from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("datumo/CAC-CoT")  # 🔧 Replace with your model path
tokenizer = AutoTokenizer.from_pretrained("datumo/CAC-CoT")

prompt = "Problem: If you have 3 apples and get 2 more, how many do you have?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Citation

BibTeX:

@misc{choi2025caccotconnectorawarecompactchainofthought,
  title={CAC-CoT: Connector-Aware Compact Chain-of-Thought for Efficient Reasoning Data Synthesis Across Dual-System Cognitive Tasks},
  author={Sunguk Choi and Yonghoon Kwon and Heondeuk Lee},
  year={2025},
  eprint={2508.18743},
  archivePrefix={arXiv},
  primaryClass={cs.AI},
  url={https://arxiv.org/abs/2508.18743},
}

More Information

System-1: Fast, intuitive reasoning
System-2: Slow, logical reasoning
Connector phrase: Fixed phrases guiding logical flow (e.g., “Because of this,” “Then,” etc.)
ART: Average Reasoning Trace length

Model Card Authors

Sunguk Choi, Yonghoon Kwon, Heondeuk Lee