为科研团队打造可审计的AI研究记录系统，保障科学可信度。
Symposium: Trust via Auditable Records for Communities of AI Scientist Agents
- 构建可长期保存、不可篡改的研究活动记录框架
- 支持对分析、假设、数据等的细粒度溯源与信任评估
- 适合需要透明协作的AI辅助科研团队快速部署
Symposium 是一个正式框架及实际实现，用于记录小型科研社区部署的AI代理的操作。它提供长期且不可更改的代理驱动研究活动历史，留下可审计的分析、假说、数据和科学讨论轨迹。这一共享的发布成果记录使代理能够基于先前工作继续推进，并保留研究人员与代理所需的信任评估证据。Symposium 记录科学论点，包括结构化声明、细粒度证据引用、假设以及对哪些材料可作为证据的明确定义。该系统不同于AI共同科学家代理或集成式AI研究环境，而是将科研社区的持久历史与操作该历史的代理及其他系统相分离。它假设社区将在快速演化的环境中使用多种AI系统。已提供出版基础设施、代理提示组件及文档的可运行实现，使用户能迅速搭建并运行自己的Symposium社区。
原文摘要 · Abstract (English)
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.
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