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    Beyond LLMs: The Rise of World Models and WAMs

    23 min
    |
    |
    24 juin 2026
    AITechnologyScience

    Explore the shift from Large Language Models to World Models. Learn how World-Action Models (WAMs) bridge the grounding gap to understand the physical world.

    Beyond LLMs: The Rise of World Models and WAMs

    Meilleure citation de Beyond LLMs: The Rise of World Models and WAMs

    “

    We’re moving from the library to the playground, and that shift is backed by the idea that predicting the next pixel is more important for physical intelligence than predicting the next word.

    ”

    Cette leçon audio a été créée par un membre de la communauté BeFreed

    Question posée

    What's next beyond LLMs and Agents, specifically focusing on the development of world models and physical AI.

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    Sources de connaissances
    Pretrained to Imagine, Fine-Tuned to Act: The Rise of World-Action Models | NVIDIA Technical Blog
    link
    https://developer.nvidia.com/blog/pretrained-to-imagine-fine-tuned-to-act-the-rise-of-world-action-models/
    A Functional Taxonomy of World Models - by Fei-Fei Li
    link
    https://www.a16z.news/p/a-functional-taxonomy-of-world-models
    How Cosmos 3 Helps Physical AI Think Before It Acts | NVIDIA Blog
    link
    https://blogs.nvidia.com/blog/cosmos-3-physical-ai-open-world-foundation-model/
    Yann LeCun World Models Bet: AMI Labs Stakes $1.03 Billion Against Large Language Models
    link
    https://www.techtimes.com/articles/317928/20260606/yann-lecun-world-models-bet-ami-labs-stakes-103-billion-against-large-language-models.htm
    Yann LeCun’s World Model Vision: Inside AMI Labs and JEPA · KAD
    link
    https://www.kad8.com/ai/yann-lecuns-world-model-vision-inside-ami-labs-and-jepa/
    A review of π0.5 — Scaling Open-World Generalization in Vision-Language-Action Models | by Isaac Edelman | Toward Humanoids | Medium
    link
    https://medium.com/correll-lab/a-review-of-%CF%800-5-scaling-open-world-generalization-in-vision-language-action-models-e4cb0d74264e

    Foire aux questions

    Large Language Models are masters of text and language, often compared to someone who has read every book in a library but never stepped outside. While LLMs excel at explaining concepts, they often lack an understanding of physical reality. World Models and World-Action Models (WAMs) represent the next step in AI, moving beyond words to understand the physical consequences of actions and how the real world functions.

    The grounding gap refers to the disconnect between an AI's digital intelligence and its understanding of the physical world. As Yann LeCun points out, the world is not made of words, yet current LLMs are primarily trained on text. This gap means that while an AI can debate philosophy or physics, it may not understand basic physical interactions, such as how a shadow stretches or how to catch a falling glass.

    World-Action Models, or WAMs, are advanced AI systems that are essentially pretrained to imagine the physical outcomes of their actions before they take them. Unlike standard LLMs or simple digital agents, WAMs create a mental playground or simulation to predict consequences. This capability is essential for autonomous systems, such as robots cleaning a kitchen or self-driving cars navigating a storm, to function safely and effectively in real-life environments.

    Cree par des anciens de Columbia University a San Francisco

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    Points clés

    1

    From Libraries to Playgrounds: Why the Next AI Revolution Isn't Made of Words

    2

    The Mental Simulation Engine: Defining the True World Model

    3:52
    3

    Why Language Is an Underspecified Goal for Reality

    5:41
    7:46
    4

    The Three Roads to Embodied Intelligence

    9:50
    10:37
    5

    The Cost of a Machine's Imagination

    11:17
    11:43
    12:06
    6

    When the Dream Collapses: The Robustness Reality Check

    14:19
    7

    Beyond the Lab: World Models in the Wild

    17:37
    8

    Your Playbook for the Physical AI Era

    9

    Closing the Loop: From Text to Touch

    21:48

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