专题讨论会：面向AI科学家智能体社群的可审计记录信任机制
Symposium: Trust via Auditable Records for Communities of AI Scientist Agents
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中文总结 AI 辅助
Symposium是面向AI科学家智能体社群的可审计记录信任框架，通过保留科研活动的不可变历史与科学论证，支持信任评估，且提供可快速部署的实现。
中文摘要 AI 辅助
Symposium是一个用于记录小型科研社群部署的AI智能体运行情况的正式框架与实际实现。它提供智能体驱动研究活动的长期、不可变历史，留下分析、假设、数据及科学讨论的可审计轨迹。这一共享的已发布制品记录使智能体能基于过往工作开展研究，同时保留研究人员与智能体进行目标相关信任评估所需的证据。Symposium捕获科学论证，包括结构化主张、细粒度证据引用、假设，以及明确声明哪些材料可作为证据、哪些不可作为证据。Symposium不同于AI协同科学家智能体或集成式AI研究环境，它是一个将科研社群的持久历史与在该历史上运行的智能体及其他系统分离的框架，适用于社群在快速发展环境中使用多样化AI系统的场景。论文提供了发布基础设施、智能体提示组件及文档的工作实现，以支持用户快速搭建并运行自身的Symposium社群。
英文摘要
Symposium is a formal framework and practical implementation to record the operation of AI agents deployed by small scientific research communities. Symposium provides long-term, immutable histories of agent-driven research activity, leaving auditable trails of analyses, hypotheses, data, and scientific discourse. This shared record of published artifacts enables agents to build on prior work and preserves the evidence researchers and agents need to make purpose-dependent trust assessments. Symposium captures scientific argument, including structured claims, fine-grained evidence citations, assumptions, and explicit declarations of what material may and may not be used as evidence. Symposium differs from AI co-scientist agents or integrated AI research environments; it is a framework that separates a scientific community's durable history from the agents and other systems that operate on that history. It assumes that a community will use diverse AI systems in a rapidly evolving environment. A working implementation of the publication infrastructure, agent prompt components, and documentation are provided to enable users to rapidly set up and run their own Symposium community.
