Once Again, Pushing the Limits of World Models

A new benchmark has been set in the race for world models.

The Beijing Academy of Artificial Intelligence (BAAI) has announced its large-scale multimodal world model — Wujie·Emu3.5.

It not only simulates complex, dynamic physical realities with remarkable realism, but also reveals the existence of a Multimodal Scaling Paradigm.

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Key Capabilities Demonstrated

• World Exploration
• Embodied Operations
• Visual Guidance
• Example: Prompted with “How to make shrimp and celery dumplings”, the model generates a step-by-step, accurate and coherent recipe — covering ingredient preparation, mixing, wrapping, boiling, and plating — along with vivid, realistic images.

• Visual Storytelling
• Image Editing and Generation

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The Context: World Model Competition

From OpenAI’s Sora to DeepMind’s Genie, and from Yann LeCun’s JEPA to Fei-Fei Li’s Marble, researchers and tech giants are converging on the potential of world models.

Yet, as visual realism approaches perfection, deeper questions arise:

> Is there a scalable, first-principles path enabling a model to autonomously learn the causal laws of the world from vast multimodal data — and truly comprehend physical reality?

BAAI’s Wujie·Emu3.5 is designed to address this challenge.

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01 — Wujie·Emu3.5: Power in Simplicity and Scale

October 2024 marked the release of Wujie·Emu3, a native multimodal world model built on a single Transformer for next-token prediction. It unified text, images, and video without diffusion models or hybrids, attracting major industry attention.

But new challenges emerged:

• How to learn efficiently from long videos
• How to achieve truly general multimodal interaction
• How to keep inference efficient at trillion-token scales

Emu3.5 addresses these with a simple, unified Next-State Prediction (NSP) objective and a broadly applicable architecture — at massive scale.

Core Breakthroughs

• Native Multimodal Unified Architecture
• Single 34B dense Transformer
• Uniform encoding of all modalities into discrete token sequences
• End-to-end autoregressive processing for true modality unification
• Single Training Objective: Predict the Next State
• Predicts the next probable state in interleaved visual-language sequences
• Forces the model to learn the “grammar” of the dynamic world — like LLMs learn language via next-token prediction
• Massive Multimodal Dataset (>10 Trillion Tokens)
• ~790 years of training-time video + speech transcripts
• Rich spatiotemporal and causal information through Video-Text Interleaved Data

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💡 Tip for Creators & Researchers:

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• AI generation tools
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• Analytics and model rankings

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4 — First-Ever Revelation of the Multimodal Scaling Paradigm

The Emu3.5 technical report shows: as compute during pretraining scales, the model’s error rate on out-of-distribution tasks drops predictably and smoothly.

This means its embodied world knowledge generalizes stably beyond training data.

Scale & Performance Gains

• Parameters: 8B → 34B
• Video-data scale: 15 years → 790 years
• Key metric gains: temporal consistency, cross-modal reasoning, embodied interaction planning

Significance: Comparable to GPT-3’s revelation of the language model scaling law.

Scaling laws make progress predictable, moving world model research from alchemy to engineering — enabling confident investment.

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02 — Opening a New Era for Multimodal World Models

Strategic Importance

World models are critical for:

• General-purpose robotics
• L5 autonomous driving
• Artificial General Intelligence (AGI)

Wujie·Emu3.5’s native multimodality + unified autoregression paradigm, combined with its scaling insights, set a clear R&D roadmap:

• Future competitions will focus on scale, data richness, and physical-world comprehension — not just video quality.

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Recommended Reading

• 
• From “WuDao” to “WuJie”: ZhiYuan Steps into the New Era of Large Models
• 

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Toward Collective Intelligence

BAAI’s Cross-Ontology Embodied Large–Small Brain Framework

BAAI has released:

• A cross-ontology embodied “large brain–small brain” framework
• An open-source embodied brain

This addresses the challenge of heterogeneous intelligent agents — having different bodies, sensors, and action spaces — cooperating in complex, dynamic environments.

Cross-Ontology Embodied Intelligence

Traditional assumption: All agents share the same ontology (perception, actions, world view).

Reality:

• Biped robots, drones, simulation agents vary dramatically in sensors & controls.
• Cooperation demands bridging ontological differences.

BAAI’s approach:

• “Large brain”: High-level planning, strategy, reasoning
• “Small brains”: Modality-specific perception, control, execution
• Enables flexible, robust cooperation among diverse agents

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Open-Sourcing the Embodied Brain

An embodied brain integrates:

• Perception
• Decision-making
• Action control

For physical robots, virtual avatars, or simulation agents.

BAAI’s open-source release invites community experimentation — fostering interoperability standards between heterogeneous AI agents.

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Significance for Collective Intelligence

Potential applications:

• Robotics
• Industrial automation
• Swarm systems
• Search & rescue
• Multi-agent simulations

Fits into AI collaboration ecosystems — modular, open frameworks to scale abilities by combining specialized agents into powerful collectives.

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Looking Ahead

• Open platforms & interoperability are essential.
• AiToEarn官网 demonstrates how AI generation + multi-platform distribution + analytics + monetization can amplify both creative and technical work.

Future AI development will converge on seamless AI-human and AI-AI collaboration, powered by scalable, open, and interoperable architectures.

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Would you like me to also create a summary infographic-style section for Emu3.5, so that readers can grasp the specs and breakthroughs in under 30 seconds? That would make the Markdown even more digestible.