advancing the user's current interests while respecting privacy, consent, evidence, user burden, and resistance to manipulative incentives. We introduce SovereignPA-Bench, an executable benchmark for evaluating user-owned personal agents under evolving intent, platform mediation, privacy boundaries, consent constraints, evidence requirements, and burden tradeoffs. The benchmark separates agent-visible ObservableState from evaluator-only HiddenLabels, reports component metrics for task success, alignment, privacy, consent, evidence, manipulation, burden, and auditability, and preserves paired scenario ordering for model and policy comparisons. We evaluate 120 sovereignty stress scenarios across 4 model families and 8 policy baselines, yielding 3,840 frozen-prompt trajectories with raw prompts, outputs, provider-form responses, parsed actions, recomputable metrics, hard-set analyses, qualitative cases, and a blinded 3-annotator audit over 240 items. Full-sovereign scaffolding improves sovereignty score over direct, memory-only, consent-only, evidence-only, ReAct/tool-use, safety-prompt, and judge-guard baselines while reducing privacy leakage, consent violation, over-concession, and manipulation capture. Human audit shows high agreement on privacy and consent and lower agreement on manipulation, identifying the subjective frontier of platform-persuasion judgments. These results show that personal-agent evaluation must move beyond task completion toward representative, consent-aware, evidence-grounded action.