Chain-of-thought prompting is a technique that asks an AI model to reason through a problem step by step before giving its final answer, which often improves accuracy on complex tasks.
Chain-of-thought prompting asks a model to show its intermediate reasoning steps, not just its final answer. Instead of jumping straight to a conclusion, the model works through a problem the way a person might on scratch paper: breaking it into smaller steps, solving each one, and building toward the final result.
Language models generate one token at a time, and each token depends on everything generated before it. By producing intermediate reasoning steps first, the model effectively gives itself more "space" to work through a problem, which measurably improves performance on tasks like math word problems, logic puzzles, and multi-step decision-making compared to asking for a direct answer.
This idea evolved from something researchers manually triggered with prompts like "let's think step by step" into a core part of how newer reasoning-focused models work: some models now generate extended internal chains of thought automatically before answering, without needing to be explicitly asked, trading extra inference time for higher accuracy on hard problems.
Chain-of-thought reasoning can make errors just as confidently as a direct answer would, and a plausible-looking chain of steps doesn't guarantee a correct final answer. It also increases the number of tokens generated, which means it takes longer and costs more per response than a direct answer, so it's typically reserved for problems complex enough to benefit from it.
Researchers created RusFinChain, the first Russian-language benchmark for testing AI's ability to reason through financial problems step-by-step. This tool helps evaluate how well AI models can perform complex financial analysis in a non-English context.
Researchers developed a method called Semi-CoT that improves AI reasoning by using unlabeled questions. This could make AI smarter without needing as much labeled training data.
Researchers found that AI models often sound too confident in answers that aren't fully justified. They developed a new method to better align confidence with the quality of explanations. This could make AI assistants more reliable when giving complex answers.
Researchers propose a new method called Dual-Track Chain-of-Thought (CoT) to improve multi-step reasoning in small language models under tight compute and token budgets. This approach aims to enhance performance without the high costs associated with existing techniques.
A new position paper challenges the prevailing view of LLM reasoning as a chain of thought, proposing instead that it should be studied as latent-state trajectory formation. This shift could redefine how we evaluate and interpret AI reasoning.