ACIL: Auto Chain of Thoughts for In-Context Learning

ArXi:2605.17088v1 Announce Type: new Recent advances in large language models (LLMs) have shown that Chain-of-Thought (CoT) reasoning can substantially improve performance on complex reasoning tasks. At the same time, In-Context Learning (ICL) has become an important mechanism for adapting LLMs to new tasks without updating model parameters, using only examples provided in the prompt. However, standard ICL often struggles on tasks that require multi-step reasoning, because the nstrations usually contain only input-output pairs and lack explicit intermediate reasoning steps. This paper