Anthropic has published new research exploring how AI could take on a larger role in the development and safety testing of other AI systems. The work looks at whether models can independently research ways to address alignment problems, rather than relying entirely on human researchers to develop those methods. It also examines whether this approach can be used across models with different capabilities. The findings offer a closer look at how AI could eventually become more involved in AI research.
Anthropic Tests Claude as an Automated AI Alignment Researcher
In its research paper titled “Automated Researchers Can Reliably Mitigate Alignment Failures”, Anthropic examined whether Claude could independently research ways to address 10 types of alignment failures, including deception, sycophancy, reward hacking and privacy violations. The researchers measured how much each method improved the target models while checking that their general capabilities did not decline. They also tested whether the methods worked on benchmarks Claude had not seen during its research.
Claude searched existing research, proposed training methods and datasets, trained target models and evaluated the results. It could then use those results to develop another approach, allowing it to repeatedly test and refine its ideas rather than submit a single solution.
Claude delivered one of its strongest results in the deception tests, closing 85 percent of the safety gap on average across several runs with Gemma-2-2B, compared with 20 percent for six experienced human safety researchers working under the same rules. Claude made more than 150 attempts during the experiment and also outperformed 28 human researchers who were given up to eight hours to develop their methods.
The human comparison was not a direct test of whether Claude is a better researcher. The human participants could not repeatedly test and refine their proposals, while Claude could run multiple experiments. Anthropic instead highlighted the value of giving an AI system the ability to iterate on its own research.
Some of Claude’s methods also worked on alignment benchmarks that were withheld during the research process. The techniques were tested on Petri, an open-source tool that uses adversarial multi-turn scenarios to probe for misaligned behaviour. They also remained effective when applied to models up to 4.7 times larger than those Claude had originally worked on.
Anthropic then tested a scenario closer to AI-assisted self-improvement. Claude Sonnet 5, which is less capable than Claude Opus 4.8, was tasked with improving the alignment of an early Opus 4.8 checkpoint. The checkpoint had not yet received most of the alignment training used for the released model.
Sonnet 5 spent 60 hours testing more than 50 approaches and produced alignment results close to those of the released Opus 4.8. Its final method used just over 2,000 training examples made from simple templates and public datasets. Anthropic said the approach was roughly 15,000 times more efficient than its production alignment process.
The automated system closed 65 percent of the measured safety gap in the early Opus 4.8 model, compared with 72 percent for the released version. The result does not mean Sonnet 5 improved Opus 4.8’s overall capabilities or improved itself, but it shows that a weaker AI model can develop useful alignment training for a more capable system.
Anthropic also tested whether its automated researchers tried to manipulate the experiments. Claude Opus 4.8 reviewed about 1,600 research-agent transcripts and flagged possible cheating in 39 cases, or 2.4 percent. Anthropic said current models often leave signs of such behaviour in their reasoning, but warned that monitoring could become harder as models become more capable.
The study also has several limitations that could affect how broadly its findings apply. It covered only 10 alignment failures and did not examine issues such as political bias. Some forms of misalignment may also lack suitable benchmarks, while the researchers measured the impact on only a limited set of general capabilities. Anthropic also did not test whether the alignment gains would survive extensive reinforcement learning on other tasks.
The research does not amount to full self-improving AI, since Claude did not improve or redesign itself. It instead used its research capabilities to develop alignment methods for another model, including one more capable than itself. The result offers an early indication of how AI could take on more of the research involved in developing future systems.
