China

Core Chinese internet-governance regulator for AI-related online information services. Primary-source materials show CAC co-issuing the 2023 Interim Measures for Generative AI Services, the 2025 AI-generated content labeling measures, the 2022 deep-synthesis rules, and the 2024 national AI standardization guide.

Central industry and telecom ministry with a major role in the China AI standards and evaluation stack. Primary sources show MIIT co-issuing CAC-facing AI service rules and the 2024 national AI standardization guide, and serving as the home ministry for affiliated institutions including CAICT and CESI.

China's main national cybersecurity standardization committee with an increasingly visible AI-security and AI-governance role. Official sources show AI-specific standards and guidance on generative AI service security, content labeling methods, AI safety governance, AI application ethics, and the 2026 formation of WG9 for AI security standards.

National information-technology AI subcommittee under the broader TC28 standards structure. Official sources show CESI as the secretariat unit and confirm the committee's role in AI standardization work.

MIIT-affiliated research institution that is central to China's applied AI governance, benchmarking, and industry-self-regulation stack. Official sources show CAICT's direct MIIT affiliation, its secretary-general-unit role in jointly founding AIIA, its role in launching the AI Risk Management System and AI Safety Benchmark through AIIA, and its Fangsheng benchmarking system with a dedicated safety testing dimension.

Needed because multiple official CAICT sources treat AIIA as the umbrella platform through which CAICT launched safety-governance structures, AI safety commitments, and AI safety benchmarks.

Best-evidenced through official CAICT reports rather than a dedicated committee page. Official CAICT sources state that CAICT, relying on AIIA, prepared a Safety Governance Committee in late 2023, released an AI Risk Management System, and later linked AIIA to AI safety commitments and the AI Safety Benchmark.

MIIT-affiliated standards institute with a central role in China's AI standards and model-evaluation infrastructure. Official sources place CESI as secretariat host for TC28/SC42 and show CESI releasing both a general large-model evaluation standard and the Qiúsuǒ national-standard benchmark system.

Official site evidence supports CSTC as an AI assessment actor rather than a pure standards drafter. The homepage lists AI evaluation and AIM AI management maturity assessment services, and the center's Sadie Lab activity stream includes an AI large-model safety-governance seminar and AI safety workgroup launch.

Major Shanghai AI research institution with visible roles in evaluation infrastructure, AI security standardization, and PR2G-imported frontier-risk framework work with Concordia AI.

Officially evidenced through a government-domain repost from Cyberspace Shanghai. The retrieved source states the lab was jointly established by SHLAB's Governance Research Center and the Shanghai Information Security Testing and Certification Center and unveiled at WAIC 2024.

Official SHLAB reporting attributes WG9's formal establishment to a TC260 notice and identifies SHLAB's director as group head, making WG9 a material node in the China AI security standards stack.

Open evaluation infrastructure from Shanghai AI Lab that explicitly includes safety as a model capability dimension.

Non-profit new-type AI R&D institution that is significant in China's model-evaluation and AI-safety dialogue ecosystem. Official sources show BAAI maintaining FlagEval as an open evaluation platform and hosting the Beijing AI Safety International Dialogue that produced the Beijing AI Safety Consensus.

BAAI’s public evaluation platform for large and foundation models, including safety and values dimensions.

Beijing's dedicated AI safety-and-governance institute appears to have evolved from a 2024 laboratory phase into a formally established institute in April 2025. Official sources also show a department structure that explicitly distinguishes AI Safety from AI Security and includes guardrails, ethics, and risk-response work.

Official Chinese institutional coverage positions CnAISDA primarily as a China-side dialogue network corresponding to an AI safety institute for international engagement, not automatically as a domestic frontier-model testing regulator. Tsinghua and SQZ sources explicitly identify CAICT, BAAI, and SHLAB as member units.

The official committee homepage shows a distinct ministry-level AI standardization architecture with dedicated working groups and a CAICT-linked operational base, making it highly relevant to the China standards stack even though it was not in the starting cluster.

Tsinghua-linked AI international governance research node included for policy, governance, and cross-border dialogue mapping.

Tsinghua-linked center that appears in public records for U.S.-China and China-EU AI and international security dialogues. PR2C keeps these descriptions high-level and models the rows as public dialogue channels, not operational security relationships.

China-side research institute visible in this PR2C import through the source-backed CnAISDA Paris side-event record. Broader IDAIS-related claims are held until exact URLs are repaired.

Chinese frontier-model developer with public disclosures centered on a transparency page plus open-weight DeepSeek-V3 repository and license documentation. This backfill does not identify a public preparedness policy, dangerous-capability evaluation framework, or external frontier-safety audit.

Alibaba Qwen model-family record backed in this pass by official repository and model-card documentation. The public evidence supports open-weight/model-card disclosures, not a public dangerous-capability or release-gating framework.

Chinese frontier-model developer whose BigModel documentation surfaces API access, content-safety controls, model-evaluation tooling, GLM-4.5 model information, and privacy policy material. These are platform and evaluation disclosures, not a public preparedness framework.

Baidu ERNIE/Qianfan record backed by Qianfan platform, user-agreement, intervention, API, and content-marking documentation. Public disclosures are strongest on platform governance, content controls, data handling, and generated-content labeling.

Tencent Hunyuan record backed by Tencent Cloud product, API, compatibility, moderation, and image-labeling documentation. The evidence supports platform controls and alignment-language disclosures, not a public frontier-risk governance policy.

ByteDance/Doubao record backed by Volcengine Ark and Doubao service documentation. Public materials emphasize platform terms, API access, and model-evaluation workflow disclosures; snippet-only platform-scope claims were held out of evidence and relationships.

Moonshot AI Kimi record backed by Kimi privacy, service-agreement, platform, and Kimi K2 model-card materials. Public evidence supports privacy, developer data-responsibility, tooling, and model-card disclosures rather than release gates or advanced-risk governance.

MiniMax record backed by about, API, privacy, model-page, and content-labeling materials. Public disclosures are mostly API/platform, privacy, benchmark, and content-provenance oriented; snippet-only API-key and labeling evidence notes were held.

StepFun record backed by privacy, user-agreement, platform, migration, and reasoning API pages. Because PR2D evidence for detailed API and agent-framework claims was snippet-only, this import keeps StepFun claims at the general platform-disclosure level.

01.AI/Yi record backed by repository, model-card, and licensing source rows. This pass treats Yi as primarily an open-weight documentation case and does not import snippet-only claims about training details, context windows, or license language.

Official Zhipu platform surface used in PR2D for source-backed API access, content-safety, and model-evaluation disclosures.

Official Baidu Qianfan platform surface used in PR2D for model services, intervention rules, API access, and content-marking controls.

Official Moonshot AI Kimi API platform surface used in PR2D for source-backed tooling, privacy, service-agreement, and model-card disclosures.

Public PKU team focused on LLM alignment, Safe-RLHF, multimodal alignment, and alignment-brittleness research.

BAAI-led public safety platform spanning red teaming, defense, interpretability, deception, and alignment.

Publicly described as working on large-model interpretability and safety alignment.

BIGAI is included for TongTest and explicit ability-plus-value-alignment framing; the row remains medium-confidence because the work is broader AGI evaluation rather than pure frontier LLM safety.