When Chain-of-Thought Helps and When It Hurts: An Empirical Investigation of the Serial-Depth Bottleneck in LLM Reasoning
New research suggests Chain-of-Thought prompting acts as a bandwidth bypass for complex, serial reasoning tasks.
It is widely assumed that chain-of-thought (CoT) prompting universally improves LLM reasoning. We investigate this through the conceptual framework of the H_dp bandwidth bound (Chen et al., 2024): although the formal bound binds only asymptotically (at astronomically large prompt...
ArXiv CS.CL
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This research clarifies that Chain-of-Thought prompting is not a universal performance boost but rather a mechanical workaround for models struggling with complex, serial reasoning tasks. By identifying the 'serial-depth bottleneck,' the study provides a framework for developers to predict when reasoning chains are necessary versus when they are simply inefficient overhead.
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