Uploaded November 2025 | Updated September 2026, 2 hours ago

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What if thinking harder actually makes LLMs worse? 🧠

A new paper by Tom Griffiths shows that both humans and AI models can perform worse when forced to reason step-by-step. In tasks like face recognition or grammar learning, “thinking out loud” (via chain-of-thought) actually reduces accuracy — a phenomenon known as verbal overshadowing.

Some problems are pure System 1: fast, intuitive, pattern-based. Forcing logic onto intuition is like overthinking a climbing move — suddenly, you move like a robot instead of just feeling it.

So next time you evaluate a model, don’t just ask how well it reasons. Try both: with and without reasoning. Sometimes, the best results come when the model — or the human — just feels it.

I’m Louis-François, PhD dropout, now CTO & co-founder at Towards AI. Follow me for tomorrow’s no-BS AI roundup 🚀

#AI #MachineLearning #CognitiveScience #short