# Research Related Articles

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Tsinghua AI Mathematician Emerges: From Intuition to Theorem, Contributing to an 84-Page Quantum Algorithm Paper

Tsinghua University’s Intelligent Industry Research Institute (AIR) has developed an AI mathematician agent named AIM, designed not just to solve math problems but to actively participate in early-stage research. In a recent study, researchers collaborated with AIM to develop "Sign Embedding Quantum Algorithms," resulting in an 84-page paper on quantum algorithms for matrix equations and functions. The research began with a human researcher's intuition: can rational approximation serve as a design principle for quantum algorithms? AIM helped expand this idea into multiple candidate research directions. Human researchers then filtered and focused on the most promising path. AIM assisted in organizing theorems, generating proof drafts, and performing complexity analysis, while humans maintained oversight, auditing assumptions and refining derivations. This case illustrates a human-AI collaborative workflow: AI rapidly explores and expands research avenues, generates draft materials, and aids in checking derivations; human researchers provide critical judgment on direction, value, and validity. The process emphasizes "high-throughput candidate generation + human value gating + AI-assisted audit and repair + human final integration." The resulting quantum algorithm framework offers a unified approach to several matrix problems, advancing quantum linear algebra under more general conditions. This work suggests AI's role in theoretical research is evolving from task-specific assistance to supporting the entire research lifecycle—enhancing exploration and efficiency while keeping human expertise central to guiding inquiry and ensuring rigor. *Paper & System Links:* - AIM application report: https://arxiv.org/abs/2606.24899 - Quantum algorithm paper: https://arxiv.org/abs/2604.25333 - AIM repository: https://github.com/TheoryFoundry/AIMv2

marsbit07/10 02:53

Tsinghua AI Mathematician Emerges: From Intuition to Theorem, Contributing to an 84-Page Quantum Algorithm Paper

marsbit07/10 02:53

Vitalik’s Rollup Proof Work Shows Ethereum Scaling Still Runs Through Cryptography

Vitalik Buterin's latest technical work focuses on proof optimization for Ethereum rollups, highlighting that the network's scaling path remains fundamentally tied to advances in cryptography. His research delves into improving the efficiency of polynomial commitments and other proof systems, which are essential for rollups to securely compress and verify transactions. While dense and technical, this ongoing development is crucial for enabling cheaper, faster, and more scalable execution on Ethereum without compromising security. The work underscores that beneath market noise around ETFs and prices, Ethereum's research layer remains actively focused on the foundational improvements needed for long-term growth and its position as a leading settlement layer.

bitcoinist07/09 23:12

Vitalik’s Rollup Proof Work Shows Ethereum Scaling Still Runs Through Cryptography

bitcoinist07/09 23:12

37.7°C Heatwave Paralyzes Cambridge University AI Supercomputer, 350 Research Projects Halted

Cambridge University's national AI supercomputer, "Dawn," was forced offline for over a week due to a record-breaking June heatwave in the UK, where temperatures reached 37.7°C. This failure of the cooling system halted more than 350 critical research projects reliant on the £300 million machine, which features 1024 Intel GPUs and liquid cooling. Impacted work included climate change modeling, Parkinson's disease drug screening, and AI-powered cancer vaccine research, highlighting the vulnerability of essential infrastructure to extreme weather. The incident mirrors similar heat-related outages at Google and Oracle data centers in the UK in 2022. Experts point to a growing challenge: as AI chips become more powerful and generate significantly more heat, data center cooling systems—often designed for historical climate norms—are increasingly struggling against the rising temperatures driven by climate change itself.

marsbit07/09 03:55

37.7°C Heatwave Paralyzes Cambridge University AI Supercomputer, 350 Research Projects Halted

marsbit07/09 03:55

Completely Free, Open-Source Alternative to Claude Science, Use DeepSeek/GLM as You Like

Free, Open-Source Alternative to Claude Science: OpenScience Lets Researchers Use Any AI Model Like DeepSeek or GLM Shortly after Anthropic launched Claude Science, a specialized AI workbench for scientific research, the open-source community responded with its own solution: OpenScience. Developed by YC-backed Synthetic Sciences, this platform offers a full AI research workflow covering literature review, hypothesis generation, code experimentation, and paper writing. Unlike Claude Science, which is restricted to macOS/Linux, paid users, and only Claude models, OpenScience is model-agnostic and free. Researchers can use any AI model—DeepSeek, GLM, Claude, GPT, or local models via Ollama—simply by providing their own API keys, keeping data private. OpenScience features over 250 editable research skill packs, more than four times Claude Science's initial offering, and supports one-command installation. It emphasizes openness, arguing that scientific AI tools should not be controlled by a single company. The project includes a clear disclaimer of no affiliation with Anthropic, reflecting caution in the open-source community.

marsbit07/07 07:30

Completely Free, Open-Source Alternative to Claude Science, Use DeepSeek/GLM as You Like

marsbit07/07 07:30

DeepMind's Classic Masterpiece Crowned Again, ICML 2026 Awards Announced

ICML 2026 has announced its annual awards, with diffusion models and AI safety ethics taking center stage. The Outstanding Paper Award was shared by two diffusion model studies. One challenges a core assumption of diffusion language models (DLMs), arguing that their touted "arbitrary order generation" is a "flexibility trap" that harms performance. The other provides a high-accuracy sampling method, pushing the technical ceiling for diffusion models and log-concave distributions. A position paper winning the Outstanding Award raises a critical ethical concern: AI alignment research is unintentionally building a "censor's toolkit," where safety tools like RLHF can be repurposed for content control. Several papers received Honorable Mentions, spanning key areas: mapping where honesty emerges in RLHF-trained models, motion attribution in video generation, quantifying how much language models memorize, analyzing diffusion model consistency via random matrix theory, and providing a mathematical proof for the "grokking" phenomenon in a simple model. The Test of Time Award was given to DeepMind's 2016 seminal work "Asynchronous Methods for Deep Reinforcement Learning," recognizing the enduring impact of the A3C algorithm. Overall, the awards signal a shift in AI research from rapid expansion to deeper scrutiny—validating diffusion models as a major architectural contender while prompting serious ethical reflection within the safety community.

marsbit07/06 02:38

DeepMind's Classic Masterpiece Crowned Again, ICML 2026 Awards Announced

marsbit07/06 02:38

Karpathy's Latest Outburst: A Single Sentence That Silenced the Entire Agent Developer Community

Andrej Karpathy, a core researcher at Anthropic, recently critiqued the current AI agent development frenzy. He argues that the biggest mistake is forcing agents to perform tasks without first thoroughly understanding the underlying large language models. Drawing from his 2016 "World of Bits" project at OpenAI—an early attempt at web-based agents that ultimately failed due to premature technology—he emphasizes that foundational model work is crucial. Karpathy offers three key pieces of advice: First, focus on getting the base models right before pushing agents. Second, recognize that creating a demo is easy, but building a real product takes a decade, akin to the journeys of autonomous driving and VR. Third, the product is the core capability, not the agent shell; a robust foundation will naturally enable advanced agents. He also suggests looking to neuroscience for inspiration, comparing agent components to brain structures like the hippocampus and thalamus. Despite his caution, Karpathy concludes that independent developers and startups, not large labs like OpenAI, are at the forefront of agent innovation. This is because the agent field is new, with no entity having a five-year head start, leveling the playing field for agile experimenters. His core message is not to abandon agent work, but to build it on a solid, deeply understood foundation.

marsbit07/06 02:33

Karpathy's Latest Outburst: A Single Sentence That Silenced the Entire Agent Developer Community

marsbit07/06 02:33

Tsinghua University's Special Award Winner, Gu Yuxian, Joins DeepSeek

Tsinghua University's prestigious Graduate Special Scholarship recipient and 2021 Ph.D. candidate, Yuxian Gu, has officially joined DeepSeek. This news coincides with DeepSeek's major recruitment drive and the imminent launch of DeepSeek V4, on whose research paper Gu is listed as an author. A doctoral student in the Conversational AI group under Professor Minlie Huang at Tsinghua, Gu's research focuses on enhancing efficiency throughout the entire lifecycle of large language models. His key contributions span three areas: innovative methods for pre-training data selection (e.g., PDS), advanced knowledge distillation techniques for model compression (notably MiniLLM), and the development of efficient model architectures like Jet-Nemotron. His work has gained significant recognition, with nearly 5,000 citations on Google Scholar. Key publications include the highly cited surveys and papers on pre-trained models and the MiniLLM distillation method. As first author, he has presented at top-tier AI conferences including NeurIPS, ICLR, and ACL. One of his notable achievements is the Jet-Nemotron architecture, which combines Post-Neural Architecture Search (PostNAS) and a novel linear attention module called JetBlock. This model series demonstrates state-of-the-art performance rivaling larger models while achieving substantial efficiency gains in inference. Gu's expertise in creating powerful yet efficient AI systems aligns with industry needs, as evidenced by the adoption of his MiniLLM method by leading tech companies. His move to DeepSeek is anticipated to contribute further advancements in the field.

marsbit07/06 02:08

Tsinghua University's Special Award Winner, Gu Yuxian, Joins DeepSeek

marsbit07/06 02:08

Hinton Praises, Gemini Core Contributor Speaks: In the Future, There Will Be Billions of Superhuman AI Einsteins

In his speech "Training Sand to Think: Artificial General Intelligence & Future of Physics," Adam Brown, a core contributor to Gemini, outlines the rapid and transformative evolution of AI. He describes how large language models (LLMs), grown rather than programmed through pre-training and fine-tuning, have progressed from performing poorly on high-school math tests to achieving gold-medal level at the International Mathematical Olympiad and recently making a genuine mathematical breakthrough by disproving a decades-old conjecture. Brown attributes this acceleration to the "Scaling Law," where predictable performance gains come from increasing compute, data, and model size. He draws parallels to the history of chess AI, predicting a similar trajectory for scientific research: moving from tools to "centaur" human-AI collaboration, and eventually to autonomous, superhuman "AI scientists." Even if progress halted today, AI already reshapes physics as a tireless tutor, powerful programming assistant, and exhaustive literature reviewer. However, Brown argues progress will continue due to immense economic runway and technical optimizations. He envisions a near-future golden age of human-AI collaboration in science, potentially leading to billions of replicated, superhuman AI researchers, making the coming years the most exciting in physics' history.

marsbit07/04 06:40

Hinton Praises, Gemini Core Contributor Speaks: In the Future, There Will Be Billions of Superhuman AI Einsteins

marsbit07/04 06:40

Vitalik Buterin Says Obfuscation Could Boost Blockchain Privacy

Ethereum co-founder Vitalik Buterin published an article exploring how cryptographic obfuscation could enhance blockchain privacy and security. Obfuscation protects the code of a program itself, making it unreadable while allowing it to function normally, unlike encryption which secures data. Buterin explained that combining obfuscation with blockchain's inherent ability to record ownership could create systems where user privacy is maintained and reliance on central authorities is reduced, enabling secure payments, finance apps, and confidential operations. However, major technical hurdles remain. While research in "indistinguishability obfuscation" has proven it theoretically possible, current implementations are extremely inefficient, with some methods taking impractically long times to execute. Scientists are working on optimizing cryptographic methods and mathematical tools to improve efficiency. Buterin concluded that practical, widely usable obfuscation will take considerable time to develop, but eventual progress could lead to highly secure blockchain products that operate without trusted third parties.

TheNewsCrypto06/29 13:01

Vitalik Buterin Says Obfuscation Could Boost Blockchain Privacy

TheNewsCrypto06/29 13:01

Google's 'Reasoning King' Also Departs for Meta, Originally Recruited by Fei-Fei Li

"Google's 'King of Reasoning' Leaves for Meta, Quietly Departing After Over Eight Years. Denny Zhou, a key figure behind Google's AI reasoning advancements including work showcased by CEO Sundar Pichai, has joined Meta's MSL as a research scientist. His low-profile move, discovered via a LinkedIn update, occurred months before the high-profile departures of Noam Shazeer to OpenAI and Nobel laureate John Jumper to Anthropic. Zhou was originally recruited to Google by Fei-Fei Li's China center initiative after nearly 11 years at Microsoft. This is part of a significant talent drain at Google, with top researchers like Shazeer (co-author of the Transformer paper) and Jumper (AlphaFold lead) recently leaving for rivals. Reports suggest internal friction is a contributing factor, particularly around Google's strategic shift. The company has reportedly formed a high-priority 'AI Coding Strike Team,' involving co-founder Sergey Brin, to urgently bridge the gap in AI coding agents, potentially reallocating resources and focus away from other research directions like DeepMind's 'world model' AGI approach. This pivot towards commercially-proven coding applications may have influenced departures, as hinted by Shazeer's comment about his compute allocation being given to another team. Meanwhile, Meta continues to bolster its team, also recently hiring UC Berkeley professor and 'security godmother' Dawn Song, along with her startup Virtue AI team, as a VP of AI research."

marsbit06/26 13:39

Google's 'Reasoning King' Also Departs for Meta, Originally Recruited by Fei-Fei Li

marsbit06/26 13:39

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