面向可靠重用的AI生成内容结构化框架
A Prompt-Aware Structuring Framework for Reliable Reuse of AI-Generated Content in the Agentic Web
AI总结：
本文提出一种结构化框架，通过在生成时自动附加元数据，提升AI生成内容的可追溯性和可靠性，以支持其安全重用。
摘要：
大型语言模型（LLMs）及其基于它们的软件代理（AI代理）的演进标志着从以人为中心的网络向由AI代理驱动的"智能网络"的转折点。然而，对于预计主导网络的AI生成内容（AIGC），目前尚无机制在生成过程中验证其可靠性、可重复性和许可合规性。这种透明度的缺乏可能通过AIGC的重用导致连锁幻觉和合规违规。因此，一个管理AIGC来源和生成条件的框架至关重要。本文提出一个框架，可在生成时自动附加结构化元数据到AIGC，包括模块化提示、上下文、思考、模型信息、超参数和置信度。该元数据与可验证凭证一起封装，以支持AIGC的可靠评估和重用。此框架可高效地整理结构化AIGC，并促进其在微调和知识蒸馏等应用中的安全使用。
英文摘要：
The evolution of Large Language Models (LLMs) and the software agents built on them (AI agents) marks a turning point in the transition from a human-centric Web to an ``Agentic Web'' driven by AI agents. However, for AI-Generated Content (AIGC), which is expected to dominate the Web, there is currently no mechanism for agents to verify its reliability, reproducibility, or license compliance during generation. This lack of transparency risks causing chained hallucinations and compliance violations through the reuse of AIGC. Consequently, a framework to manage the provenance and generation conditions of AIGC is essential. In this paper, we present a framework that automatically attaches structured metadata to AIGC at generation time, including modularized prompts, contexts, thoughts, model information, hyperparameters, and confidence. The metadata is enveloped together with verifiable credentials to support the reliable assessment and reuse of AIGC. This framework enables efficient curation of structured AIGC and facilitates its safe use for applications such as fine-tuning and knowledge distillation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
