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arXiv cs.CL · 2026-08-12 00:00 UTC
research

When Chain-of-Thought Helps and When It Hurts: An Empirical Investigation of the Serial-Depth Bottleneck in LLM Reasoning

arXiv:2608.09942v1 Announce Type: new Abstract: 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 lengths), it identifies a real architectural bottleneck -- serial computation exceeding a transformer's single-pass capacity must be externalised, which is what CoT does. Our central finding is a within-benchmark serial-depth gradient: single-pass (no-CoT) accuracy degra

Why it matters

Knowing when chain-of-thought degrades performance helps teams choose reasoning strategies that improve accuracy and latency, avoiding harmful overthinking or leakage risks.

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Published to Cognify News · Week 33, 2026