MMTR-Bench/MMTR-Bench-Dataset

MMTR-Bench: Multimodal Masked Text Reconstruction Benchmark

📖 Abstract

We present MMTR-Bench (Multimodal Masked Text Reconstruction Benchmark) to evaluate native visual context reconstruction in complex multimodal inputs. Unlike traditional question-answering tasks, MMTR-Bench presents models with masked single- or multi-image inputs from diverse real-world scenarios, such as documents and webpages.

To solve the task, models must recover the hidden text by relying on the remaining layout structure, visual cues, and relevant world knowledge. By removing question-based guidance, this task challenges models to autonomously parse and reason over complex visual structures, testing their fundamental capacity for end-to-end document parsing and structured reading. The benchmark contains 2,771 test samples spanning multiple languages and varying target lengths. To fairly assess this diversity, we introduce a level-aware scoring mechanism. Extensive experiments on representative models demonstrate that MMTR-Bench remains highly challenging, particularly for sentence- and paragraph-level recovery.

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📊 Dataset Overview

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).

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.

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🏆 Leaderboard & Evaluation

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.

Main Results

Note: "Think" marks models with explicit reasoning capabilities, except for variants explicitly marked as "nothink" or "Instruct". All numbers are reported as percentages.

Models	Think	Single-page	Multi-page	L1	L2	L3	L4	Final
Gemini-3.1-Pro	✅	42.57	38.70	64.17	44.64	37.50	31.86	41.87
GPT5.4-High	✅	41.00	30.98	57.46	41.20	35.72	30.92	39.18
Gemini-3-Flash	✅	38.49	34.90	56.75	38.51	34.86	29.46	37.84
GPT5.2-High	✅	36.64	37.62	51.49	38.61	34.02	29.42	36.81
Doubao-Seed2-Medium	✅	37.06	31.96	52.46	36.10	33.63	31.28	36.13
GPT5.2-Medium	✅	35.39	36.61	50.27	37.22	32.72	30.51	35.61
Qwen3.5-397B-A17B	✅	34.67	30.10	48.39	34.67	31.46	26.68	33.84
Qwen3.5-122B-A10B	✅	30.37	23.94	43.91	27.23	27.84	23.92	29.20
Doubao-Seed1.6-Thinking	✅	25.50	23.01	33.81	22.10	24.74	25.02	25.04
Qwen3.5-397B-A17B		24.25	18.96	31.94	20.75	22.91	22.37	23.29
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.

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🚀 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:

pip install datasets

2. Loading the Benchmark

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
)
benchmark_data = dataset["train"]

# Inspect the first evaluation sample
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']}")

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📜 Citation

If you find our benchmark, models, or data useful in your research, please consider citing our paper:

@article{mmtrbench2026,
  title={MMTR-Bench: Multimodal Masked Text Reconstruction Benchmark},
  author={Anonymous Authors},
  journal={Under Review},
  year={2026}
}