AI 中文总结
针对AI中介工作流中隐私传播外部性问题，提出PAPC平台调解机制，通过事件级拦截结合多信号决策，消除原始值暴露并保持任务完成。
AI 中文摘要
AI中介平台通过代表不同主体的LLM智能体协调工作。在这些工作流中，隐私损失可能在最终答案出现之前就已产生：一次内存写入、共享工作区更新、智能体间消息或工具事件可能会对另一主体施加下游暴露成本。我们将这种故障模式建模为隐私传播外部性，其中原始披露的成本取决于拓扑结构和扇出以及内容。我们提出PAPC，一种平台中介机制，它在信息移动事件更新共享状态或外部渠道之前拦截这些事件。PAPC结合策略、来源、拓扑/扇出、权限和内容信号，以允许事件、发布策略安全的抽象、隔离原始内容、阻止转换或缩小后续权利。该模型解释了为什么最终输出控制会遗漏中间暴露成本，以及为什么高扇出对象会放大传播。在检索内存和多智能体工作流基准测试中，PAPC保持了确定性的任务完成，并消除了测量的精确原始值和外部原始值暴露。结果将事件级调解定位为智能体中介在线工作的平台治理原语。
英文摘要
AI-mediated platforms coordinate work through LLM agents acting for different principals. In these workflows, privacy loss can be created before a final answer appears: a memory write, shared-workspace update, inter-agent message, or tool event may impose downstream exposure cost on another principal. We model this failure mode as a privacy-propagation externality, where the cost of a raw disclosure depends on topology and fanout as well as content. We present PAPC, a platform-mediated mechanism that intercepts information-moving events before they update shared state or external channels. PAPC combines policy, provenance, topology/fanout, privilege, and content signals to allow an event, release a policy-safe abstraction, quarantine raw content, block a transition, or narrow onward rights. The model explains why final-output control misses intermediate exposure costs and why high-fanout objects amplify propagation. Across retrieval-memory and multi-agent workflow benchmarks, PAPC preserves deterministic task completion and eliminates measured exact raw-value and external raw-value exposure. The results position event-level mediation as a platform-governance primitive for agent-mediated online work.
Comments25 pages, 1 figure
