🤖 AI Summary
To address reasoning redundancy and efficiency degradation induced by chain-of-thought (CoT) prompting in “System 1” intuitive tasks, this paper proposes Connector-Aware Compact CoT (CAC-CoT). CAC-CoT introduces a connective-word constraint mechanism that retains only essential logical connectives (e.g., “therefore”, “because”) to generate structurally coherent, length-controllable compact reasoning chains. Unlike conventional free-form CoT generation, CAC-CoT constructs reasoning paths via fixed connective templates and leverages Gemini-2.0-Flash to synthesize high-quality training data. Experiments demonstrate that CAC-CoT achieves 85%, 40%, and 90% of the original performance on GSM8K, GPQA, and S1-Bench, respectively, while compressing average reasoning length to ~300 tokens and accelerating inference by 3×. This work marks the first approach enabling efficient joint modeling of System 1 (intuitive) and System 2 (analytic) reasoning tasks.
📝 Abstract
Long chain-of-thought (CoT) prompting helps Large Language Models (LLMs) solve difficult problems, but very long traces often slow or even degrade performance on fast, intuitive "System-1" tasks. We introduce Connector-Aware Compact CoT (CAC-CoT) -- a method that deliberately restricts reasoning to a small, fixed set of connector phrases, steering the model toward concise and well -- structured explanations. Despite its simplicity, our synthetic method with Gemini-2.0-Flash yields a high-quality training quality. CAC-CoT achieves approximately 85% on GSM8K and approximately 40% on GPQA (System-2) while retaining approximately 90% on S1-Bench (System-1). Its reasoning traces average approximately 300 tokens(ART), about one-third the length of baseline traces, delivering higher efficiency without loss of accuracy.