Anthropic fellow Chen Yueh-Han published a paper titled "Automated Researchers Can Reliably Mitigate Alignment Failures" showing that an automated system can improve AI alignment benchmarks. The system searches the literature, proposes alignment methods, and trains the model for 30 minutes per iteration, gradually increasing the difficulty of the benchmarks. Effective methods are retained while ineffective ones are discarded, allowing rapid, large‑scale operation. In tests covering ten specific misaligned behaviors, the automated researchers improved performance on every benchmark without degrading overall model performance. The paper notes that the best automated method outperforms average human proposals within six hours and costs roughly $4 per hour in API inference compared with $150 per hour for human researchers. The authors describe the work as early evidence that automated alignment post‑training could become practical in the near term and a step toward recursive self‑improvement, which might reduce the need for human AI researchers. Limitations are noted: the approach relies on benchmarks accurately reflecting alignment goals and requires ongoing maintenance of the literature base and benchmark relevance.