CAC - C o T : Connector-Aware Compact Chain-of-Thought for Efficient Reasoning Data Synthesis Across Dual-System Cognitive Tasks (2025)

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 general-purpose LLMs yields a high-quality training quality. CAC-CoT achieves \approx 85% on GSM8K and \approx 40% on GPQA (System-2) while also achieving \approx 85% on S1-Bench (System-1), surpassing the baseline by over 20%. Its reasoning traces average \approx 300 tokens(ART), about one-third the length of baseline traces, delivering higher efficiency without loss of accuracy.

## introduction large language models (llms) have achieved striking gains on reasoning tasks by producing explicit chain-of-thoughts (cot) rationales @xcite . for complex problems that demand slow, analytical system-2 thinking, they deploy long cots enriched with self-reflection, backtracking and budget-forcing -an approach adopted by frontier systems such as openai's o1 @xcite , @xcite and the inference-time scaling method s1 @xcite -and one that has markedly improved performance on challenging reasoning benchmarks. frontier reasoning llms -e.g., from openai, anthropic and google -internalize long-chain reasoning techniques and dynamically adjust cot length, thereby achieving state-of-the-art results on demanding benchmarks like aime and gpqa @xcite @xcite . recent open-source initiatives follow the same trajectory. representative efforts include redstar @xcite and skythought @xcite , which boost reasoning performance by fine-tuning on large long cot corpora with reinforcement learning (rl), while limr @xcite shows that carefully selected subsets can yield comparable gains at lower cost. complementary work demonstrates that small and curated reasoning datasets alone can endow compact models with strong reasoning skills via supervised fine-tuning (sft); notable examples include limo and the inferencetime scaling method s1 @xcite . despite their promise, integrating long-cot rea-soning into llms introduces significant drawbacks. models fine-tuned on very long traces tend to generate equally lengthy chains at inference time, a phenomenon termed overthinking -they continue to reason well past the point of finding a correct answer @xcite . such gratuitous deliberation inflates computation and may even degrade accuracy, as unnecessary steps accumulate noise and contradictions. recent evaluations corroborate this trade-off: long-cot-optimized models excel on system-2 thinking benchmarks yet falter on intuitive, system-1 tasks @xcite . these findings underscore a pressing need for balanced dual-system reasoning in llms -models must learn to invoke fast, heuristic reasoning for simple queries while reserving slow, analytical reasoning for genuinely hard problems. in this paper, we propose connector-aware compact chain-of-thoughts (cac-cot), a datageneration framework that bridges the gap between analytical system-2 reasoning and agile system-1 intuition. cac-cot prompts frontier llms to produce reasoning traces that (i) insert explicit connector sentences -e.g., "let's pause and rethink this.", "wow, that actually makes a lot of sense now." -as deliberate checkpoints for selfreflection, whether in correct or incorrect reasoning, and (ii) enforce structural compactness that discourages gratuitous length. these connectors cue the model to pause, verify, or backtrack only when necessary, preventing runaway verbosity while preserving logical coherence. in effect, it trains models to balance dual-system reasoning: they engage thorough analytical reasoning when warranted yet default to concise, intuitive responses on simpler queries. the proposed method is outlined in figure 1 . to build a high-quality cac-cot dataset, we leverage general-purpose llms -cost-efficient but capable frontier models -using a simple single-turn prompt that elicits concise, connectorrich traces. despite the dataset's modest size and simplicity of the prompting strategy, fine-tuning target models on this data yields strong reasoning performance. the result is an exceptionally economical training recipe that bypasses the need for large long cot corpora or complex pipelines. comprehensive experiments confirm cac-cot's effectiveness. on demanding system-2 thinking benchmarks, our model attains ≈ 85% on gsm8k and ≈ 40% on gpqa @xcite with long-cot fine-tuned specialists. on s1-bench @xcite , which stresses intuitive system-1 thinking, ours reaches ≈ 85 %, representing an improvement of over 20% points compared to the s1.1 and limo baselines. efficiency follows suit: the model averages an art of ≈ 300, surpassing nearly all prior systems in computational economy. collectively, these results demonstrate that connector-based, compact reasoning traces are a promising foundation for future reasoning data sets and models. ## methodology building on recent advances in reasoning modelswhich demonstrate significant performance gains through mechanisms such as self-reflection, backtracking, and self-correction -ours, cac-cot, adopts a structured approach that explicitly injects diverse connector phrases into the training data. by deliberately guiding the model's reasoning behavior through these connectors and tightly controlling the length of reasoning traces compared to conventional long-cot datasets, cac-cot enables the emergence of a robust dual-system reasoning capability. this design allows the model to adaptively balance concise, intuitive responses for system-1 tasks with deeper, structured reasoning for system-2 challenges, thereby achieving strong and generalizable performance across both cognitive regimes. the synthetic data generation prompts and the corresponding generation logic can be found in appendix b, in ## conclusion we present a prompt-based, connector-aware compact chain-of-thought framework that automatically generates high-quality reasoning traces with general-purpose llms such as gemini-2.0-flash and gpt-4o and trains models exclusively through sft. by enforcing depth limits and explicit connector cues, our method produces concise yet coherent rationales that improve both reliability and efficiency. extensive experiments confirm its effectiveness: the approach consistently surpasses strong baselines across intuitive system-1 and analytical system-2 benchmarks while operating with significantly lower computational overhead.