Update dataset card: task category, paper link, and usage instructions

README.md

@@ -1,24 +1,25 @@
 ---
-license: apache-2.0
-task_categories:
-- visual-question-answering
 language:
 - en
 - zh
 - ko
 - ja
 - fr
+license: apache-2.0
+task_categories:
+- image-text-to-text
 configs:
-  - config_name: default
-    data_files:
-      - split: test
-        path: MMTR.jsonl
+- config_name: default
+  data_files:
+  - split: test
+    path: MMTR.jsonl
 ---
+
 # MMTR-Bench: Multimodal Masked Text Reconstruction Benchmark
-<div align="center">
+<div align="center Krank">
   <a href="https://mmtr-bench-dataset.github.io/MMTR-Bench/">🏠 HomePage</a> |
-  <a href="#">📊 Dataset</a> |
-  <a href="#">📄 Paper</a> |
+  <a href="https://huggingface.co/datasets/MMTR-Bench/MMTR-Bench-Dataset">📊 Dataset</a> |
+  <a href="https://huggingface.co/papers/2604.21277">📄 Paper</a> |
   <a href="https://github.com/MMTR-Bench-Dataset/MMTR-Bench-eval">💻 Code</a>
 </div>
 
@@ -35,12 +36,12 @@ To solve the task, models must recover the hidden text by relying on the remaini
 
 MMTR-Bench evaluates a model's ability to maintain a continuous, structured reading flow across complex multimodal layouts. The dataset is rigorously balanced across various dimensions to ensure a comprehensive evaluation of current Multimodal Large Language Models (MLLMs).
 
-![Dataset Overview](./png/overview.png)
+![Dataset Overview](https://mmtr-bench-dataset.github.io/MMTR-Bench/static/images/overview.png)
 
 The distributions in the dataset highlight our multi-faceted evaluation strategy:
-* **Difficulty Level & Context Mode (a):** The dataset is categorized into four distinct difficulty levels (L1 to L4), scaling from word-level completion to complex paragraph-level reconstruction. It incorporates both Single Context (single-page) and Multi Context (multi-page) scenarios, demanding robust cross-page reasoning.
-* **Answer Length Distribution (b):** Target texts span a wide spectrum of character lengths, ensuring models are tested on both concise factual recall and extended, coherent text generation based on visual context.
-* **Mask Ratio Distribution (c):** The proportion of masked content varies dynamically across difficulty levels, pushing the boundaries of how much missing information a model can infer purely from surrounding document structures and visual semantics.
+* **Difficulty Level & Context Mode:** The dataset is categorized into four distinct difficulty levels (L1 to L4), scaling from word-level completion to complex paragraph-level reconstruction. It incorporates both Single Context (single-page) and Multi Context (multi-page) scenarios, demanding robust cross-page reasoning.
+* **Answer Length Distribution:** Target texts span a wide spectrum of character lengths, ensuring models are tested on both concise factual recall and extended, coherent text generation based on visual context.
+* **Mask Ratio Distribution:** The proportion of masked content varies dynamically across difficulty levels, pushing the boundaries of how much missing information a model can infer purely from surrounding document structures and visual semantics.
 
 ---
 
@@ -48,7 +49,7 @@ The distributions in the dataset highlight our multi-faceted evaluation strategy
 
 The benchmark assesses models using a specialized level-aware scoring mechanism to account for the varying complexities of L1 through L4 tasks. The inclusion of explicit reasoning ("Thinking") models reveals a significant paradigm shift in how MLLMs approach visual text reconstruction.
 
-![Model Benchmark Comparison](./png/model_bench.png)
+![Model Benchmark Comparison](https://mmtr-bench-dataset.github.io/MMTR-Bench/static/images/model_bench.png)
 
 ### Main Results
 
@@ -69,19 +70,12 @@ The benchmark assesses models using a specialized level-aware scoring mechanism
 | Qwen3.5-112B-A10B | | 18.56 | 15.47 | 18.79 | 13.62 | 19.31 | 23.40 | 18.00 |
 | Qwen3-VL-8B-Instruct | | 12.16 | 11.38 | 7.94 | 7.12 | 14.19 | 20.11 | 12.02 |
 
-### Key Observations
-1. **The Power of Explicit Reasoning:** Models utilizing a "Think" mechanism consistently outperform their standard instruction-tuned counterparts. For instance, the reasoning-enabled `Qwen3.5-397B-A17B` achieves a Final score of 33.84%, compared to 23.29% without it. This underscores the necessity of chain-of-thought processing when parsing end-to-end multimodal documents.
-2. **Multi-page Degradation:** Across almost all models, performance drops significantly in the Multi-page setting compared to Single-page, highlighting a critical gap in current architectures' ability to sustain long-context visual reasoning.
-3. **Difficulty Scaling:** Performance steeply declines as the difficulty progresses from L1 (word-level) to L4 (paragraph-level). Even the leading model, Gemini-3.1-Pro, struggles at L4 (31.86%), proving that MMTR-Bench leaves ample headroom for future research in multimodal document understanding.
-
 ---
 
 ## 🚀 How to Use
 
 MMTR-Bench is an evaluation-only benchmark designed to test Multimodal LLMs. There is no training set. 
 
-The dataset annotations (including mask bounding boxes, ground truth answers, and image paths) are stored in the metadata file, and the images are located in the `images/` directory.
-
 ### 1. Installation
 Ensure you have the required libraries installed:
 ```bash
@@ -94,12 +88,11 @@ pip install datasets
 from datasets import load_dataset
 
 # Load the benchmark dataset
-# Note: Hugging Face maps single data files to the 'train' split by default.
 dataset = load_dataset(
-    "MMTR-Bench/MMTR_Bench_Dataset", 
-    data_files="metadata.json" # or metadata.jsonl
+    "MMTR-Bench/MMTR-Bench-Dataset", 
+    data_files="MMTR.jsonl"
 )
-benchmark_data = dataset["train"]
+benchmark_data = dataset["test"]
 
 # Inspect the first evaluation sample
 sample = benchmark_data[0]
@@ -107,8 +100,7 @@ sample = benchmark_data[0]
 print(f"Sample ID: {sample['sample_id']}")
 print(f"Difficulty Level: L{sample['level']}")
 print(f"Ground Truth Answer: {sample['answer']}")
-print(f"Mask Bounding Box: {sample['bbox']}")
-print(f"Target Image: {sample['image_path']}")
+print(f"Context Image Paths: {sample['context_img_paths']}")
 ```
 
 ---
@@ -119,9 +111,9 @@ If you find our benchmark, models, or data useful in your research, please consi
 
 ```bibtex
 @article{mmtrbench2026,
-  title={MMTR-Bench: Multimodal Masked Text Reconstruction Benchmark},
+  title={Can MLLMs "Read" What is Missing?},
   author={Anonymous Authors},
-  journal={Under Review},
+  journal={arXiv preprint arXiv:2604.21277},
   year={2026}
 }
 ```