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| <a target="_blank"><img src="figs/FysicsWorld-logo.png" alt="" style="width: 50%; min-width: 200px; display: block; margin: auto;"></a> |
| </p> |
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| <h1>FysicsWorld: A Unified Full-Modality Benchmark for Any-to-Any Understanding, Generation, and Reasoning</h1> |
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| <font size=3><div align='center' > [[🏠 Project Page](https://github.com/Fysics-AI/FysicsWorld)] [[📖 arXiv Paper](https://arxiv.org/pdf/2512.XXXX)] [[🤗 Dataset](https://huggingface.co/datasets/Fysics-AI/FysicsWorld)] [[🏆 Leaderboard](https://huggingface.co/spaces/Fysics-AI/FysicsWorld-Leaderboard)] </div></font> |
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| ## 🚀 News |
| * **`2025.12.14`** We release [***FysicsWorld***](https://huggingface.co/datasets/Fysics-AI/FysicsWorld), the first unified full-modality benchmark that supports bidirectional input–output across image, video, audio, and text, enabling comprehensive any-to-any evaluation across understanding, generation, and reasoning. |
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| ## 🎯 ***FysicsWorld*** Overview |
| <img src="figs/fig-teaser.jpg" width="100%" height="100%"> |
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| We introduce ***FysicsWorld***, the **first** unified full-modality benchmark that supports bidirectional input–output across *image, video, audio, and text*, enabling comprehensive any-to-any evaluation across understanding, generation, and reasoning. Our systematic design spans uni-modal perception tasks to fusion-dependent reasoning under strong cross-modal coupling, allowing us to diagnose, with unprecedented clarity, the limitations and emerging strengths of modern multimodal and omni-modal architectures. In contrast to existing omni-modal and multi-modal benchmarks, our ***FysicsWorld*** has several advantages: |
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| * **Diversity and High Quality**. ***FysicsWorld*** is characterized by **8 "*multi*"** properties, reflecting its comprehensive coverage, diversity, and robustness, namely: |
| *multi-dimensional* (understanding, generation, reasoning, voice interaction), *multi-modal* (text, image, video, audio as both inputs and outputs), *multi-task* (16 primary tasks, 200+ sub-tasks), *multi-source* (3,268 samples from 40+ data sources and curated web data), *multi-domain* (170+ fine-grained open-domain categories), *multi-type* (closed-ended, open-ended, multiple-choice question, and image/video/audio generation), *multi-target* (evaluates Omni-LLMs, MLLMs, modality-specific models, unified understanding–generation models), and *multi-assurance* (multi-stage quality control strategies). |
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| * **Fusion-Dependent Cross-Modal Reasoning**. We propose a method for omni-modal data construction, which is named **C**ross-**M**odal **C**omplementarity **S**creening (**CMCS**) strategy, which ensures that our tasks maintain strong cross-modal coupling, preventing single-modality shortcuts and enforcing true synergistic perception of omni-modality. |
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| * **Speech-Driven Cross-Modal Interaction**. To support natural, multimodal communication and interaction, we develop a speech-grounded multimodal data construction pipeline that ensures both linguistic fluency and semantic fidelity in voice-based interactions, including 20+ authentic voices and tones. |
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| Based on ***FysicsWorld***, we extensively evaluate various advanced models, including Omni-LLMs, MLLMs, modality-specific models, and unified understanding–generation models. By establishing a unified benchmark and highlighting key capability gaps, FysicsWorld provides not only a foundation for evaluating emerging multimodal systems but also a roadmap for the next generation of full-modality architectures capable of genuinely holistic perception, reasoning, and interaction. |
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| <p align="center"> |
| <img src="figs/fig-statiscs.jpg" width="100%" height="100%"> |
| </p> |
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| ## 🔍 Dataset Download |
| The full dataset, including associated multimedia files (images, videos, and audio), can be downloaded from [here](https://huggingface.co/datasets/Fysics-AI/FysicsWorld). |
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| ## 🔮 Evaluation |
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| To ensure a fair and standardized evaluation protocol, we release the full ***FysicsWorld*** dataset with ground-truth answers withheld, along with a test-mini subset (300 samples) that includes answers for local validation and debugging. You can find the QA data in [./data](https://github.com/Fysics-AI/FysicsWorld/tree/main/data) (full ***FysicsWorld***) and [./test-mini](https://github.com/Fysics-AI/FysicsWorld/tree/main/test-mini) (test-mini), respectively. |
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| 🕹️ **Usage**: |
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| 1. Download the full FysicsWorld dataset from [here](https://huggingface.co/datasets/Fysics-AI/FysicsWorld). |
| 2. Run inference using your model on the provided questions. |
| 3. Format the model responses according to the required [submission format](https://github.com/Fysics-AI/FysicsWorld/blob/main/eval/submission_format.json). |
| 4. Send the formatted responses to *t1.jiangyue@outlook.com*. We will periodically update the corresponding scores on the leaderboard. |
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| ## 📈 Experimental Results |
| - **Evaluation results of Omni-LLMs and proprietary MLLMs on image-centric omni-modal tasks** |
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| <p align="center"> |
| <img src="figs/tab-image.png" width="90%" height="100%"> |
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| *Task abbreviations:* |
| Task1-1 (Image Understanding), Task2-1 (Speech-Driven Image Understanding), Task2-2 (Image–Audio Contextual Reasoning), Task2-3 (Speech-Based QA on Image Content), Task2-4 (Speech Generation from a Person in an Image), and Task2-5 (Audio Matching from Image Context). |
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| - **Evaluation results of Omni-LLMs and proprietary MLLMs on video-centric omni-modal tasks.** |
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| <p align="center"> |
| <img src="figs/tab-video.png" width="90%" height="100%"> |
| </p> |
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| *Task abbreviations:* |
| Task1-2 (Video Understanding), Task3-1 (Speech-Driven Video Understanding), Task3-2 (Video–Audio Contextual Reasoning), Task3-3 (Speech-Based QA on Video Content), Task3-4 (Speech Generation from a Person in an Video), Task3-5 (Audio Matching from Video Context), and Task3-6 (Next-Action Prediction from Video Sequences and Current Visual State). |
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| - **Evaluation results of open-source MLLMs on modality-supported tasks.** |
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| <p align="center"> |
| <img src="figs/fig-open-mllm.jpg" width="60%" height="100%"> |
| </p> |
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| *Task abbreviations:* |
| Task1-1 (Image Understanding), Task1-2 (Video Understanding), and Task3-6 (Next-Action Prediction from Video Sequences and Current Visual State). |
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| - **Evaluation results of various models on (a) Audio Reasoning and (b) Video Generation.** |
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| <p align="center"> |
| <img src="figs/fig-exp-audio-video.jpg" width="90%" height="100%"> |
| </p> |
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| ## 📖 Citation |
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| If you find ***FysicsWorld*** helpful for your research, please consider citing our work. Thanks! |
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| ```bibtex |
| @article{jiang2025fysicsworld, |
| title={FysicsWorld: A Unified Full-Modality Benchmark for Any-to-Any Understanding, Generation, and Reasoning}, |
| author={Jiang, Yue and Yang, Dingkang and Han, Minghao and Han, Jinghang and Chen, Zizhi and Liu, Yizhou and Li, Mingcheng and Zhai, Peng and Zhang, Lihua}, |
| journal={arXiv preprint arXiv:2512.XXXX}, |
| year={2025} |
| } |
| ``` |
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