Harvey AI

Chain-of-Thought May Not Be a Window into AI’s Reasoning: Anthropic’s New Study Reveals Hidden Gaps

Chain-of-thought (CoT) prompting has become a popular method for improving and interpreting the reasoning processes of large language models (LLMs). The idea is simple: if a model explains its answer step-by-step, then those steps should give us some insight into how it reached its conclusion. This is especially appealing in safety-critical domains, where understanding how a model reasons—or misreasons—can help prevent unintended behavior. But a fundamental question remains: are these explanations actually true to what the model is doing internally? Can we trust what the model says it’s thinking?

Cloud AI Market to USD 847.8 Billion by 2032

Key Takeaways from Trump's First Week in Office

President Donald Trump had promised a slew of executive orders once he took office, and the tech and finance industry executives who threw their support behind him expected several would address their long-held beefs with Biden administration policies.