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We’re excited to see the first TPUv7 submission to AgentX, served with vLLM. Thanks to the @Google TPU, @RedHat_AI, and @inferact folks who put in the work to get it running, and we’re looking forward to more models landing on TPU through AgentX. https://t.co/Ea730oFhrd
Industrial Next presented AI-driven robotic stations at TechCrunch Disrupt 2026 that it says let manufacturers retool production lines in minutes rather than weeks without traditional programming. Its proprietary foundation models connect vision, task context, and motion, with new tasks taught through simulation and demonstration. The startup announced a partnership with BMW Group Technology Office to evaluate dexterous manipulation for manufacturing, and co-founder Lukas Pankau said some stations already perform delicate assembly tasks for an undisclosed smartphone customer.
Shing-Tung Yau and leading Chinese mathematicians launched the Yau Laboratory Alliance for Mathematics and Artificial Intelligence on Sunday in Beijing, according to the South China Morning Post. It unites eight institutions and aims to connect over 1,000 researchers, with plans for specialized models, machine-verifiable proofs, and transparent AI reasoning. Meanwhile, 25 Fields Medal winners, including Terence Tao and Peter Scholze, worry that AI-generated proofs erode mathematical understanding and encourage dependence on tools researchers cannot follow.
TypeSafe AI, the San Francisco company behind the decision-only model Jev, announced an $870 million Series A at a $7.5 billion post-money valuation, up from a $200 million valuation at its $40 million seed round 24 days earlier. a16z, Sequoia and DCVC participated, and a16z partner Martin Casado joined the board. The company says about one third of Fortune 500 companies now use or integrate Jev, which outputs decisions rather than chat.
OpenAI posted 372 mathematical results generated by an internal frontier model in a GitHub repository. It says each result solves or substantially advances an open problem, including improvements to major computational algorithms and progress related to the Riemann hypothesis.
Why it matters: OpenAI posted 372 AI-generated mathematical results directly on GitHub rather than in a journal, highlighting tension between academic review processes and the speed of AI output.
NVIDIA says AI factory return on investment depends on three factors: output capacity, useful life, and demand. Its platform uses full-stack co-design to maximize throughput per megawatt. SemiAnalysis AgentX data show Vera Rubin NVL72 delivers more than 30 times the throughput per megawatt of GB300 NVL72 and, on DeepSeek V4 Pro, can reduce cost per million tokens by up to 45 times.
OpenAI said it blocked a distillation operation intended to steal model reasoning. Activity began at low volume on July 1 and surged on July 24–25 to 16,000 requests from more than 4,000 users across more than 15,000 linked accounts. OpenAI says it shut them all down by July 28, noting that these were attempted extractions and may not have succeeded.
Why it matters: OpenAI disclosed a model-reasoning distillation theft incident that also raised questions about inconsistent cloud-platform protections, showing a real path for reasoning leakage.
Naive AI released open model Naive-N0.5-Flash. Starting from an open base model, it redesigned the attention architecture into a hybrid structure with one DSA layer for every five SWA layers, retained a 1M context window throughout, and trained for a total of 3.25T tokens in three stages.
Tsinghua-affiliated startup Fermi Universe has launched FermiQLLM 1.0, which it calls the world's first fully quantum-enhanced foundation model. Adapted from the open-source Qwen base model, it applies quantum enhancement across data representation, model architecture, training, reinforcement, and evaluation. It can be trained and deployed on existing GPUs without fault-tolerant quantum computers.
Latent Space's AINews roundup reports six reproductions of Jev appearing within two days of launch, including Laya, DiffusionGemmaJev, and Bespoke Nimble.
NVIDIA submitted preview results for Vera Rubin NVL72 for the first time in MLPerf Inference v6.1, reporting up to 3.7× the throughput of GB300 NVL72 on Qwen3-VL and up to 2.5× on DeepSeek-R1.
Why it matters: MLPerf Inference v6.1 reports throughput, scaling efficiency, and software-optimization gains for Vera Rubin NVL72 versus GB300 NVL72, offering a basis for judging inference infrastructure value.
At the AI Infra Summit, NVIDIA announced several advances in Vera Rubin and its DSX platform, shifting AI infrastructure measurement from peak performance to useful agentic tokens per megawatt.
Why it matters: The article reports several measured token-output figures per megawatt for Vera Rubin and DSX, showing how efficiency metrics for AI factories are changing.
We solved Navier-Stokes! Many of my colleagues left their fields because they believed that working on AI would be the fastest way to solve their fields' grand challenges. It's surreal to see the first of those challenges fall and to watch a future we believed in become real.
At I/O 2026, Google Research introduced the Gemini for Science experimental toolkit, including Computational Discovery built on ERA and AlphaEvolve, Hypothesis Generation based on Co-Scientist, and Literature Insights based on NotebookLM.
