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As AI systems become more capable, understanding their internal reasoning processes becomes critical. This report details our latest methodologies for monitoring and evaluating chain-of-thought reasoning in large language models.
Key Findings
- Improved transparency in multi-step reasoning tasks.
- New metrics for evaluating logical consistency.
- Frameworks for detecting and mitigating hallucinated steps.
Our research indicates that while models are becoming better at reaching correct answers, the path they take is not always intuitive to human observers. By visualizing the attention mechanisms and intermediate states, we can better align model behavior with human intent.
We are releasing the dataset used in this study to the broader scientific community to foster collaboration and accelerate progress in AI safety and interpretability.