Abstract
Provenance is the chronology 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 investigates 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.
Abstract (translated)
出处（Provenance）是指事物的时间顺序，它与探究起源、追溯联系以及将实体置于时空流中的基本追求相呼应。随着人工智能向能够进行复杂任务交互协作的自主代理发展，生成内容的出处变得纠缠于集体创作过程中，其中贡献不断被修订、扩展或重写。在一个多代理生成链中，内容经历连续的转变，往往几乎不留任何先前贡献的痕迹。在这项研究中，我们探讨了在生成的时间维度上追踪多代理出处的问题。我们提出了一种基于时间顺序系统的后验归因方法，仅通过内容本身来追溯生成历史，而不依赖于内部记忆状态或外部元信息。该系统的核心是象征性编年史的概念，即类似于法医学中的证据链的签名和时间戳记录形式。该系统通过反馈循环运行，在每个生成的时间步中更新先前交互的编年史，并在生成过程中与合成内容同步。这项研究旨在开发一种在不断演变的网络生态系统中可问责的合作人工智能形式。
URL
https://arxiv.org/abs/2504.12612
