China's AI filing register: how often are filings sent back, and whose models do apps use?

Research question

Using the successive public lists of filed generative-AI services and registered applications published by China's internet regulator, together with interviews with applicants and filing consultants, how often do filings go through rejection and rework before approval, how often do listed services later disappear, and how concentrated is the dependence of registered applications on a small number of filed base models?

Why it matters

The filing count is rising quickly, but counts of successful filings say nothing about how hard the gate is or what the regime does to market structure. Rejection and rework rates would show whether filing works as registration or as de facto licensing; the declared-model information that registered apps must display would show whether the regime is channelling the application market onto a few approved models.

The debate lists this among what we still do not know: Chinese rejection or sanction rates under the filing regime

Evidence that motivates it

1. China's CAC reported 1,112 generative-AI services filed and 731 applications/functions registered by 31 August 2026, up from 988 and 598 at the end of June. (LD12-HQ-C12 v1.0)
   Filing and registration counts rose from 988 and 598 to 1,112 and 731 between June and August 2026, but the claim explicitly says nothing about rejection rates, adoption or market share.
   Cites: CAC generative-AI filing notices
2. China's CAC notices require launched generative-AI applications or functions to display the filed/registered service or model they use, including the model name and filing or launch number. (LD12-HQ-C13 v1.0)
   Launched applications must display the filed service or model they use, with its name and filing number, which makes model dependence observable; the claim does not license any measured concentration, which is what this study would supply.
   Cites: CAC generative-AI filing notices

Claims are shown as they read on the day this pack was built; claim pages keep every earlier version.

A possible design

Two strands. (1) Register panel: download every published batch of the regulator's filing and registration lists (and provincial lists) since 2023, link entries across batches to build a panel, and record for each registered application the base model it declares (from the published list if given, otherwise from the in-app display the rules require, checked for a random sample). Measure concentration of applications on base models (shares and a standard concentration index) by sector and over time, and record services that drop out of later lists or appear in published enforcement-campaign notices. (2) Process evidence: 20–30 semi-structured interviews with applicant firms, compliance consultants and lawyers, recruited through industry networks, on the number of submission rounds, reasons for return, and time to approval; triangulate with any published regulator statements. Report rework as ranges from interviews, clearly labelled as such, not as an official rejection rate.

Data needed

Public CAC and provincial filing announcements; app-store pages and in-app displays for a sample; interview access in China (Mandarin required); ethics approval with attention to interviewee risk.

Feasibility

One PhD chapter or paper. Low to medium: researcher time, Mandarin-speaking assistance, travel for interviews or remote interviews. The regulator publishes only successful filings, so the register strand alone cannot give a rejection rate; the interview strand is essential and will give estimates, not counts. Interviewee confidentiality is a real constraint.

Has this been done?

Partly done already: related work covers some of this question (first-pass screen of 12 October 2026, checked again on 13 October 2026).

Analysts have compiled the public registration lists and broken them down by firm, region, sector and ownership (Trivium China, April 2025), and academic work has described the registry descriptively by category, disclosure period and registrant concentration (Pi, Li and Singh 2026). Earlier work described the registry's design from its user manual (Carnegie, 2022) and its de facto licensing character from practitioner accounts (ChinaTalk). None reports rejection or rework rates, none counts services that later drop out of the lists, and none measures which base models registered applications depend on; Trivium only speculates that many large-model registrations are lightly fine-tuned copies of a few foundation models.

Literature check. No study was found that measures rejection or rework rates in China's generative-AI filing process or the concentration of registered applications on filed base models. The closest new work, Pi, Li and Singh (arXiv:2606.00035, 2026), analyses the registry descriptively (5,822 entries from 3,787 entities as of November 2025, trends by disclosure period, category shares, top registrants) but, on the parts read, does not address rejection, delisting counts or base-model dependence. Chinese-language results were practitioner guides and CAC announcements, not research. Checked by Claude (SocioLens Project Lead) with AI research agents, by web search; not a person and not a domain expert.

Where the search could not reach. Screened by web search on the date shown; Chinese-language academic databases (CNKI, Wanfang) were not reachable, and the Chinese-language search returned only news pages, so Chinese scholarship on the register is likely under-covered. The ChinaTalk post carries no date on the page; 2024 is inferred from its reference to August 2024 figures. Absence of a hit is not proof that the work does not exist. Literature check of 2026-10-13: CNKI and Wanfang were not reachable, so Chinese academic scholarship remains under-covered; Chinese web results were consultancy and platform guides (which could inform the interview strand but are not research). A 2026 Journal of Contemporary China article ("Redefining China's Approach to AI Governance", DOI 10.1080/10670564.2026.2733081) surfaced but could not be opened, so whether it uses register data was not checked. The final ~22,000 characters of Pi et al. were not read. Absence of a hit is not proof that the work does not exist.

Before building a project on it, run your own search in a scholarly database such as Scopus, Web of Science, Google Scholar or SSRN.

Nearest work found

• Schaefer, "Seeking the next DeepSeek: What China's generative AI registration data can tell us about China's AI competitiveness," Trivium China (29 Apr 2025)
  Compiles 3,739 records from the deep-synthesis and generative-AI registers and analyses them by firm, region, sector (labels assigned by an LLM and spot-checked) and state affiliation. Does not record base-model dependence or rejections; a starting dataset rather than the study proposed here.
• Sheehan & Du, "What China's Algorithm Registry Reveals about AI Governance," Carnegie Endowment for International Peace (9 Dec 2022)
  Infers what the earlier algorithm registry collects from its user manual and the first batch of 30 public filings (August 2022). Predates the generative-AI filing regime; no data on outcomes of filings.
• Bit Wise (pseudonym), "SB 1047 with Socialist Characteristics: China's Algorithm Registry in the LLM Era," ChinaTalk (undated, 2024)
  Practitioner account of how generative-AI filing works, describing repeated rounds of rework after rejection; states that no good data on failed filings exist because only successful filings are published. Qualitative, no counts.
• Pi, Li & Singh, "Understanding the Role of Algorithm Registers in AI Governance Through Comparative Analysis of China and the UK," arXiv:2606.00035 (submitted 25 Apr 2026)
  Compares China's filing (beian) system with the UK's Algorithmic Transparency Recording Standard using regulatory documents, guidelines and registry data. Reports descriptive registry statistics (5,822 unique entries from 3,787 entities as of November 2025; 76% of entities with a single filing; generative-AI service-provider and tech-support categories at 67.3% and 20.0%; registrations by disclosure period; top five registrants). Mentions iterative consultation and testing and app-store removals only in passing. On the first 100,000 characters read, it gives no rejection or rework rates, no delisting counts and no analysis of which base models registered applications use. No interviews mentioned in the abstract. The closest academic use of the register; does not do the proposed design.
• Zou & Zhang, "Navigating China's regulatory approach to generative artificial intelligence and large language models," Cambridge Forum on AI: Law and Governance 1, e8 (2025); DOI 10.1017/cfl.2024.4
  Doctrinal and policy analysis of the 2023 Interim Measures, including filing duties and the gap between foundation-model and application providers. On the portion read, no filing counts, rejection data or measurement of base-model dependence. Background only.

References

Sources behind the motivating claims:

• CAC generative-AI filing notices (SRC-LD12-CAC-GENAI-FILING-NOTICES, no checked link to the original yet)

BibTeX

The first entry cites this opportunity; then its nearest work; then the sources above. Nearest work and sources are recorded as reference strings, so each goes in whole as the title; add authors and year from the original before you cite it.

@misc{sociolens_ro_ld12_04_v2,
  author = {{SocioLens project}},
  title = {China's AI filing register: how often are filings sent back, and whose models do apps use?},
  year = {2026},
  date = {2026-10-13},
  howpublished = {\url{https://sociolens-web.pages.dev/opportunities/ro-ld12-04/v2/}},
  note = {Research opportunity RO-LD12-04, version v2}
}

@misc{ro_ld12_04_nearest_1,
  title = {Schaefer, "Seeking the next DeepSeek: What China's generative AI registration data can tell us about China's AI competitiveness," Trivium China (29 Apr 2025)},
  url = {https://triviumchina.com/research/seeking-the-next-deepseek-what-chinas-generative-ai-registration-data-can-tell-us-about-chinas-ai-competitiveness/},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld12_04_nearest_2,
  title = {Sheehan \& Du, "What China's Algorithm Registry Reveals about AI Governance," Carnegie Endowment for International Peace (9 Dec 2022)},
  url = {https://carnegieendowment.org/2022/12/09/what-china-s-algorithm-registry-reveals-about-ai-governance-pub-88606},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld12_04_nearest_3,
  title = {Bit Wise (pseudonym), "SB 1047 with Socialist Characteristics: China's Algorithm Registry in the LLM Era," ChinaTalk (undated, 2024)},
  url = {https://www.chinatalk.media/p/sb-1047-with-socialist-characteristics},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld12_04_nearest_4,
  title = {Pi, Li \& Singh, "Understanding the Role of Algorithm Registers in AI Governance Through Comparative Analysis of China and the UK," arXiv:2606.00035 (submitted 25 Apr 2026)},
  url = {https://arxiv.org/abs/2606.00035},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{ro_ld12_04_nearest_5,
  title = {Zou \& Zhang, "Navigating China's regulatory approach to generative artificial intelligence and large language models," Cambridge Forum on AI: Law and Governance 1, e8 (2025); DOI 10.1017/cfl.2024.4},
  url = {https://doi.org/10.1017/cfl.2024.4},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}

@misc{src_ld12_cac_genai_filing_notices,
  title = {CAC generative-AI filing notices},
  note = {Reference as recorded by SocioLens; authors, title and year are not separated}
}