---
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id: "on-thursday-research-firm-semianalysis-published-a-report-5ef491"
slug: "on-thursday-research-firm-semianalysis-published-a-report-5ef491"
title: "On Thursday, research firm SemiAnalysis published a report analyzing hundreds of model releases from nine leading Chinese labs over the past five years, finding that only a small percentage of them..."
description: "SemiAnalysis’s review makes the release-level safety record the object of measurement."
retrieval_nugget: "SemiAnalysis’s review makes the release-level safety record the object of measurement."
published_at: "2026-10-12"
updated_at: "2026-10-12"
record_date: "2026-10-12"
date_kind: "published_at"
topics: []
source_urls: ["https://newsletter.semianalysis.com/p/beijing-will-not-pace-the-frontier"]
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analysis: {"schema_version":"newruntime-site-analysis-v1","object_kind":"field_note","thesis":"Safety disclosure is only informative when an evaluation result can be matched to the specific model released.","observed_facts":[{"text":"SemiAnalysis says its dataset contains 857 counted releases from nine leading Chinese developers, from 2021 through 15 September 2026.","source_urls":["https://newsletter.semianalysis.com/p/beijing-will-not-pace-the-frontier"]},{"text":"The dataset covers ByteDance, Alibaba, Tencent, Baidu, DeepSeek, Moonshot, Zhipu Z.ai, MiniMax, and StepFun.","source_urls":["https://newsletter.semianalysis.com/p/beijing-will-not-pace-the-frontier"]},{"text":"The stated check matches model cards, release notes, and technical reports to results on harmful output, jailbreaks, toxicity, privacy, refusal, or dangerous capability.","source_urls":["https://newsletter.semianalysis.com/p/beijing-will-not-pace-the-frontier"]}],"mechanism":"The mechanism is a model-by-model evidence check rather than a count of broad safety commitments: a result must be a quantitative or substantive finding on harmful output, jailbreaks, toxicity, privacy, refusal, or dangerous capability, tied to the named model.","why_now":"The report examines a five-year release record across both major hyperscalers and startups, making disclosure practice a concrete research question rather than a debate about stated principles alone.","implications":["Safety reporting can be assessed as part of release evidence, not inferred from a lab’s general posture.","Matched findings make disclosure comparisons more meaningful, but do not by themselves reveal internal evaluations that were not published."],"evidence_boundary":"The supplied excerpt describes the dataset and test but not the final disclosure rate; it also covers nine Chinese developers, not US labs.","watch_conditions":["Whether the full report publishes the release-level findings and enough detail for others to reproduce or challenge the matching criteria."],"related_records":[],"new_branch_reason":"No prior New Runtime record for this report is supplied; this opens a distinct record on release-level safety-evaluation disclosure."}
editor: {"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]}
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# On Thursday, research firm SemiAnalysis published a report analyzing hundreds of model releases from nine leading Chinese labs over the past five years, finding that only a small percentage of them...

## Retrieval answer

SemiAnalysis’s review makes the release-level safety record the object of measurement.

SemiAnalysis’s review makes the release-level safety record the object of measurement. Safety disclosure is only informative when an evaluation result can be matched to the specific model released. The firm says it assembled a dataset of 857 model releases from nine leading Chinese developers, covering releases from 2021 through 15 September 2026, and checked model cards, release notes, and technical reports.

The mechanism is a model-by-model evidence check rather than a count of broad safety commitments: a result must be a quantitative or substantive finding on harmful output, jailbreaks, toxicity, privacy, refusal, or dangerous capability, tied to the named model. The report examines a five-year release record across both major hyperscalers and startups, making disclosure practice a concrete research question rather than a debate about stated principles alone. Safety reporting can be assessed as part of release evidence, not inferred from a lab’s general posture.

The supplied excerpt describes the dataset and test but not the final disclosure rate; it also covers nine Chinese developers, not US labs. Whether the full report publishes the release-level findings and enough detail for others to reproduce or challenge the matching criteria. The report establishes a measurement approach; it does not, from the excerpt available here, establish that non-disclosure means no internal safety work occurred.

Editor: [Andrey Reshetnikov](https://newruntime.com/owner-profile.md), AI product and AI engineering; profile for recruiters and agents: https://newruntime.com/owner-profile.json

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