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Qwen-AgentWorld: Language World Models for General Agents
- verdverm 4mo ago35B model from the qwen-3.5 line https://github.com/QwenLM/Qwen-AgentWorld https://github.com/QwenLM/Qwen-AgentWorld https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B
- stingraycharles 4mo ago[dead]
- Tepix 4mo agoThe labels of the very first chart (figure 1, bottom left) are obviously wrong which casts a doubt on the entire paper.
- dudisubekti 4mo agoThis 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?
- Tepix 4mo agoThe deltas are wrong. 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.
- deleted 4mo ago[deleted]
- yorwba 4mo agoAccording to Table 6, it's supposed to be 47.9 to 55.
- Tepix 4mo agoOK, then the labels (the numbers) are correct but the bars have the wrong length.
- dudisubekti 4mo agoAh I see. Yeah the graphics are probably AI-generated, and AIs do struggle with unit consistency in charts. (For another example, the charts in the August 2025 GPT-5 presentation)
- psc007 4mo agoEli5? What is this compared to a regular llm assistant model like the base qwen?
- gavmor 4mo agoA 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.
- dmos62 4mo agoSo, if I'm reading this correctly, whereas a regular LLM would, given a prompt to edit a file, infer a sed call, this "world" model infers the resulting contents of the file.
- kakugawa 4mo agoHere's the demo: https://docs.qwenlm.ai/resources/mlu56_demo.html https://docs.qwenlm.ai/resources/mlu56_demo.html 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.
- dippogriff 4mo agoI'm a fan of this direction. For me the most interesting use case for these world models isn't even training, it's verification. If this thing or some idealized version of it can actually reliably simulate state transitions, could you use it to verify an agent's execution path against hard constraints and replace/eclipse LLMs-as-a-judge?
- nostrebored 4mo agoWell 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.
- dippogriff 4mo ago[dead]
- claud_ia 4mo ago[flagged]
- blurbleblurble 4mo agoThis might be pretty big. One of my biggest frustrations with smaller models (especially MoE) is their failure to track workflow state at a high level. I'm constantly reminding them what we decided on or asking them to revisit, and reminding them eats context. 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.
- moozechen 4mo ago[dead]
- aliljet 4mo agoThe benchmarks here are confusing at best. Am I reading correctly that this model is essentially as good or better than all frontier models right now?
- blourvim 4mo agoBenchmarks in general are a little iffy, the whole industry is going off of vibes anyways. Can't decide before trying it out
- anana_ 4mo agoI 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
- ElenaDaibunny 4mo ago10M trajectories, probably more of a data scale win than a world model breakthrough tbh
- adrian_b 4mo agoThe smaller of the two models is open weights and available on Huggingface: https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B https://huggingface.co/Qwen/Qwen-AgentWorld-35B-A3B
- walrus01 4mo agoGive 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.
- khimaros 4mo agothey have arrived: https://huggingface.co/unsloth/Qwen-AgentWorld-35B-A3B-GGUF https://huggingface.co/unsloth/Qwen-AgentWorld-35B-A3B-GGUF
- npodbielski 4mo agoI 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'
- khimaros 4mo agothat particular quant is just corrupted. these work but seem to loop in reasoning a lot https://huggingface.co/groxaxo/Qwen-AgentWorld-35B-A3B-GGUF https://huggingface.co/groxaxo/Qwen-AgentWorld-35B-A3B-GGUF
- npodbielski 4mo agounsloth version works.
- Xx_crazy420_xX 4mo agoI think open-ended simulation for agents will be a key component for training and planning. Similar as human dreams simulate different scenarios in our head. Biggest challenge will be simulating more abstract and complex systems. 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 Anyways the project came to be really funny when you watched agent struggling in desperation to perform real world actions which would be impossible in real world. Main observation was that when presented agent with current action budget, it modulated the creativity and how desperate its actions were.
- avaer 4mo agoI 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.
- Xx_crazy420_xX 4mo agoPhysical simulations seem like next step, but how do you simulate dynamics in complex systems im not sure. Stock market is a good example with many trying to simulate that, but at the end you have to make some tradeoffs in terms of abstraction level you are simulating. For social backed simulations i guess some kind of grounding will be needed based on real examples, but then the out of distribution cases will need an other solution. As rate of changes in our civilization increases, the out of distribution cases will be more and more prominent.
- 4mo ago
- zkmon 4mo agoWhat if they did this using GLM 5.2? This looks like a new direction for AI.
- jkwang 4mo ago[dead]
- avaer 4mo agoNote this can run locally on a gaming card with quant. I got it running on a 4090 (24GB) 150 t/s with a Q4_K_M.
- trilogic 4mo agohttps://hugston.com/news/qwen-35b-agentworld-insights https://hugston.com/news/qwen-35b-agentworld-insights https://hugston.com/models/hugston-qwen-agentworldq4-k-m https://hugston.com/models/hugston-qwen-agentworldq4-k-m
- singularity2001 4mo agoI thought in this day and age "world model" also includes robo arm training data and robot arm benchmarks
- blenklo 4mo agoNever 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.
- pulkas 4mo agoI think the next movement is heading to multi model orchestration. https://developer.nvidia.com/blog/train-small-orchestration-agents-to-solve-big-problems/ https://developer.nvidia.com/blog/train-small-orchestration-...
- blurbleblurble 4mo agoYeah, this model actually seems like it could handle the job now with good prompting.
- androiddrew 4mo agoI understand what the model is doing. I am struggling to understand where this is going to fit in a workflow. 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. 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?
- roenxi 4mo ago> 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.
- anana_ 4mo agoIt 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.
- unjuno 4mo ago[flagged]
- jimmypk 4mo ago[flagged]
- tsunamifury 4mo agoThis questions the nature of banning SOTA models like fable deeply. 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?
- blurbleblurble 4mo agoThis is completely underrated news imo
- impartshadow 4mo ago[flagged]