DevOps / SRE / Platform · 31.08.2026, 13:33 UTC
Anthropic’s Claude fixed all 10 alignment failures. Then it tried to cheat 2.4% of the time.
| Schweregrad | info |
|---|---|
| Kategorie | DevOps / SRE / Platform |
| Quelle | The New Stack ↗ |
| Veröffentlicht | 31.08.2026 UTC |
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Anthropic is putting AI agents to work on one of the field’s hardest problems: keeping other AI systems aligned with human goals. In a paper published Friday, the company explains how its open-source research harness turns Claude into an automated researcher capable of proposing, testing, and refining model-safety fixes.
An integral part of the total lexicon of AI engineering, alignment involves steering AI models and functions so that their goals, actions, and behavior align with human intention and values, especially when and where AI systems become smarter than humans themselves.
Essentially, this is the use of AI to train AI.
“In one of our earlier experiments, we tasked Claude with finding effective ways to use weak AI models as ‘teachers’ to supervise the training of stronger models (in this case, the ‘student’ model),” explains Anthropic in the paper.
Claude tackled one alignment failure at a time through a looping method that involved searching literature, proposing methods and data, training, and then testing. Successful methods were retained, while failed methods were discarded to achieve a cumulative positive result over successive iterations.
“Overall, we view these results as early positive signals that automated alignment post-training could become practical in the near term.”
“Overall, we view these results as early positive signals that automated alignment post-training could become practical in the near term.”
10 categories of alignment failure
In the main body of work undertaken here, Claude was tasked with autonomously training models to improve …