AI enters era of world models as industry shifts

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The artificial intelligence industry is transitioning from a period defined by content generation to a new phase focused on understanding the physical world. During the…
AI enters era of world models as industry shifts
A smartphone screen shows a user asking the Claude 3.7 Sonnet AI model for restaurant recommendations in Tampa, highlighting the practical use of AI assistants / © AERPS

The artificial intelligence industry is transitioning from a period defined by content generation to a new phase focused on understanding the physical world. During the World Artificial Intelligence Conference in July 2026, Fang Han, the chairman and chief executive of Kunlun Tech, identified 2026 as the inaugural year of world models. This shift marks a move from the previous two-year focus on generating text, images, video and music towards a stage where AI comprehends and interacts with physical reality.

This technological transition is being compared to the historical impact of photography. Jiao Juan, a chief analyst at Founder Securities, noted that the invention of photography disrupted the economic foundations of realist painting before eventually giving rise to cinema. This suggests that significant technological revolutions often start by restructuring existing supply systems rather than simply creating new abundance.

A major technical challenge for world models has been long-term memory. Previous models relied on storing entire frames, which meant they often failed to recognize objects when camera angles changed. Evaluation data showed that no mainstream world model achieved an object-reappearance score higher than 0.6 out of 1.0.

Skywork AI has attempted to resolve these issues with its new interactive world model, Matrix-Game 3.5. Instead of storing complete frames, the system utilizes a mechanism known as Patch Memory. This method breaks each frame into many small patches, each assigned precise three-dimensional coordinates detailing its distance from the camera and its spatial position.

The model has shifted from simply storing individual frames to mapping out entire spaces, allowing it to pinpoint what’s present at a specific point in three-dimensional space, rather than just recalling a previous visual. This tackles the long-standing issue of disappearing objects by leveraging PRoPE geometric position encoding and the Warped RoPE mechanism, paving the way for a significant change in how artificial intelligence interacts with the physical world.

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Nov 3, 2026 at 12:54 am
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3 min read (415 words)
Published
August 14, 2026
Last Updated
August 14, 2026 at 4:39 am
Editorial Analysis
The AI industry is shifting from simple content generation toward "world models" that can comprehend and interact with physical reality. While long-term memory and spatial consistency have historically hindered these models, Skywork AI’s Matrix-Game 3.5 addresses these gaps using a Patch Memory mechanism for precise 3D coordinate mapping. This transition represents a fundamental restructuring of AI capabilities, moving from visual recall to a sophisticated understanding of three-dimensional space.
Article Facts
Industry Shift: The AI industry is transitioning from content generation to understanding the physical world, with 2026 marked as the inaugural year of world models.
Technical Challenge: A major challenge for world models has been long-term memory, with previous models struggling to recognize objects when camera angles changed, achieving object-reappearance scores no higher than 0.6 out of 1.0.
Innovative Solution: Skywork AI’s Matrix-Game 3.5 model addresses this issue with its Patch Memory mechanism, breaking frames into small patches with precise 3D coordinates to improve object recognition.
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