DoubleAI’s AI system just beat a decade of expert GPU engineering WarpSpeed just beat a decade of expert-engineered GPU kernels — every single one of them. cuGraph is one of the most widely used GPU-accelerated libraries in the world. It spans dozens of graph algorithms, each written and continuously refined by some of the world’s top performance engineers. @_doubleAI_'s WarpSpeed autonomously rewrote and re-optimized these kernels across three GPU architectures (A100, L4, A10G). Today, we released the hyper-optimized version on GitHub — install it with no change to your code. The numbers: - 3.6x average speedup over human experts - 100% of kernels benefit from speedup - 55% see more than 2x improvement. But hasn’t AI already achieved expert-level status — winning gold medals at IMO, outperforming top programmers on CodeForces? Not quite. Those wins share three hidden crutches: abundant training data, trivial validation, and short reasoning chains. Where all three hold, today’s AI shines. Remove any one of them and it falls apart (as Shai Shalev Shwartz wrote in his post). GPU performance engineering breaks all three. Data is scarce. Correctness is hard to validate. And performance comes from a long chain of interacting choices — memory layout, warp behavior, caching, scheduling, graph structure. Even state-of-the-art agents like Claude Code, Codex, and Gemini CLI fail dramatically here, often producing incorrect implementations even when handed cuGraph’s own test suite. Scaling alone can’t break this barrier. It took new algorithmic ideas — our Diligent framework for learning from extremely small datasets, our PAC-reasoning methodology for verification when ground truth isn’t available, and novel agentic search structures for navigating deep decision chains. This is the beginning of Artificial Expert Intelligence (AEI) — not AGI, but something the world needs more: systems that reliably surpass human experts in the domains where expertise is rarest, slowest, and most valuable. If AI can surpass the world’s best GPU engineers, which domain falls next? For the full blog: doubleai.com/research/double… CuGraph: docs.rapids.ai/api/cugraph/s… Winning Gold at IMO 2025: arxiv.org/abs/2507.15855 Codeforces benchmarks: rdworldonline.com/openai-rel… @shai_s_shwartz post: xitter.cf/shai_s_shwartz/status/… From Reasoning to Super-Intelligence: A Search-Theoretic Perspective arxiv.org/abs/2507.15865 Artificial Expert Intelligence through PAC-reasoning arxiv.org/abs/2412.02441

We just released two papers from AAI describing recent work on our pursuit to build a “super-intelligent" AI that can match, and exceed, human level expertise in STEM. The first paper states that if we want to reach "super intelligence" then we must change the way we train our LLMs. In particular, the work argues that: 1) Expert Intelligence requires precise reasoning. 2) Precise reasoning requires efficient search in the space of ideas while trying out various explorative line of thoughts until one reaches a solution to a difficult problem. The thought process can be represented as a "reasoning search tree". 3) Expert level, difficult problems, requires deep search trees. 4) We prove that existing modern training techniques can only learn shallow search trees. 5) We’ve constructed “the diligent learner”, a training method that can efficiently build deep search trees, reach and exceeds human level reasoning capabilities.    The paper: openreview.net/forum?id=Dw1r…   The second paper makes the point that state of the art reasoning language models like O3-pro, Gemini 2.5 pro and Grok 4 heavy are not really "Phd level" as the hype goes. Benchmarks and competitive programming contests are not always a true testament to AI capabilities. Take for example CodeForces where OAI-O3 attains a very high score (0.02%top ranking). Since Dynamic Programming is a major element of CF problems, one would expect state of the art LRMs to deeply understand DP right? we took that to the test and devised a DP problem generation engine. We took 120 of the "hard" problems and gave it to leading LRMs with all the "help" we can give them including few-shot examples. The performance is "flat line" - practically zero. The dataset will be available for researchers to play with.   The paper: arxiv.org/abs/2507.13337