Agent-Planning-Analysis

Environments

conda create -n agent-planning python==3.9.20
conda activate agent-planning
pip install -r requirements.txt
cd code/captum
pip install -e .

Environments

Compute attribution scores.

cd code/AttrScoreCalc
# for blocksworld
python blocksWorld.py 
# for travelplanner
python travelPlanner.py 

If you want to analyze the obtained attribution scores, just open and running two jupyter notebooks in code/analysis.

Model Release

We fine-tune Llama3.1-8B-Instruct and Qwen2-7B-Instruct on BlocksWorld and TravelPlanner. The fine-tuned model weights are available on the HuggingFace 🤗.

• Llama-3.1-8B-Instruct-blocksworld-SFT
• Llama-3.1-8B-Instruct-travelplanner-SFT
• Qwen2-7B-Instruct-blocksworld-SFT
• Qwen2-7B-Instruct-travelplanner-SFT

Citation Information

If our paper or related resources prove valuable to your research, we kindly ask for a citation.

@inproceedings{xie-etal-2025-revealing,
    title = "Revealing the Barriers of Language Agents in Planning",
    author = "Xie, Jian  and
      Zhang, Kexun  and
      Chen, Jiangjie  and
      Yuan, Siyu  and
      Zhang, Kai  and
      Zhang, Yikai  and
      Li, Lei  and
      Xiao, Yanghua",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    year = "2025",
    url = "https://aclanthology.org/2025.naacl-long.93/",
}