Move over “chatbots with arms.” Meta AI has published a sweeping manifesto that recasts embodied intelligence as a world-model problem. The 40-page paper, Embodied AI Agents: Modeling the World (July 7, 2025), is signed by a who’s-who of researchers from EPFL, Carnegie Mellon, NTU and Meta’s own labs, and argues that any meaningful agent—virtual, wearable or robotic—must learn a compact, predictive model of both the physical and the mental worlds it inhabits.
Three kinds of bodies, one cognitive engine
The authors sort today’s prototypes into three buckets:
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Virtual agents (think emotionally intelligent avatars in games or therapy apps)
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Wearable agents that live in smart glasses and coach you through daily tasks
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Robotic agents capable of general-purpose manipulation and navigation
Despite wildly different form factors, all three need the same six ingredients: multimodal perception, a physical world model, a mental model of the user, action & control, short-/long-term memory, and a planner that ties them together.
What “world modeling” actually means
Meta’s framework breaks the catch-all term into concrete modules:
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Multimodal perception – image, video, audio and even touch encoders deliver a unified scene graph.
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Physical world model – predicts object dynamics and plans low- to high-level actions.
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Mental world model – tracks user goals, emotions and social context for better collaboration.
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Memory – fixed (weights), working and external stores that support life-long learning.
The paper contends that current generative LLMs waste compute by predicting every pixel or token. Instead, Meta is experimenting with transformer-based predictive models and JEPA-style latent learning to forecast just the state abstractions an agent needs to plan long-horizon tasks.
New benchmarks to keep them honest
To measure progress, the team proposes a suite of “world-model” stress tests—from Minimal Video Pairs for perceptual prediction to CausalVQA and the WorldPrediction benchmark that evaluates high-level procedural planning. Early results show humans near-perfect and SOTA multimodal models barely above chance, highlighting the gap Meta hopes to close.
Where they’re headed next
Two research directions top the agenda:
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Embodied learning loops that pair System A (learning by passive observation) with System B (learning by physical action), each bootstrapping the other.
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Multi-agent collaboration, where a family of specialized bodies—your glasses, a kitchen robot, and a home avatar—share a common world model and negotiate tasks.
Ethics is a running theme: privacy for always-on sensors and the risk of over-anthropomorphizing robots both get dedicated sections.
Why it matters
Meta isn’t open-sourcing code here; it’s setting the intellectual agenda. By declaring world models—not ever-larger GPTs—the “missing middle” of embodied AI, the company positions itself for a future where agents must act, not just talk. Expect the next iterations of Meta’s smart-glasses assistant (and perhaps its humanoid robot partners) to lean heavily on the blueprint sketched in this paper.
Paper link: arXiv 2506.22355 (PDF)
