Update README

README.md

@@ -1,3 +1,106 @@
----
-license: mit
----
+---
+license: mit
+language:
+- en
+metrics:
+- accuracy
+- bleu
+base_model:
+- AIDC-AI/Ovis2-8B
+- tablegpt/TableGPT2-7B
+pipeline_tag: table-question-answering
+tags:
+- code
+---
+# TableDART Gating Network Checkpoint
+
+This repository provides the trained gating network checkpoint for **TableDART: Dynamic Adaptive Multi-Modal Routing for Table Understanding**.
+
+TableDART is a training-efficient framework that dynamically routes each table-query pair through the most appropriate reasoning path: Text-only, Image-only, or Fusion, while keeping all pretrained expert models **frozen**.
+
+---
+
+## 🔍 Overview
+
+Modeling semantic and structural information from tabular data remains a core challenge for effective table understanding.
+Existing LLM-based approaches face several limitations:
+
+- Table-as-Text methods flatten tables into text sequences, losing structural cues.
+- Table-as-Image methods preserve layout but struggle with precise semantics.
+- Static multimodal methods process all modalities for every query, introducing redundancy and potential cross-modal conflicts.
+- Most approaches require expensive fine-tuning of large LLMs or multimodal models.
+
+**Our Solution: TableDART** addresses these limitations through:
+
+- Reusing pretrained single-modality expert models (kept frozen, plug-and-play)
+- Learning only a lightweight 2.59M-parameter MLP gating network
+- Dynamically selecting the optimal path for each table-query pair (instance-level)
+- Introducing an LLM agent that mediates cross-modal knowledge integration when needed
+
+This design avoids full LLM/MLLM fine-tuning, reduces computational redundancy, and maintains strong efficiency-performance trade-offs.
+
+---
+
+## 🚀 Performance
+
+Across 7 benchmarks, TableDART:
+
+- Achieves state-of-the-art results on 4/7 benchmarks among open-source models
+- Outperforms the strongest baseline by +4.02% accuracy on average
+- Maintains significant computational efficiency gains
+
+
+## 📦 What This Checkpoint Contains
+
+This Hugging Face model includes:
+
+- The trained MLP gating network checkpoint
+
+⚠️ Note: This checkpoint does not include the pretrained text or image expert models. Please load those separately according to the official repository instructions.
+
+---
+
+## 🛠 Code and Usage
+
+Full training scripts, inference pipelines, and reproduction details are available at our Github Repository: https://github.com/xiaobo-xing/TableDART
+
+---
+
+## 📄 Paper
+
+ICLR 2026 OpenReview Version:  
+https://openreview.net/forum?id=4aZTiLH3fm
+
+ArXiv Version:  
+https://arxiv.org/abs/2509.14671
+
+---
+
+## 📚 Citation
+
+If you find TableDART helpful, please cite our paper and consider starring the repository.
+
+### ICLR 2026 Version
+
+```bibtex
+@inproceedings{xing2026tabledart,
+    title={Table{DART}: Dynamic Adaptive Multi-Modal Routing for Table Understanding},
+    author={Xiaobo Xing and Wei Yuan and Tong Chen and Quoc Viet Hung Nguyen and Xiangliang Zhang and Hongzhi Yin},
+    booktitle={The Fourteenth International Conference on Learning Representations},
+    year={2026},
+    url={https://openreview.net/forum?id=4aZTiLH3fm}
+}
+```
+
+### ArXiv Version
+```bibtex
+@misc{xing2025tabledartdynamicadaptivemultimodal,
+    title={TableDART: Dynamic Adaptive Multi-Modal Routing for Table Understanding}, 
+    author={Xiaobo Xing and Wei Yuan and Tong Chen and Quoc Viet Hung Nguyen and Xiangliang Zhang and Hongzhi Yin},
+    year={2025},
+    eprint={2509.14671},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL},
+    url={https://arxiv.org/abs/2509.14671}
+}
+```