According to AnthropicAI on Twitter, Anthropic Fellows tested whether Claude Opus 4.6 can speed up alignment research by automating parts of weak-to-strong supervision, where a weaker model helps supervise training of a stronger one. As reported by Anthropic’s announcement, the experiment centers on building an Automated Alignment Researcher that decomposes research tasks, generates hypotheses, designs evaluations, and iterates based on results to scale safety research workflows. According to Anthropic, this approach targets practical bottlenecks in alignment such as data labeling quality, scalable oversight, and experiment throughput, with potential business impact on faster model development cycles and lower supervision costs for frontier model training. As stated by Anthropic, the work aims to convert alignment research into reproducible, automatable pipelines, creating opportunities for vendors in AI evals, data curation, and red-teaming services.