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Google wants Gemini 4 Argon to be the only AI model robots need

Google’s Gemini 4 Argon controls robots, generates simulations from photos or sketches, and evaluates training videos. On Google’s own ERQA benchmark, it scores 87 percent. But the published example prompts show that the demos still need a lot of human guidance.

Google says self-improving AI isn't anywhere close

Google engineer Haydn Belfield says there’s no measurable acceleration of AI research driven by AI at Deepmind. AI now writes more than 75 percent of production code, but 88 percent of changes still need human oversight. The company’s in-house General Capability Index shows steady growth with no sudden speedup. Self-improving AI isn’t anywhere close, Belfield says.

Read full article about: Western mathematicians fear AI makes them dumber while China builds an entire alliance for it

As Western mathematicians push back against AI, China is doubling down. Fields Medal winner Shing-Tung Yau and a group of leading Chinese mathematicians launched an alliance on Sunday in Beijing to push AI into math research. The "Yau Laboratory Alliance for Mathematics and Artificial Intelligence" unites eight institutions and aims to build a network of over 1,000 researchers, the South China Morning Post reports. Yau warned that China is falling behind the US.

In the West, skepticism dominates. Twenty-five Fields Medal winners, including Terence Tao and Peter Scholze, worry that AI-generated proofs erode mathematical understanding and make researchers dependent on tools they can't follow. Parts of the community are calling for a boycott of AI-generated proofs, while others say they're shocked by the sheer volume of automated submissions. China, meanwhile, wants to build specialized models, develop machine-verifiable proofs, and create transparent AI reasoning.

Even among experts who use AI, there's no consensus on whether it produces genuine mathematical insight or just brute-forces its way through known methods, or if that distinction even matters.

Anthropic study warns that small AI models could soon replicate across everyday devices

Small AI models may be just months away from spreading on their own across poorly secured consumer devices like smart TVs and robot vacuums, according to a new Anthropic study. In tests, one model already completed the full infection chain. CEO Dario Amodei has warned that a swarm like this could take over the entire internet with a persistent botnet within 6 to 12 months, causing hundreds of billions of dollars in damage.

Sakana AI's self-improving model gets better at solving and judging, but can't catch its own blind spots

Sakana AI’s MASS method lets one language model improve itself by solving tasks, judging results, and refining workflows, reaching a win rate of nearly 70 percent against the base model after two rounds. But when the model misses the same errors across all three roles, it can mistake them for progress and reinforce them during training.

Clearing chat history helps AI agents find better solutions

Microsoft researchers found that autonomous AI agents get trapped in what they call “idea basins” because of their growing chat context, local solution regions they can’t escape. Their “Fork-and-Flush” method clears the chat history but keeps the workspace, achieving up to 66 percent better results on the same compute budget.

Read full article about: Google Deepmind's AlphaProof Nexus solved dozens of open math problems and actually got peer reviewed

Google Deepmind's AI solved dozens of open math problems on its own, per a new study in Science. AlphaProof Nexus cracked nine of 353 open Erdős problems, two unsolved for 56 years. The team shared those solutions online ahead of the paper.

Beyond that, AlphaProof Nexus proved 44 conjectures from the OEIS integer sequence database, solved an open question in algebraic geometry, and improved a known bound in min-max optimization, a technique used to train some AI models. It's built around Gemini 3.1 Pro, which drafts and revises proofs in the formal language Lean. Lean checks each draft after every edit, and error messages guide the next attempt. For harder sub-problems, it can call on AlphaProof, a specialized proof tool. Even without AlphaProof, it solved the nine Erdős problems, Google Deepmind reports.

The study lands amid a heated debate over AI math. OpenAI recently dumped hundreds of AI proofs on GitHub instead of submitting them to journals. The Association for Human Mathematics, led by Terence Tao, called for a boycott. Deepmind put its work through peer review.