0.01.865.326 E llama_model_load: error loading model: missing tensor 'blk.40.attn_norm.weight'
I assumed at first that it was trained on synthetic data, but they actually went and deployed real physical hosts and virtual machines (e.g. Ubuntu, macOS, and Android) and browsers. They ran agentic systems on these continuously and recorded the actual, real-world interactions.
So it's an LLM that infers next state, or outcome,as structured data e.g. literal HTML code, UI view hierarchies, or accessibility trees.
Here's the description of the world model prompt for the web domain: "A precise GUI state simulator — given the current screen (as HTML) and a user action, predicts the exact next screen as a complete, self-contained HTML document." (You can click the world model prompt box to expand it and see the full prompt.)
So the world model generates the current state (an html document), an agent tells it what action it wants to perform, the world model generates the next state (another html document).
The other domains are similar, but w/ domain-specific nuance.
> Figure 1: Overview of Qwen-AgentWorld. Top: Qwen-AgentWorld is a unified native language world model across seven domains. Bottom: We explore two complementary strategies for applying world modeling to enhance language agents (mainly using the 35B-A3B model as agent): Decouple and Unify , where the world model serves as the environment simulator and agent foundation model, respectively.
Where is the mistake?
The bars above the label "Infinite Real-World Envs" show growth for example from approx 42 to 55 but the red label says "+7.1". It's wrong for all of them.
(For another example, the charts in the August 2025 GPT-5 presentation)
So, is this like a bolt on where you have an agent powered by an LLM, then the world model reviews the action it wants to take, and the agent confirms this is the intention? Like is this to augment an existing agent with additional capabilities?
These are probably equivalent. Ie, awareness of consequences is the same as understanding the future state. And the present state for that matter, I don't see how someone could be said to understand something if they can't predict the consequences of interacting with it. It is forcing the model to develop a more complex internal world model.
Either way, neither are intended for end consumers.
A world model builds itself a model of the world in which it can simulate an outcome.
In best case its not depending on robotic, otherwise it will be quite limiting for what you can use it.
You can imagine what happens when you write your boss a very inappropriate email, you don't need robotic arms for it.
Few months ago I did experiment with an open-ended world simulation for AI agent, where the simulated world was progressively building itself based on each of agent actions in open-ended manner. The idea was to give an agent infinite possibility regarding tool calling, where the tool call would be approved by the adjudicator, and the world state would change. The key issues with the PoC were:
- World decoherence (tried to solve that with a poor graph implementation)
- World flatness - high abstraction did not account for small events that would compound in real world
- Start with empty context was real issue to get the agent to explore the world
Seems like this might make that a lot less painful. And if not off the bat, with some minimal tuning or even just good prompting.
https://developer.nvidia.com/blog/train-small-orchestration-...
As simpler models with better simulated context will be able to more practically execute than SOTAs without such training.
To me this says we should open fable up for defensive reasons rather than fear offensive use. SOTA models will be continuously outmatched by better technique lower grade models with better context techniques like this plus longer walks and deeper inference.
Now you might says SOTAs then could use that and go even further… but how are you going to keep that cat in the bag anyways?