WuJie·Emu3.5 — The Next Leap in Multimodal World Models

Introduction

In October 2024, the Beijing Academy of Artificial Intelligence (BAAI) released the world’s first natively multimodal world model — WuJie·Emu3.

This groundbreaking model is based entirely on next-token prediction, avoiding diffusion or composite methods, and achieves a unified representation across images, text, and video.

One year later, BAAI launched WuJie·Emu3.5, extending the Next-Token Prediction paradigm to Next-State Prediction (NSP) for multimodal sequences. This evolution mimics human-style learning and provides generalized world modeling capabilities.

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Core Principles

> "The essence of a world model is predicting the next spatio-temporal state." — Wang Zhongyuan, BAAI Director

Wang emphasized that world modeling is vital for embodied intelligence — far beyond just handling video or images.

Real-World Analogy

Humans naturally form multimodal understandings of their environments (e.g., seeing coffee near the edge of a table and anticipating the risk of falling).

Robots interacting in such scenarios must precisely control force, timing, and direction.

📄 Paper: Read on arXiv

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Key Enhancements in Emu3.5

Emu3.5 introduces three major upgrades:

• Intention to Planning
• Understand high-level human goals (e.g., build a spaceship, make latte art)
• Autonomously generate multi-step action plans
• Dynamic World Simulation
• Unified framework for understanding → planning → simulation
• Can predict physical dynamics, spatio-temporal evolution, and causal relationships
• Foundation for Generalized Interaction
• Emergent causal reasoning and planning
• Supports human-AI interaction in physical and digital contexts

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The Third Scaling Paradigm

> "Emu3.5 may mark the beginning of the third scaling paradigm." — Wang Zhongyuan

From Language Models to Multimodal Scaling

• Language Scaling: Increased parameters, datasets, and compute boosted LLM performance
• Challenge: Text data is nearing saturation
• Recent Trend: Scaling via post-training & inference stages to unlock potential

Multimodal AI lacked a mature scaling approach — until now.

Why Emu3.5 Can Scale

• Autoregressive Design — Unified architecture for multimodal data
• Large-Scale Multimodal RL — First successful application in an autoregressive multimodal model
• Performance Gains — Production-level results, prepped for industrial adoption

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Back to First Principles

Humans start learning visually — not with text.

With the ubiquity of video, AI models now have an ideal medium for world knowledge learning:

• Understanding operational rules
• Building causal reasoning
• Learning physical common sense

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Limitations of Current Approaches

Most AI systems separate understanding from generation.

This often leads to:

• Forgetting — Memory limitations remain unsolved
• Poor Agent Optimization — Limited usability in applied contexts

Emu series solves this using a unified autoregressive architecture where the next token may be visual or text, with no performance trade-off.

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Real-World Applications

Platforms like AiToEarn integrate such models into content workflows:

• AI-Driven Content Creation
• Cross-Platform Publishing (Douyin, Kwai, Instagram, YouTube, X, etc.)
• Revenue Opportunities for creators & researchers

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Technological Innovations

1. Two-Stage End-to-End Pretraining

• Stage 1 & 2: ~13 trillion tokens
• Data diversity: Higher resolution, quality, annotation richness
• Unified multimodal generation interface
• Final steps: SFT (150B samples) → Large-scale RL → DiDa tech for fast inference

Dataset Highlights:

• 10T+ tokens
• 63M videos (avg. 6.5 min)
• Total: ~790 years of video time
• Covers real-world & imaginative domains
• Only 1% of publicly available online videos

> Parameter Size: 34B — significant room for scaling

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2. Large-Scale Native Multimodal RL

Key achievements:

• Unified multitask multimodal RL
• Reward system designed for generality, task-specificity, and uniformity
• Handles complex multimodal outputs: text → image, text + image → image edit

Example: Generating sequences like “taking out a phone step-by-step” or “pouring water” — with realism and interaction.

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3. Practical Breakthroughs in Inference Acceleration

Challenge: Autoregressive models are slower than diffusion-based ones

Solution: DiDa Technology — lossless next-token prediction acceleration → 20× speed boost

How DiDa Works

• Extends discrete diffusion formula to visual tokens
• Generates initial image token sequence in parallel
• Refines via multiple discrete denoising steps

> “A 20× acceleration drastically reduces the cost of native multimodal processing.” — Wang Xinlong

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Conclusion

WuJie·Emu3.5 stands out as a multimodal world model that can:

• Understand and generate across space, time, and modalities
• Maintain long-term consistency
• Reason causally about physical and temporal events

It may open a new track in large model evolution — bridging theoretical AI capabilities with real-world creator economies.

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🔗 Related Platform: AiToEarn官网 — public, open-source AI monetization ecosystem for cross-platform distribution and analytics.

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Would you like me to also produce a side-by-side comparison chart between Emu3 and Emu3.5 to make the upgrades clearer? This could make the Markdown even more reader-friendly.