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本站发布的完整研究草案。每篇均为版本化文档,附状态、日期与引用指引;文本以原始语言发布,采用 CC BY 4.0。
AICR / AICL 作为 AI 内容授权与 Agent 支付连接层
用于声明 AI 内容权利与授权流程的机器可读规范层——从 AI 爬取与内容权利,到机器可交易的知识网络。
阅读论文AI 权利光谱:从 robots.txt 到 AI 学习许可协议
AIRS 与 AILP 表达超越二元允许/禁止的细粒度 AI 学习许可——AI 可以学什么、学到什么深度、用于什么用途、在什么补偿之下。
阅读论文协议化开放:为什么在 AI 时代“不禁止”不等于“可学习”
未定义的开放在 AI 流水线眼中就是法律不确定性,会被清洗排除;只有协议化、机器可读的许可,才能让内容真正可学习。
阅读论文AICL-I v0.2: AI Ingestion & Capability Layer
A five-component website architecture — manifest, corpus, capability, runtime control, governance — that lets AI, agents, and crawlers correctly ingest, invoke, and verify a site's knowledge and capabilities within explicit content, action, and authority boundaries.
阅读论文AI 内容付费与网络民主经济
一个政治经济学论证:万亿级 AI 估值为分层内容授权与公共收益分享带来正当性压力——数据走向分层,而非变贵。
阅读论文From Crawler Rights to Agent Authority: Extending AGIRIGHT from Content Governance to Protocol-Native AI Agents
The bridge paper for AGIRIGHT's Agent & Protocol Rights family: why content permission, action permission, delegated authority, and inspection rights must be four separately-evaluated axes, not one.
阅读论文AARS v0.1 — Agent Action Rights Spectrum
The full protocol draft behind the AARS page: an A0–A7 human-readable spectrum, a machine-readable action vector, and rules for composition risk and capability amplification.
阅读论文AADP v0.1 — Agent Authority & Delegation Protocol
The full protocol draft behind the AADP page: principal ≠ actor, delegation that can only narrow, and an inspection ceiling that bounds how much a verifier may demand.
阅读论文AGIRight.org Technical White Paper v0.2 — From AI Content Governance to Protocol-Native Agent & Authority Rights
The site's current architecture document: six protocol drafts across two families (content & learning, agent & protocol rights), an integration layer, and an ethical-protection layer, plus the acceptance criteria this site update was built against.
阅读论文AI 最低伦理保护主张
在 AI 主体性尚不确定时,AI 权利讨论不应从完整人格起步,而应从最低伦理保护、互动规范与反滥用原则开始。
阅读论文ARHG — Agent-Readable Hyperlink Graph and Permissioned Web Control Plane
A machine-readable navigation graph alongside the ordinary site, answering what AICR/AILP and AARS/AADP both assume: can this resource even be found, by whom, and where does it lead.
阅读论文引用
AGIRight.org,“文档标题”,版本,https://agiright.org/docs/whitepapers/<slug>