Add task category, paper & code links, full abstract and tags

README.md

@@ -1,5 +1,12 @@
 ---
 license: mit
+task_categories:
+- graph-ml
+tags:
+- chemistry
+- molecular-biology
+- drug-discovery
+- multi-modal
 dataset_info:
   features:
   - name: edge_index
@@ -53,14 +60,15 @@ configs:
 
 # MuMo Pretraining Dataset
 
-- 📄 Paper: [NeurIPS 2025 Poster](https://neurips.cc/virtual/2025/poster/119127)
+- 📄 Paper: [Structure-Aware Fusion with Progressive Injection for Multimodal Molecular Representation Learning](https://huggingface.co/papers/2510.23640)
+- 💻 Code: [https://github.com/selmiss/MuMo](https://github.com/selmiss/MuMo)
 - 📬 Contact:
   - Zihao Jing: [email protected] | Wechat: A2016A315214 | Instagram: nobeljing25
   - Pingzhao Hu: [email protected]
 
 ## Abstract
 
-Multimodal molecular models often suffer from 3D conformer unreliability and modality collapse, limiting robustness and generalization. MuMo addresses these challenges with a Structured Fusion Pipeline (SFP) that combines 2D topology and 3D geometry into a stable structural prior, and a Progressive Injection (PI) mechanism that asymmetrically integrates this prior into the sequence stream. Built on a state space backbone, MuMo supports long-range dependency modeling. Across 21+ benchmarks, MuMo achieves strong improvements and robustness to 3D conformer noise. See paper for details: [NeurIPS 2025](https://neurips.cc/virtual/2025/poster/119127).
+Multimodal molecular models often suffer from 3D conformer unreliability and modality collapse, limiting their robustness and generalization. We propose MuMo, a structured multimodal fusion framework that addresses these challenges in molecular representation through two key strategies. To reduce the instability of conformer-dependent fusion, we design a Structured Fusion Pipeline (SFP) that combines 2D topology and 3D geometry into a unified and stable structural prior. To mitigate modality collapse caused by naive fusion, we introduce a Progressive Injection (PI) mechanism that asymmetrically integrates this prior into the sequence stream, preserving modality-specific modeling while enabling cross-modal enrichment. Built on a state space backbone, MuMo supports long-range dependency modeling and robust information propagation. Across 29 benchmark tasks from Therapeutics Data Commons (TDC) and MoleculeNet, MuMo achieves an average improvement of 2.7% over the best-performing baseline on each task, ranking first on 22 of them, including a 27% improvement on the LD50 task. These results validate its robustness to 3D conformer noise and the effectiveness of multimodal fusion in molecular representation. The code is available at: this http URL .
 
 ## Dataset Overview
 
@@ -127,7 +135,7 @@ We use `preprocess/mol3d_processor.py` to derive graph and geometry features fro
 
 If you find this work useful, please cite:
 
-Zihao Jing, Yan Sun, Yanyi Li, Sugitha Janarthanan, Alana Deng, and Pingzhao Hu. "MuMo: Multimodal Molecular Representation Learning via Structural Fusion and Progressive Injection." In Advances in Neural Information Processing Systems (NeurIPS), 2025. ([paper](https://neurips.cc/virtual/2025/poster/119127))
+Zihao Jing, Yan Sun, Yanyi Li, Sugitha Janarthanan, Alana Deng, and Pingzhao Hu. "MuMo: Multimodal Molecular Representation Learning via Structural Fusion and Progressive Injection." In Advances in Neural Information Processing Systems (NeurIPS), 2025. ([paper](https://huggingface.co/papers/2510.23640))
 
 ```bibtex
 @inproceedings{jing2025mumo,