Circuit tracing is an interpretability method for observing the internal, trained (not explicitly programmed) computational pathways that large language models form to connect concepts and solve tasks, making it possible to identify structures such as forward planning within a model's processing before output is generated. The approach further allows targeted intervention on identified internal circuits (e.g., suppressing a specific represented concept) to test causal influence on the model's eventual output, providing evidence that models perform genuine internal reasoning/planning rather than purely local, step-by-step generation.
Circuit tracing is an interpretability method for observing the internal, trained (not explicitly programmed) computational pathways that large language models form to connect concepts and solve task…