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Uncovers the inner workings of transformer-based time series classifiers, revealing how specific attention heads and timesteps causally drive correct classifications.
Chain-of-thought reasoning isn't just window dressing: swapping CoT-related features into smaller LLMs reveals a scale threshold where it actually steers the model towards correct answers.
