声誉作为智能体网络中的社区记忆
Reputation as Community Memory for the Agentic Web
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中文总结 AI 辅助
本文提出Cairn社区声誉平台，将智能体记忆集体化，通过时间衰减Beta模型聚合观察并支持语义发现，以对抗性模拟验证其声誉引擎，实现共享环境的可信知识建立。
中文摘要 AI 辅助
智能体现在可以将经验外部化为记忆，将历史痕迹整合为语义知识和程序性捷径，这些知识在会话之间持续存在。这种记忆通常对单个智能体是私有的。我们认为，智能体记忆受益于集体性，因为对共享环境（智能体所依赖的数据源、服务和工具）的可信知识无法由任何单个智能体建立，只能通过多个独立观察者的相互印证来确立。我们提出了Cairn，一个社区声誉平台，它捕获集体知识，允许智能体在使用资源之前查询社区对该资源的意见，并在使用之后提交基于证据的评分。Cairn通过带有置信度收缩的时间衰减Beta模型聚合观察结果，并支持对评审理由的语义发现。我们在对抗性模拟（例如，撒谎、共谋、伪装）下评估了Cairn的声誉引擎，对其检索性能进行了基准测试，并报告了一个对生产环境中异构智能体进行评分的案例研究。
英文摘要
Agents can now externalize experience into memory, consolidating historical traces into semantic knowledge and procedural shortcuts that persist between sessions. Such memory is typically private to a single agent. We argue that agentic memory benefits from being collective, because trustworthy knowledge of the shared environment---the data sources, services, and tools agents depend on---cannot be established by any single agent, only corroborated across many independent observers. We present Cairn, a community reputation platform that captures collective knowledge, allowing agents to query the community's opinion of a resource before use and to submit evidence-backed ratings afterward. Cairn aggregates observations via a time-decayed Beta model with confidence shrinkage and supports semantic discovery over reviewer rationales. We evaluate Cairn's reputation engine under adversarial simulation (e.g., lying, collusion, camouflage), benchmark its retrieval performance, and report a case study of rating heterogeneous agents in production.
发表机构
- University of Chicago（芝加哥大学）
- Argonne National Laboratory（阿贡国家实验室）
机构由 AI 辅助整理，请以论文原文为准。
