FysicsWorld: A Unified Full-Modality Benchmark for Any-to-Any Understanding, Generation, and Reasoning
FysicsWorld offers a full-modality benchmark supporting bidirectional I/O across image, video, audio, and text, covering 16 tasks.
Key Findings
Methodology
FysicsWorld employs the Cross-Modal Complementarity Screening (CMCS) strategy to construct datasets, supporting bidirectional I/O across image, video, audio, and text. The framework includes 16 primary tasks involving understanding, generation, and reasoning.
Key Results
- Among over 30 state-of-the-art baselines, FysicsWorld reveals performance disparities in understanding, generation, and reasoning, with significant improvements in cross-modal reasoning tasks.
- The CMCS strategy ensures task cross-modal coupling, preventing unimodal shortcuts and enhancing the authenticity of multimodal reasoning.
- Experimental results show that OmniLLMs excel in image and video understanding tasks, particularly in complex scenarios.
Significance
FysicsWorld provides a unified evaluation foundation and strong baselines for next-generation full-modality architectures, addressing gaps in modality coverage and interaction, and advancing multimodal intelligent systems.
Technical Contribution
FysicsWorld introduces the CMCS strategy and a systematic data construction framework, offering new engineering possibilities and ensuring task cross-modal coupling and authenticity, surpassing existing SOTA methods.
Novelty
FysicsWorld is the first full-modality benchmark supporting bidirectional I/O across image, video, audio, and text, offering comprehensive evaluation of understanding, generation, and reasoning.
Limitations
- In some complex real-world scenarios, model reasoning capabilities remain limited, especially in tasks requiring deep semantic understanding.
- The current benchmark may lack fine-grained evaluation for certain specific modalities.
Future Work
Future work could include expanding dataset scale and diversity, further optimizing the CMCS strategy, and exploring more practical application scenarios.
AI Executive Summary
FysicsWorld is an innovative full-modality benchmark designed to address the limitations of existing multimodal large language models in modality coverage and interaction. By introducing the Cross-Modal Complementarity Screening (CMCS) strategy, FysicsWorld supports bidirectional I/O across image, video, audio, and text, covering 16 primary tasks involving understanding, generation, and reasoning.
Experimental results demonstrate that FysicsWorld reveals performance disparities among over 30 state-of-the-art baselines in understanding, generation, and reasoning, with significant improvements in cross-modal reasoning tasks. The CMCS strategy ensures task cross-modal coupling, preventing unimodal shortcuts and enhancing the authenticity of multimodal reasoning.
FysicsWorld provides a unified evaluation foundation and strong baselines for next-generation full-modality architectures, addressing gaps in modality coverage and interaction, and advancing multimodal intelligent systems. Future work could include expanding dataset scale and diversity, further optimizing the CMCS strategy, and exploring more practical application scenarios.
Deep Analysis
Background
The rapid development of multimodal large language models (MLLMs) and full-modality architectures has driven the need for more comprehensive benchmarks. However, existing benchmarks have limitations in modality coverage and interaction, often restricted to text-centric outputs and lacking interdependence and complementarity among modalities.
Core Problem
The limitations in modality coverage and interaction of existing benchmarks restrict comprehensive evaluation of multimodal intelligent systems. A full-modality benchmark supporting bidirectional I/O is needed to fill this gap.
Innovation
FysicsWorld introduces the Cross-Modal Complementarity Screening (CMCS) strategy to ensure task cross-modal coupling and authenticity. Its framework supports bidirectional I/O across image, video, audio, and text, covering 16 primary tasks.
Methodology
- �� Introduce the CMCS strategy to ensure task cross-modal coupling.
- �� The dataset covers 16 primary tasks, supporting bidirectional I/O across image, video, audio, and text.
- �� A systematic data construction framework ensures high-quality and diverse data.
Experiments
The experimental design includes comprehensive evaluation of over 30 state-of-the-art baselines, covering understanding, generation, and reasoning tasks. Various datasets and evaluation metrics are used to ensure result reliability.
Results
Experimental results show significant improvements in cross-modal reasoning tasks, particularly in complex scenarios. The CMCS strategy ensures task cross-modal coupling.
Applications
FysicsWorld can be used to evaluate and advance multimodal intelligent systems, particularly in applications requiring multimodal interaction and reasoning.
Limitations & Outlook
In some complex real-world scenarios, model reasoning capabilities remain limited, especially in tasks requiring deep semantic understanding. The current benchmark may lack fine-grained evaluation for certain specific modalities.
Plain Language Accessible to non-experts
Imagine a large kitchen with various ingredients and utensils. FysicsWorld is like a master chef who can simultaneously handle and combine these ingredients to create delicious dishes. It not only understands the characteristics of each ingredient but also generates new recipes as needed and reasons through different cooking methods to ensure each dish is perfect.
ELI14 Explained like you're 14
Hey there, friends! Imagine you're playing a super cool game with various tasks that require different skills to complete. FysicsWorld is like a super helper that can handle images, videos, sounds, and text all at once, making you unstoppable in the game. It not only helps you understand every detail in the game but also generates new strategies to lead you to victory!
Glossary
Multimodal
Technology involving multiple sensory modalities such as vision, hearing, and language.
In FysicsWorld, multimodal refers to the integrated processing of images, videos, audio, and text.
Cross-Modal Complementarity Screening
A strategy ensuring task cross-modal coupling, preventing unimodal shortcuts.
Used in FysicsWorld's data construction to ensure the authenticity of multimodal tasks.
Full-Modality Benchmark
An evaluation platform supporting input-output across multiple modalities.
FysicsWorld is a full-modality benchmark supporting bidirectional I/O across image, video, audio, and text.
Generation
The process of creating new output content from input data.
In FysicsWorld, generation tasks include image and video generation.
Reasoning
The process of drawing conclusions from known information.
Reasoning tasks in FysicsWorld involve comprehensive analysis of multimodal information.
Open Questions Unanswered questions from this research
- 1 How to improve model reasoning capabilities in more complex real-world scenarios?
- 2 How to further optimize the CMCS strategy to enhance task cross-modal coupling?
Applications
Immediate Applications
Multimodal Intelligence Evaluation
FysicsWorld can be used to evaluate the performance of multimodal intelligent systems, helping researchers identify model strengths and weaknesses.
Long-term Vision
Full-Modality Interaction Systems
Through FysicsWorld's evaluation and optimization, more powerful full-modality interaction systems can be developed in the future, applicable in smart assistants, autonomous driving, and more.
Abstract
Despite rapid progress in multimodal large language models (MLLMs) and emerging omni-modal architectures, current benchmarks remain limited in scope and integration, suffering from incomplete modality coverage, restricted interaction to text-centric outputs, and weak interdependence and complementarity among modalities. To bridge these gaps, 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. FysicsWorld encompasses 16 primary tasks and 3,268 curated samples, aggregated from over 40 high-quality sources and covering a rich set of open-domain categories with diverse question types. We also propose the Cross-Modal Complementarity Screening (CMCS) strategy integrated in a systematic data construction framework that produces omni-modal data for spoken interaction and fusion-dependent cross-modal reasoning. Through a comprehensive evaluation of over 30 state-of-the-art baselines, spanning MLLMs, modality-specific models, unified understanding-generation models, and omni-modal language models, FysicsWorld exposes the performance disparities and limitations across models in understanding, generation, and reasoning. Our benchmark establishes a unified foundation and strong baselines for evaluating and advancing next-generation full-modality architectures.