Chain-of-Thought Reasoning May Be a Mistake
December 31, 2025
Long chain-of-thought traces can improve benchmarks and still point model design in the wrong direction. A striking single-shot answer sometimes outperforms an hour of narrated intermediate reasoning, revealing substantial capability inside latent computation.
Brian Greenforest asks whether forcing every inference through text creates its own entropy, distraction, and overthinking.
Visible Steps Are One Interface to Computation
A reasoning model can generate intermediate tokens, inspect them, and continue. That process expands test-time compute and makes some tasks easier, yet the token stream also commits the model to a narrow sequential path.
Latent representations can integrate many relationships without spelling each one out. A direct answer may preserve that parallel structure and avoid errors introduced by a long self-conditioning chain.
Evaluate the Answer and the Compute Path Separately
Leaderboards often reward accuracy after large reasoning budgets. A stronger evaluation can compare direct and deliberative modes, cost, calibration, consistency, and the kinds of tasks each mode solves.
AI researchers can design models that choose the right internal mode instead of treating more visible text as more intelligence. The same lesson applies to people: reflection helps until narration begins to replace thought.
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Chain-of-thoughts reasoning model architecture is a mistake. It does improve results. It does put you on leaderboards, but have you ever asked yourself, "Why some single-shots sometimes are so surprisingly capable to outperfrom an hour of a complicated chain-of-thoughts 'reasoning' by that much of an unexplained latent cognitive capacity"? Have you? I wonder if it's the same with people too, when we over-think ourselves into some kind of Gödelian entropy decay?.. 🤷♂️💡🧠
#GPT #LLM #airesearch #overthinking