The China Research Collective (CRC) is an independent media collective dedicated to producing high-quality English-language analysis concerning the PRC

Joined February 2026
104 Photos and videos
Who is actually driving global AI research? A new deep analysis by @TheCRC_Substack of over 37,000 authors at top-tier AI conferences (ICLR, ICML, NeurIPS) reveals a staggering shift in global talent dynamics. Where are these elite researchers getting their PhDs? Domestic Chinese universities have recently exploded in capacity. Looking at a recent cohort (2023): • Matured in China: 783 top AI PhDs graduated from domestic Chinese universities. • Matured in the US: 380 mainland Chinese researchers received their PhDs from US institutions. China is now training more than double the number of elite AI PhDs at home compared to what it sends to the US. Read the full deep dive here: chinaresearchcollective.subs… 🧵👇
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China's 12 Trillion Yuan Deleveraging: Progress, Costs, and What Comes Next The numbers are massive: 12 trillion yuan, years of cleanup, and an economy at a crossroads. We track the progress of China’s deleveraging campaign as of mid-2026. Full report below: open.substack.com/pub/chinar…
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China Research Collective reposted
We are looking for postdocs in Embodied AI & World Model through the Shuimu program @Tsinghua_Uni We offer: - 2-3 yrs 300K salary 42k CNY housing - Active global collaboration - Abundant resources: GPU&intern - NSFC overseas talent, BAAI Scholar, Kaichuang Electric.
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Who is actually driving global AI research? A new deep analysis by @TheCRC_Substack of over 37,000 authors at top-tier AI conferences (ICLR, ICML, NeurIPS) reveals a staggering shift in global talent dynamics. Where are these elite researchers getting their PhDs? Domestic Chinese universities have recently exploded in capacity. Looking at a recent cohort (2023): • Matured in China: 783 top AI PhDs graduated from domestic Chinese universities. • Matured in the US: 380 mainland Chinese researchers received their PhDs from US institutions. China is now training more than double the number of elite AI PhDs at home compared to what it sends to the US. Read the full deep dive here: chinaresearchcollective.subs… 🧵👇
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What about the Chinese researchers who get their PhDs or early jobs in the US? The data shows a distinct "Return Migration" window. While recent grads show low return rates (mostly because they are still in their early career phases), a considerable fraction of mid-career cohorts eventually return to China.
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Top 3 PhD institutions in China among 2020-2026 ICLR, ICML, and NeurIPS authors? - Tsinghua - Chinese Academy of Sciences - Peking University
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New Z.ai office logo next to Tsinghua University
Introducing GLM-5.2: Frontier Intelligence, Open Weights - Significant improvements in coding and agentic tasks - Strong long-horizon capabilities with a 1M context window - Two levels of reasoning effort: GLM-5.2 (max) pushes the limits, while GLM-5.2 (high) strikes a strong balance between performance and token efficiency - MIT-licensed open weights - Same API pricing as GLM-5.1 Tech Blog: z.ai/blog/glm-5.2 Weights: huggingface.co/zai-org/GLM-5… API: docs.z.ai/guides/llm/glm-5.2 Coding Plan: z.ai/subscribe Chat: chat.z.ai
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Huawei Ascend CloudMatrix 384 Supernode: In-Depth Project Analysis Link: chinaresearchcollective.subs…
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From Matrix Machine to System Machine: The Architectural Leap of the Next-Generation Ascend Core "Therefore, the core direction for future Ascend cores should shift from matrix peak to data-flow efficiency for complex models." Link: chinaresearchcollective.subs…
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China Research Collective reposted
Dropping new paper!! 🚨We've been padding Masked Diffusion Language Models the wrong way. MDLMs are a promising way to accelerate agentic AI systems via parallel decoding, but the dual use of [EOS] for padding and termination makes [EOS] unreliable as a termination signal. (1/3)
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New work by @lamblabtsinghua at Tsinghua College of AI on Diffusion Language Models Lamb Lab is headed by Alex Lamb, the only American Professor in the College of AI Lamb was the former PhD Student of Turing Award Winner and godfather of AI, Yoshua Bengio Lamb left Microsoft Research in NYC to join Tsinghua College of AI in 2025
Dropping new paper!! 🚨We've been padding Masked Diffusion Language Models the wrong way. MDLMs are a promising way to accelerate agentic AI systems via parallel decoding, but the dual use of [EOS] for padding and termination makes [EOS] unreliable as a termination signal. (1/3)
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Link to paper: arxiv.org/abs/2511.05963
Next-token prediction is myopic. What if transformers learn to predict their own next latent state? 🌠 We present 𝗡𝗲𝘅𝘁-𝗟𝗮𝘁𝗲𝗻𝘁 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 (𝗡𝗲𝘅𝘁𝗟𝗮𝘁): a self-supervised learning method that teaches transformers to form compact world models for reasoning and planning. It also unlocks up to 3.3x faster inference via self-speculative decoding! 🚀
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China Research Collective reposted
Next-token prediction is myopic. What if transformers learn to predict their own next latent state? 🌠 We present 𝗡𝗲𝘅𝘁-𝗟𝗮𝘁𝗲𝗻𝘁 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 (𝗡𝗲𝘅𝘁𝗟𝗮𝘁): a self-supervised learning method that teaches transformers to form compact world models for reasoning and planning. It also unlocks up to 3.3x faster inference via self-speculative decoding! 🚀
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Huawei Kirin 2026: LogicFolding - A Deep Dive into the Future of Mobile SoCs Link to full article: chinaresearchcollective.subs…
🧵 Today we're sharing a deep dive into Huawei's ‘Tao (τ) Law’, written by 乱序摸鱼, a Huawei HiSilicon chip architect. As traditional scaling becomes increasingly difficult, the semiconductor industry is searching for the next growth curve. Huawei's proposal is both ambitious and practical: Use 3D integration not just as a packaging technology, but as a new design paradigm for future mobile SoCs. This isn't simply about stacking more silicon. It's about turning 3D space into a first-class design dimension for architecture, packaging, thermals, power delivery, and even OS scheduling. The article explores a bigger question: What comes after conventional transistor scaling? Let's dive in 👇 1️⃣ Why Mobile Chips Need A New Direction Mobile devices want everything at once: • more AI • bigger batteries • better cameras • higher performance • lower power But motherboard space isn't growing. Meanwhile, traditional 2D scaling is becoming increasingly expensive, both economically and physically. The next frontier may not be smaller transistors alone. It may be learning how to build upward. 🏗️ In other words, future gains may come from architectural density, not just transistor density. 2️⃣ Huawei Didn't Invent 3D ICs The industry has been exploring 3D integration for years: • AMD's 3D V-Cache • Intel Foveros • TSMC SoIC • Apple's UltraFusion The real question isn't: "Can chips be stacked?" It's - "Can core logic itself be folded across multiple layers?" That's a much harder problem. Most existing solutions focus on: • cache stacking • chiplet integration • package-level interconnects Huawei's discussion goes further: ⚡ Can CPU, GPU, NPU and cache structures themselves be partitioned across multiple silicon layers? 3️⃣ Why Wafer-to-Wafer Matters Huawei's proposed direction isn't traditional chiplets. It's wafer-to-wafer face-to-face bonding. Why? Because logic folding requires extremely dense vertical interconnects. The goal isn't modularity, but making two logic layers behave like one piece of silicon. 🔬 Compared with conventional packaging approaches, wafer-level hybrid bonding can dramatically reduce: • interconnect length • latency • energy per bit transferred Those benefits become critical once logic starts spanning multiple dies. 4️⃣ Why 1.5μm Pitch Matters Pitch determines how many vertical connections can fit into a given area. Smaller pitch means: ✅ higher bandwidth ✅ shorter signal paths ✅ lower communication energy ❌ lower yield ❌ harder manufacturing ❌ tighter process control requirements The article argues that 1.5μm hybrid-bonding pitch is aggressive enough to enable meaningful logic folding, while remaining manufacturable at scale. 📈 At this density, vertical connectivity begins approaching the scale needed for true logic-level integration rather than simple die stacking. 5️⃣ The Real Battle: Partitioning This may be the most important challenge in the entire stack. Where should CPU blocks go? What about GPU, NPU, cache, clocks and power networks? A bad partition creates: • thermal hotspots • routing congestion • timing failures • excessive cross-die traffic A good partition unlocks most of the benefits of 3D integration. Before physical design begins, the architecture battle has already started. 🧠 This is fundamentally a system-level optimization problem balancing: performance × power × thermal × manufacturability 6️⃣ TSVs Turn Everything Into DTCO TSVs aren't just vertical wires. They introduce: • mechanical stress • routing blockage • thermal impact • yield penalties Every decision becomes a DTCO problem: architecture × process × packaging × layout In 3D chips, everything is connected. ⚙️ A TSV placement decision can affect: • timing closure • thermal distribution • power delivery • manufacturing yield This is why 3D IC design pushes DTCO (Design-Technology Co-Optimization) to a completely different level. 🔗 Full thinking: zhihu.com/question/204219473… #AI #Semiconductor #Huawei #Kirin #ChipDesign #EDA #AIInfra #Hardware #Computing
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FIRST ORIGINAL ARTICLE: Marine Corps Maoism: How "Maoist" Military Doctrine Would Influence the USMC Read here:chinaresearchcollective.subs…
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Hardware-first AI: Unitree’s billion-yuan bet ahead of its June 1 IPO. But will it be enough to beat Tesla? Read more: chinaresearchcollective.subs…
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Ascend Chief Architect Presentation on Tau Scaling chinaresearchcollective.subs…
Ascend architect Xia Jing's (夏晶) powerpoint slides on Tau Scaling Law zhida.zhihu.com/search?previ…
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Huawei Kirin Processor Chief Architect on LogicFolding and Mobile Chips chinaresearchcollective.subs…
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China Research Collective reposted
HUAWEI's Tau (τ) Scaling Law is a new principle for guiding the future development of semiconductors. By 2031, HUAWEI's high-end chips are expected to feature a transistor density equivalent to 14 Å (1.4 nm) processes. Watch the livestream to learn more! x.com/i/broadcasts/1XxygggOb…
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