Update task category, add direct paper and code links, and improve related resources

README.md

@@ -1,9 +1,11 @@
 ---
-license: cc-by-4.0
-task_categories:
-- other
 language:
 - en
+license: cc-by-4.0
+size_categories:
+- 10K<n<100K
+task_categories:
+- text-classification
 tags:
 - binary-analysis
 - malware-detection
@@ -11,12 +13,12 @@ tags:
 - cross-platform
 - tokenized
 - stratified-splits
-size_categories:
-- 10K<n<100K
 ---
 
 # Binary-30K: Cross-Platform Binary Dataset with Stratified Splits
 
+[Paper](https://huggingface.co/papers/2511.22095) | [Code](https://github.com/mjbommar/binary-dataset-paper)
+
 **🔗 Original Dataset (no splits):** [`mjbommar/binary-30k-tokenized`](https://huggingface.co/datasets/mjbommar/binary-30k-tokenized)
 
 This is the **stratified train/validation/test split version** of the Binary-30K dataset, containing **29,793 unique cross-platform binaries** with pre-computed tokenization. This version provides standardized splits for reproducible machine learning research.
@@ -162,17 +164,17 @@ All binaries are tokenized using **BPE tokenization** ([`mjbommar/binary-tokeniz
 
 ## 🎓 Supported Research Tasks
 
-1. **Malware Detection**: Binary classification with balanced classes (26.9% malware)
-2. **Cross-Platform Analysis**: Transfer learning across Windows/Linux/macOS/Android
-3. **Architecture-Invariant Detection**: Generalization to exotic architectures (IoT/embedded)
-4. **Mobile Malware Research**: Dedicated Android and macOS malware samples
-5. **Binary Similarity**: Embedding learning for similar binary detection
-6. **Format-Agnostic Analysis**: Multi-format models (PE/ELF/Mach-O/APK)
+1.  **Malware Detection**: Binary classification with balanced classes (26.9% malware)
+2.  **Cross-Platform Analysis**: Transfer learning across Windows/Linux/macOS/Android
+3.  **Architecture-Invariant Detection**: Generalization to exotic architectures (IoT/embedded)
+4.  **Mobile Malware Research**: Dedicated Android and macOS malware samples
+5.  **Binary Similarity**: Embedding learning for similar binary detection
+6.  **Format-Agnostic Analysis**: Multi-format models (PE/ELF/Mach-O/APK)
 
 ## 📊 Comparison with Other Datasets
 
 | Dataset | Size | Platforms | Architectures | Malware | Pre-tokenized | Splits |
-|---------|------|-----------|---------------|---------|---------------|--------|
+|---------|------|-----------|---------------|---------|---------------|--------|\
 | **Binary-30K** | 30K | Win+Linux+macOS+Android | 15+ (incl. exotic) | 26.9% | ✅ | ✅ |
 | SOREL-20M | 20M | Windows only | x86/x64 | 100% | ❌ | ❌ |
 | EMBER | 1.1M | Windows only | x86/x64 | 50% | ❌ (features) | ✅ |
@@ -265,7 +267,7 @@ If you use this dataset in your research, please cite:
 
 - **Original dataset (no splits)**: [`mjbommar/binary-30k-tokenized`](https://huggingface.co/datasets/mjbommar/binary-30k-tokenized)
 - **Tokenizer**: [`mjbommar/binary-tokenizer-001-64k`](https://huggingface.co/mjbommar/binary-tokenizer-001-64k)
-- **Paper**: *Binary-30K: A Cross-Platform, Multi-Architecture Binary Dataset* (2025)
+- **Paper**: [Binary-30K: A Heterogeneous Dataset for Deep Learning in Binary Analysis and Malware Detection](https://huggingface.co/papers/2511.22095)
 - **Code & Documentation**: [github.com/mjbommar/binary-dataset-paper](https://github.com/mjbommar/binary-dataset-paper)
 - **Technical Documentation**: See [DATASET_SPLITS.md](https://github.com/mjbommar/binary-dataset-paper/blob/master/DATASET_SPLITS.md) for detailed stratification methodology
 
@@ -280,4 +282,4 @@ If you use this dataset in your research, please cite:
 
 ---
 
-*Last Updated: November 15, 2025*
+*Last Updated: November 15, 2025*