FysicsWorld: A Unified Full-Modality Benchmark for Any-to-Any Understanding, Generation, and Reasoning
🚀 News
2025.12.14We release 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.
🎯 FysicsWorld Overview
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:
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).
Fusion-Dependent Cross-Modal Reasoning. We propose a method for omni-modal data construction, which is named Cross-Modal Complementarity Screening (CMCS) strategy, which ensures that our tasks maintain strong cross-modal coupling, preventing single-modality shortcuts and enforcing true synergistic perception of omni-modality.
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 10+ authentic voices and tones.
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.
🔍 Dataset Download
The full dataset, including associated multimedia files (images, videos, and audio), can be downloaded from:
🔮 Evaluation
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 (full FysicsWorld) and ./test-mini (test-mini), respectively.
🕹️ Usage:
- Download the full FysicsWorld dataset from here.
- Run inference using your model on the provided questions.
- Follow the guidelines, and format the model responses according to the required submission format.
- Send the formatted responses to dicken@fyscis.ai. We will periodically update the corresponding scores on the leaderboard.
📈 Experimental Results
- Evaluation results of Omni-LLMs and proprietary MLLMs on image-centric omni-modal tasks
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).
- Evaluation results of Omni-LLMs and proprietary MLLMs on video-centric omni-modal tasks.
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).
- Evaluation results of open-source MLLMs on modality-supported tasks.
Task abbreviations: Task1-1 (Image Understanding), Task1-2 (Video Understanding), and Task3-6 (Next-Action Prediction from Video Sequences and Current Visual State).
- Evaluation results of various models on (a) Audio Reasoning and (b) Video Generation.
📖 Citation
If you find FysicsWorld helpful for your research, please consider citing our work. Thanks!
@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:2512.12756},
year={2025}
}