arxiv_ai 98% Match Research Paper AI Researchers,ML Engineers,Developers of large-scale AI systems,Researchers focused on AGI 1 week ago

Ming-Flash-Omni: A Sparse, Unified Architecture for Multimodal Perception and Generation

large-language-models › multimodal-llms

📄 Abstract

Abstract: We propose Ming-Flash-Omni, an upgraded version of Ming-Omni, built upon a sparser Mixture-of-Experts (MoE) variant of Ling-Flash-2.0 with 100 billion total parameters, of which only 6.1 billion are active per token. This architecture enables highly efficient scaling (dramatically improving computational efficiency while significantly expanding model capacity) and empowers stronger unified multimodal intelligence across vision, speech, and language, representing a key step toward Artificial General Intelligence (AGI). Compared to its predecessor, the upgraded version exhibits substantial improvements across multimodal understanding and generation. We significantly advance speech recognition capabilities, achieving state-of-the-art performance in contextual ASR and highly competitive results in dialect-aware ASR. In image generation, Ming-Flash-Omni introduces high-fidelity text rendering and demonstrates marked gains in scene consistency and identity preservation during image editing. Furthermore, Ming-Flash-Omni introduces generative segmentation, a capability that not only achieves strong standalone segmentation performance but also enhances spatial control in image generation and improves editing consistency. Notably, Ming-Flash-Omni achieves state-of-the-art results in text-to-image generation and generative segmentation, and sets new records on all 12 contextual ASR benchmarks, all within a single unified architecture.

Authors (58)

Inclusion AI

Bowen Ma

Cheng Zou

Canxiang Yan

Chunxiang Jin

+52 more

Submitted

October 28, 2025

arXiv Category

cs.CV

arXiv PDF

Key Contributions

Ming-Flash-Omni presents a sparse Mixture-of-Experts (MoE) architecture with 100B parameters (6.1B active per token), achieving highly efficient scaling and unified multimodal intelligence across vision, speech, and language. It demonstrates state-of-the-art performance in contextual ASR and significantly advances image generation quality, marking a step towards AGI.

Business Value

Paves the way for more powerful and efficient AI systems capable of understanding and generating content across multiple modalities, accelerating progress towards AGI and enabling new applications in various industries.

Paper Metadata

Innovation Type

Model Architecture and Scaling

Deployment Feasibility

Moderate. While efficient for its size, deploying a 100B parameter model requires significant computational resources. The sparse MoE design helps.

Limitations Addressed

Computational inefficiency of large dense models,Limited unified capabilities across multiple modalities,Performance ceilings in specific tasks like ASR and image generation

Performance Gains

Substantial improvements across multimodal understanding and generation; state-of-the-art in contextual ASR; marked gains in image generation quality.

Technical Tags

multimodal AIMixture-of-Experts (MoE)sparse architecturelarge modelefficient scalingvisionspeechlanguageASRimage generationAGIMing-Flash-Omni

Research Topics

Multimodal LearningArtificial General Intelligence (AGI)Large Model ArchitecturesEfficient AISpeech RecognitionImage Generation

Methods & Architectures

Mixture-of-Experts (MoE) architectureSparse model designUnified multimodal processingParameter-efficient scaling Mixture-of-Experts (MoE)Sparse Transformer variants

Applications & Tasks

Artificial General Intelligence (AGI) Human-Computer Interaction Content Creation Speech Technology Computer Vision Achieving Unified Multimodal IntelligenceEfficiently Scaling Large ModelsImproving Speech RecognitionEnhancing Image Generation Quality Multimodal understanding (vision, speech, language)Multimodal generation (image, speech)Contextual Automatic Speech Recognition (ASR)Dialect-aware ASRHigh-fidelity image generation

Datasets & Benchmarks

Benchmarks

State-of-the-art performance in contextual ASR • Highly competitive results in dialect-aware ASR

Accuracy (ASR)Performance metrics for image generation (fidelity, consistency, identity preservation)

Related Fields

Artificial IntelligenceMachine LearningDeep LearningNatural Language ProcessingComputer VisionSpeech Processing

Keywords

Multimodal AIMixture-of-ExpertsMoESparse ModelLarge Language ModelAGIVisionSpeechLanguageASRImage GenerationEfficient ScalingMing-Flash-Omni

Academic Context

#Multimodal Learning#Artificial General Intelligence (AGI)#Large Model Architectures#Efficient AI#Speech Recognition#Image Generation

Commercial Potential

Potential Products

Highly capable multimodal AI assistantsAdvanced content generation platformsNext-generation speech recognition systems

Target Industries

TechnologyMedia & EntertainmentAutomotiveRoboticsHealthcare

Use Case Examples

AI systems that can see, hear, speak, and generate contentAdvanced virtual assistantsTools for creating realistic multimodal content

Competitive Edge

Represents a significant advancement in large-scale multimodal architectures, particularly through its sparse MoE design, pushing the boundaries of unified AI capabilities.

Market Opportunity

Vast, as it targets foundational capabilities for future AI systems and AGI.

Revenue Models

Licensing of the model/APIcloud-based AI services.

Resource Requirements

Compute Needs

Very High, due to the 100B parameter scale, even with sparse activation.

Data Requirements

Massive, diverse multimodal datasets covering vision, speech, and language tasks.

Deployment Constraints

High computational cost for inference,Memory requirements,Complexity of managing sparse MoE models

Scalability

Designed for efficient scaling via the sparse MoE architecture, allowing for significant parameter expansion while managing active computation.

Regulatory Considerations

Ethical implications of powerful AGI-like systemspotential for misuse.

Production Readiness

Maturity Level

Research

Time to Market

3-5 years for widespread commercial deployment due to resource requirements.

Patent Potential

High, for the novel sparse MoE architecture and its multimodal capabilities.

View Full Paper Back to Papers