nielsr HF Staff commited on
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Update pipeline tag and improve model description

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Hi! I'm Niels from the Hugging Face community science team.

I've opened this PR to refine your model card. The main changes include:
- Updating the `pipeline_tag` to `other`, as this model is a specialized SAT branching policy rather than a general-purpose text generator.
- Adding a brief introduction to the model based on your paper abstract to provide more context.
- Updating the citation to the ICLR 2026 version as found in your GitHub repository.

Files changed (1) hide show
  1. README.md +14 -12
README.md CHANGED
@@ -1,8 +1,10 @@
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  ---
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- license: apache-2.0
 
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  language:
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  - en
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- pipeline_tag: text-generation
 
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  tags:
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  - imitation-learning
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  - boolean-satisfiability
@@ -12,9 +14,8 @@ tags:
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  - perceiver-ar
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  - autoregressive
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  - decision-sequence
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- datasets:
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- - zeweizhang/ImitSAT-KeyTrace
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  ---
 
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  <p align="center">
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  <h1 align="center"><em>ImitSAT</em>: Boolean Satisfiability via Imitation Learning</h1>
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  <!-- <br /> -->
@@ -44,6 +45,9 @@ datasets:
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  <a href="https://github.com/zewei-Zhang/ImitSAT">GitHub repository</a>
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  and the <a href="https://arxiv.org/abs/2509.25411">paper</a>.</em>
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  </p>
 
 
 
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  ## Download the model
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  ```bash
@@ -62,13 +66,11 @@ tokenizer/
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  ## Citation
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  ```bibtex
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- @misc{zhang2025booleansatisfiabilityimitationlearning,
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- title={Boolean Satisfiability via Imitation Learning},
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- author={Zewei Zhang and Huan Liu and Yuanhao Yu and Jun Chen and Xiangyu Xu},
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- year={2025},
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- eprint={2509.25411},
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- archivePrefix={arXiv},
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- primaryClass={cs.AI},
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- url={https://arxiv.org/abs/2509.25411},
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  }
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  ```
 
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  ---
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+ datasets:
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+ - zeweizhang/ImitSAT-KeyTrace
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  language:
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  - en
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+ license: apache-2.0
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+ pipeline_tag: other
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  tags:
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  - imitation-learning
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  - boolean-satisfiability
 
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  - perceiver-ar
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  - autoregressive
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  - decision-sequence
 
 
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  ---
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+
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  <p align="center">
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  <h1 align="center"><em>ImitSAT</em>: Boolean Satisfiability via Imitation Learning</h1>
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  <!-- <br /> -->
 
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  <a href="https://github.com/zewei-Zhang/ImitSAT">GitHub repository</a>
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  and the <a href="https://arxiv.org/abs/2509.25411">paper</a>.</em>
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  </p>
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+
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+ ## Introduction
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+ ImitSAT is a branching policy for conflict-driven clause learning (CDCL) solvers based on imitation learning for the Boolean satisfiability problem (SAT). Unlike previous methods that predict instance-level signals, ImitSAT learns from expert **KeyTrace**—a sequence of surviving decisions from a full solver run. This prefix-conditioned supervision enables ImitSAT to reproduce high-quality branches, reducing propagations and wall-clock time.
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  ## Download the model
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  ```bash
 
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  ## Citation
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  ```bibtex
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+ @inproceedings{zhang2026boolean,
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+ title={Boolean Satisfiability via Imitation Learning},
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+ author={Zewei Zhang and Huan Liu and YUANHAO YU and Jun Chen and Xiangyu Xu},
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+ booktitle={The Fourteenth International Conference on Learning Representations},
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+ year={2026},
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+ url={https://openreview.net/forum?id=LNqWbY5iIf}
 
 
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  }
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  ```