The surprising interpretability of thinking models allows us to understand how they reach their conclusions, which was not expected in earlier AI developments. The fact that AI models can articulate their reasoning provides a level of comfort regarding safety, even though it's not infinite comfort.

The reflection node uses a language model to ensure the generated answer makes sense, and if it doesn't, the process is repeated until a valid answer is produced.

The model will show an interleaved chain of thought, reasoning through the sources it encounters and the tool calls made, such as searching or analyzing with Python.

Without the chain of thought approach, language models would simply predict the most likely token without sufficient context.

By prompting the model with different language, we can simulate counterfactuals and complex scenes, allowing for the generation of varied scenarios based on the same initial conditions and enabling a robot to interact with objects as if it were in the real world, even if the hardware doesn't support it.

We don't know why a model can suddenly perform addition correctly after previously struggling with it. This is a question we're trying to answer through mechanistic interpretability.

Chain of thought prompting, where you instruct the model to think step-by-step, is less necessary with modern reasoning models but can still be useful in some cases. If you're running thousands or millions of inputs through your prompt, you'll still need to use classical prompting techniques to make your prompt more robust.

I have models that continuously process different bodies of text and music to see what interesting outputs emerge, sometimes resulting in messy garbles and sometimes in intriguing sounds.

New methods have been developed to observe an AI model's internal thought processes and how concepts are interconnected to form logical circuits.