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.
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.