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I agree; after running out of data on the internet, and humans being too slow to generate data, simulation is the only frontier left for improving things (training, datasets, reasoning). And it's probably the most ethical one too.

If nothing else I'm glad to see "world models" that are actually modeling some kind of worlds, instead of the term being applied as a hype layer for video/splats diffusion.

Is there much evidence we use dreams to pre-emptively simulate scenarios?

Dreaming seems much more likely to be neurological tidying and emotional reprocessing. Helpful for identifying and surfacing long term subconscious needs but not for planning.

My dreams would be precisely useless for making plans from, unless those plans were to involve being caught in public wrapped only in a towel. And even then, I'm not sure they'd be particularly helpful.

Out of curiosity would you be willing to share the full system prompt for the agent in question described in this test?

Yeah, this model actually seems like it could handle the job now with good prompting.

It looks like the purpose of this model is to i. generate environmental sim data for doing RL on other models or ii. act as a foundation model (they trained it to select actions as well as predicting the next state in the same loop?)

Either way, neither are intended for end consumers.

> I understand a big gap is that any LLM based ai agent isn't aware of the consequences of its actions because it barely understands the future state its actions will have, hence this model that can.

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.

Well if you can do this then you don't delegate execution path derivation to the agent. The benefit is a predictable coherent world state where you understand the impact of { current state } x { action } without having to enumerate that huge cartesian product.

Give it a day or two and the 'unsloth' people will probably publish a Q6 and Q8 (maybe Q8XL?) quantization in GGUF format for llama-server and other users.

I tried to run it but seems like it is either broken or it does not work on dockerized llama.cpp:

0.01.865.326 E llama_model_load: error loading model: missing tensor 'blk.40.attn_norm.weight'

A regular LLM acts as a "policy," mapping a current state to a specific action (states → actions). Their new LLM acts as a "world model," mapping a current state and a chosen action to a predicted future state ((states, actions) → subsequent states). Instead of deciding "what to do," its explicit objective is to predict the exact environment observation that will result from the interaction history and the agent's current action.

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.

Same thing, but qwen has decided to rebrand certain LLMs that were trained slightly differently as "world models". Despite the fact that "world model" typically means !LLM.

Never heard that.

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.

I believe the benchmark listed is about simulating the environment for the various tasks, rather than doing them. It seems that the point of this model is to generate sim data to improve other models with

Benchmarks in general are a little iffy, the whole industry is going off of vibes anyways. Can't decide before trying it out

This label?

> 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?

unsloth, activate!