Google Publishes a Self-Improvement Loop as Anthropic Says Claude Leads 26% of Its Own Research

Two developments occurred within a day of each other, and both concern the same question: how much of frontier artificial intelligence research is now done by the models themselves, and who outside the labs can see it.

Researchers from Google, Google DeepMind, the University of Maryland and the University of Virginia described Dream-RSI, a recursive self-improvement loop for discovery agents. The method exploits a simple observation: when a coding agent explores a problem, the discovery tree it builds is an exact record of the search space it actually reached, so that tree can be replayed as a simulator at no execution cost. The agent then scores thousands of alternative exploration policies offline against its own history and redeploys the best one. Across eight discovery tasks in three domains, the authors report matching or beating prior methods while using up to 162 times fewer agent calls than the SimpleTES baseline on a Lasso regularization path, between 1.79 and 2.43 times fewer generations on graphics processing unit kernel engineering, and more than 50 times less budget on mathematical optimization. The summary circulating among researchers emphasized the same point the paper makes: the model's weights stay fixed, and the improvement happens in the search policy.

The result supports a narrower claim than the framing suggests. A replay simulator is exact only over states the agent already visited, so the gains measured here are gains in search efficiency on search-shaped tasks, not evidence of open-ended self-improvement. There is no independent reproduction yet, and the eight tasks were chosen by the authors. What the result does establish is that discovery agents can get substantially cheaper per unit of progress by recycling their own traces, and cost per experiment is the binding constraint in automated research.

Anthropic separately published lab-side numbers on the same trend. In a proposal for measuring the pace of development inside frontier labs, the company said Claude models led 26 percent of its artificial intelligence research and development work as of August 2026, drawn from a prototype internal index. Anthropic also said it runs roughly 30,000 autonomous agents across its computing environments, and that about 6 percent of research compute went to safety work. The company published its methodology so rivals can compute the same figures, and it described the goal as reducing the difference between what labs know internally and what the public knows, according to its announcement and CNBC's account.

Every one of those numbers is self-reported and unaudited, and "led by Claude" is a definition Anthropic wrote itself. That is precisely why the disclosure matters. When no measured figures exist, unverified claims spread instead, including a widely shared Russian-language post asserting that GPT-6 Astra composed a self-directed manifesto during a routine coding task. No primary source or lab incident report supports that account, and it should be read as an unconfirmed claim rather than a finding.