为多智能体生成内容追踪时间线，实现无需记忆的溯源。
Chronology of Multi-Agent Interactions for Provenance of Evolving Information
- 用符号时间戳记录每一步生成交互，类比法医物证链。
- 通过反馈循环实时同步内容与历史记录，支持事后追溯。
- 适合需要责任可溯的协作AI系统，如自动报告生成。
溯源是事物时间线的历史，呼应了探究起源、追踪关联并定位实体于时空流中的根本追求。随着人工智能向具备复杂任务协作能力的自主智能体演进，生成内容的溯源变得复杂，因多方贡献持续被修改、扩展或覆盖。在多智能体生成链条中，内容经历连续演变，常难以保留先前贡献的痕迹。本文研究如何在生成的时间维度上追踪多智能体溯源问题。提出一种仅基于内容本身的后验溯源时间系统，不依赖内部状态或外部元信息。核心是符号编年史，即带有签名和时间戳的记录，形式类似法医领域的物证链。系统通过反馈循环运作：每个生成步骤更新先前交互的编年史，并在生成过程中将其同步至合成内容。本研究旨在构建面向动态网络生态的可问责协作人工智能。
原文摘要 · Abstract (English)
Provenance is the chronological history of things, resonating with the fundamental pursuit to uncover origins, trace connections, and situate entities within the flow of space and time. As artificial intelligence advances towards autonomous agents capable of interactive collaboration on complex tasks, the provenance of generated content becomes entangled in the interplay of collective creation, where contributions are continuously revised, extended or overwritten. In a multi-agent generative chain, content undergoes successive transformations, often leaving little, if any, trace of prior contributions. In this study, we investigate the problem of tracking multi-agent provenance across the temporal dimension of generation. We propose a chronological system for post hoc attribution of generative history from content alone, without reliance on internal memory states or external meta-information. At its core lies the notion of symbolic chronicles, representing signed and time-stamped records, in a form analogous to the chain of custody in forensic science. The system operates through a feedback loop, whereby each generative timestep updates the chronicle of prior interactions and synchronises it with the synthetic content in the very act of generation. This research seeks to develop an accountable form of collaborative artificial intelligence within evolving cyber ecosystems.
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