China State Media Group Releases Technical Framework for AI-Friendly Media

A technical framework designed to make news and publishing content structured and machine-readable for generative artificial intelligence models was officially launched in Guiyang, China. Jointly developed by the technical department of People's Daily, the State Key Laboratory of Communication Content Cognition, and the China Newspaper Association, the standard seeks to transition media archives from human-facing displays into structured data foundations. The framework has submitted formal applications for national standard status, with its main specification and value corpus standard clearing initial reviews.

As generative AI engines and search answer interfaces increasingly replace traditional web browsing, media institutions face the risk of becoming unreadable or unindexed by foundation models. By creating technical standards across editorial pipelines, ontology engineering, and agent-to-agent communication, the initiative establishes a blueprint for structuring authoritative journalism into compliant training and retrieval datasets for AI systems.

A technical framework aimed at standardizing how media organizations prepare and structure content for generative artificial intelligence systems was officially unveiled at the China International Big Data Industry Expo in Guiyang, according to a report by People's Daily Online. The framework, titled Technical Specifications for Generative Artificial Intelligence-Friendly Media, was jointly formulated by the Technical Department of the People's Daily, the State Key Laboratory of Communication Content Cognition, and the China Newspaper Association. The initiative aims to transform high-value journalistic data into structured knowledge bases, shifting institutional media from simply being read by human audiences to being accurately understood and cited by AI systems. Speaking at the launch event, Wang Hudong, chief engineer of the People's Daily Technical Department, emphasized that AI-powered search and conversational query systems are rapidly replacing conventional entry points for public information retrieval. Mainstream media must now fulfill a dual role: producing quality editorial products for human consumption while simultaneously serving as a structured data foundation for automated model ingestion. The framework adopts a modular '1+N+X' design structure, comprising one primary standard, multiple sub-standards, and domain-specific implementation annexes. The primary specification establishes technical architecture, semantic rules, security protocols, and evaluation systems. Supporting it are four core sub-specifications: metadata standards covering the full reporting and publishing lifecycle; construction and safety-audit guidelines for mainstream value corpora; protocol-adaptation requirements for Agent-to-Agent (A2A) and AI-Protocol (AIP) inter-agent platforms; and knowledge ontology specifications designed to power knowledge graphs. Four initial implementation annexes have also been drafted to address distinct media operational scenarios. The framework has already been applied within the People's Daily's internal service platform. Applications have been formally submitted to the National Data Standards Committee to establish the specifications as national standards, with both the primary standard and the value corpus specification having passed initial technical assessments.