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    What counts as an AI world model? A post argues prediction and planning aren’t the same

    The author says their blog maps 16 overlapping research traditions by what models represent and predict, how they connect to actions, and what they’re used for—not as mutually exclusive categories.

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    TLDR

    A post argues that generating a plausible future, predicting what an action will cause, and helping choose a better action are distinct achievements. Calling them all “world models,” the author says, shouldn’t blur what each has demonstrated. They also argue that agents should compare possible futures before committing, and that models should connect imagined outcomes to concrete actions while retaining the details that matter. For opening a drawer, they suggest, the latch matters more than the wood grain.

    Combined views

    24.6K

    4 Sources, first seen 19d ago

    Combined views

    24.6K

    4 Sources, first seen 19d ago

    172 likes
    19d ago
    first seen 19d ago
    172 likes
    7 comments
    178 saves
    14 reposts

    Sentiment

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    7 comments
    178 saves
    14 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

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    4 Sources

    @furonghHot take: world models will be a big part of the next leap in AI agents. The most useful ones might never show you a video. They might tell you that a code change will break billing. That a drawer is probably locked. Or that your plan depends on someone saying yes—and they haven’t. Three things I think matter: 1. Give me a multiverse. What happens if I do this? What if I try something else? What if I wait? An agent should compare those futures before committing. Generating a happy ending is easy compared with figuring out whether we can actually get there. And we choose our actions, not our luck. 2. Show me how to get there. A video of a finished meal doesn’t tell you how to cook it. The same problem shows up in world models. Imagining an outcome and predicting what my actions will cause are different capabilities. A robot needs to know where to grip, how to pull, and what to try when the drawer won’t budge. That connection to doing is where I think much of the hard work remains. 3. Keep the details that matter. For opening a drawer, the latch matters more than the wood grain. For arranging a delivery, who agreed to what may matter more than where the truck is. This is what interests me about neural–symbolic world models: learning from messy data while making objects, relationships, and actions explicit enough to reason about and check. Giving something a label doesn’t mean the model understands it. The label has to hold up when the agent acts. Now put several agents in the same world, each working for a different person. Say, let your agent negotiate with mine. Your agent wants a discount. Mine wants full price. Both are being helpful. Just not to the same person. Suddenly, “choose the future you like” gets awkward. Your agent can simulate me saying yes a thousand times. We still don’t have a deal. That’s the kind of world modeling I want to see: helping agents work out what could happen, what they can do about it, and what still requires someone else’s agreement. Full essay: World Models: A Multiverse We Can Act On https://furong-huang.com/blog/world-models-a-multiverse-we-can-act-on/ My take on where world models should go next, and a roadmap through the literature, from foundational ideas to recent work. 30-minute read. 100 references. Bring coffee. ☕️ #WorldModels #AgenticAI #NeuroSymbolicAI #MultiAgentSystems
    @thoma_guAmazing summary about what are world models!
    @CSProfKGDRT @abursuc: Nowadays “world models” is such loosely used. I’m super appreciative when people specify up-front what they mean by it @ALVARE…

    4 Sources

    @furonghHot take: world models will be a big part of the next leap in AI agents. The most useful ones might never show you a video. They might tell you that a code change will break billing. That a drawer is probably locked. Or that your plan depends on someone saying yes—and they haven’t. Three things I think matter: 1. Give me a multiverse. What happens if I do this? What if I try something else? What if I wait? An agent should compare those futures before committing. Generating a happy ending is easy compared with figuring out whether we can actually get there. And we choose our actions, not our luck. 2. Show me how to get there. A video of a finished meal doesn’t tell you how to cook it. The same problem shows up in world models. Imagining an outcome and predicting what my actions will cause are different capabilities. A robot needs to know where to grip, how to pull, and what to try when the drawer won’t budge. That connection to doing is where I think much of the hard work remains. 3. Keep the details that matter. For opening a drawer, the latch matters more than the wood grain. For arranging a delivery, who agreed to what may matter more than where the truck is. This is what interests me about neural–symbolic world models: learning from messy data while making objects, relationships, and actions explicit enough to reason about and check. Giving something a label doesn’t mean the model understands it. The label has to hold up when the agent acts. Now put several agents in the same world, each working for a different person. Say, let your agent negotiate with mine. Your agent wants a discount. Mine wants full price. Both are being helpful. Just not to the same person. Suddenly, “choose the future you like” gets awkward. Your agent can simulate me saying yes a thousand times. We still don’t have a deal. That’s the kind of world modeling I want to see: helping agents work out what could happen, what they can do about it, and what still requires someone else’s agreement. Full essay: World Models: A Multiverse We Can Act On https://furong-huang.com/blog/world-models-a-multiverse-we-can-act-on/ My take on where world models should go next, and a roadmap through the literature, from foundational ideas to recent work. 30-minute read. 100 references. Bring coffee. ☕️ #WorldModels #AgenticAI #NeuroSymbolicAI #MultiAgentSystems
    @thoma_guAmazing summary about what are world models!
    @CSProfKGDRT @abursuc: Nowadays “world models” is such loosely used. I’m super appreciative when people specify up-front what they mean by it @ALVARE…