NVIDIA
4 items across 2 editions · appeared in the last 2 editions in a row. First seen Fri 11 Sep, last seen Sat 12 Sep.
- The paper, posted to arXiv on 9 September 2026 and announced in the 11 September listing, reports that the system "scored 30 out of 42 points at IMO 2026, reaching the gold-medal threshold".
- The abstract states the pipeline "operates entirely in natural language, with no formal prover, external tools, or internet access", using three Nemotron 3 Ultra checkpoints — the general-availability model and two post-trained specialists — in "an iterative search that generates, verifies, and refines candidate proofs", with a separate high-compute stage selecting each submission.
- The authors say they release both post-trained checkpoints plus the training data, training and inference code, the submitted solutions, and "Nemotron-IMO-Bench, a new benchmark of 200 novel olympiad-level problems".
- The arXiv abstract page carries no affiliation block; Hugging Face lists the institution as NVIDIA. The IMO score is the authors' own report of their own submission and is not peer reviewed.
- Reuters reported on 11 September at 8:46 pm that Nvidia is in talks to invest up to $10 billion as an anchor investor in Anthropic's IPO, which is seeking to raise up to $100 billion at a valuation of around $2 trillion, with completion expected before the US midterm elections in November.
- For comparison, Reuters cites Anthropic's May round of $65 billion raised at a $965 billion post-money valuation, and an annualised revenue run rate that surpassed $65 billion by the end of July, up from roughly $9 billion at the end of 2025.
- The report notes Nvidia said in November 2025 it would invest up to $10 billion in Anthropic under a broader partnership including a $30 billion Azure computing commitment, and that Anthropic committed more than $100 billion over a decade to AWS in April.
- Reuters says "The plans remain under negotiation and could change". Both companies declined to comment or did not respond, and no filing has been made. This is a Reuters exclusive; other outlets are aggregating it.
- Reported 10 September, sourced to the New York Times: the Department of Justice is examining whether Nvidia's roughly $20 billion arrangement with Groq, disclosed in late December 2025 and billed as a nonexclusive licensing agreement rather than an acquisition, was structured to sidestep merger review. Groq founder and then-CEO Jonathan Ross moved to Nvidia along with several key team members.
- The deal produced the Groq 3 language processing unit, now in full production as part of Nvidia's LPX rack-scale platform, integrating Groq's low-latency inference silicon into Nvidia's AI factory architecture.
- The inquiry sits alongside an FTC examination of acqui-hires across big tech. FTC Chairman Andrew Ferguson said in February that regulators want to ensure such deals "are not an attempt to get around" merger review. A finding against Nvidia would put a widely copied AI-industry deal template at risk.
- This is an investigation, not a complaint. No charges have been filed, and the underlying NYT report is behind a paywall; details here are as relayed by SDxCentral.
- Announced 10 September: Skild AI's S1 learns new manipulation tasks from a single video demonstration using in-context learning, with no weight updates or task-specific retraining. NVIDIA reports roughly 66% per-step success on multistep tasks against about 9% for comparable systems, and that one video example is worth roughly 380 hands-on training examples — 50 to 100 hours of manual collection.
- S1 executes unfamiliar tasks up to 10 minutes long across dozens of steps, including potting plants, making pancakes, pour-over coffee and kit assembly. In one plant-potting test, the gap from recording the demonstration to autonomous execution on hardware was 11 minutes.
- Skild reports a $100 million annual revenue run rate ten months after launch and more than 60 deployment partnerships across manufacturing, logistics, inspection, security and food preparation. If the demonstration-efficiency claim holds outside curated tasks, the cost of teaching a robot a new job falls by orders of magnitude — which is the labour-substitution variable to watch.
- These are vendor figures published on a supplier's blog, not an independent benchmark. "Per-step" success is not end-to-end task success, and the 66% versus 9% comparison does not name the baseline systems.