Update pipeline tag and improve model description

README.md

@@ -1,8 +1,10 @@
 ---
-license: apache-2.0
+datasets:
+- zeweizhang/ImitSAT-KeyTrace
 language:
 - en
-pipeline_tag: text-generation
+license: apache-2.0
+pipeline_tag: other
 tags:
 - imitation-learning
 - boolean-satisfiability
@@ -12,9 +14,8 @@ tags:
 - perceiver-ar
 - autoregressive
 - decision-sequence
-datasets:
-- zeweizhang/ImitSAT-KeyTrace
 ---
+
 <p align="center">
   <h1 align="center"><em>ImitSAT</em>: Boolean Satisfiability via Imitation Learning</h1>
   <!-- <br /> -->
@@ -44,6 +45,9 @@ datasets:
     <a href="https://github.com/zewei-Zhang/ImitSAT">GitHub repository</a>
     and the <a href="https://arxiv.org/abs/2509.25411">paper</a>.</em>
   </p>
+
+## Introduction
+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.
   
 ## Download the model
 ```bash
@@ -62,13 +66,11 @@ tokenizer/
 
 ## Citation
 ```bibtex
-@misc{zhang2025booleansatisfiabilityimitationlearning,
-      title={Boolean Satisfiability via Imitation Learning}, 
-      author={Zewei Zhang and Huan Liu and Yuanhao Yu and Jun Chen and Xiangyu Xu},
-      year={2025},
-      eprint={2509.25411},
-      archivePrefix={arXiv},
-      primaryClass={cs.AI},
-      url={https://arxiv.org/abs/2509.25411}, 
+@inproceedings{zhang2026boolean,
+      title={Boolean Satisfiability via Imitation Learning},
+      author={Zewei Zhang and Huan Liu and YUANHAO YU and Jun Chen and Xiangyu Xu},
+      booktitle={The Fourteenth International Conference on Learning Representations},
+      year={2026},
+      url={https://openreview.net/forum?id=LNqWbY5iIf}
 }
 ```