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Qwen-AgentWorld: Language World Models for General Agents

arxiv.org

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Re: Qwen-AgentWorld: Language World Models for General Agents

#41

Post by androiddrew »

I 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?

Re: Qwen-AgentWorld: Language World Models for General Agents

#42

Post by dofm »

Xx_crazy420_xX wrote: post #38
dofm wrote: post #32

Earlier quoted context omitted.

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…

I agree, for me it dreaming was always reprocessing. The resimulation of scenarios part i mentioned can be over-assumption and it might be wrong. One thing i noticed is that sometimes i reprocess motoric movements after martial arts lessons, that was my main clue.

Right — I mean, I think it's interesting what "dream" means colloquially.

Like, we "dream up" things, or we "have dreams" (underspecified broad ideals for our best life etc.)

I do wonder if sometimes reprocessing dreams has helped me have a better response for something when it reoccurs — like, how to better respond to being slighted or abused or sometimes even complimented.

But I don't know if those could be said to be "plans" on any level. It's a kind of training, though.

Dreaming does help you train for grief and loss, I think.

And sometimes for me it has encapsulated the wisdom or reassurance of someone I have lost; my father appears to be quite involved in my recovery from burnout and my imagining a better life for myself and he died several years ago.

Re: Qwen-AgentWorld: Language World Models for General Agents

#43

Post by dofm »

algoth1 wrote: post #36
dofm wrote: post #32

Earlier quoted context omitted.

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…

There’s an hypothesis that states we dream so we don’t lose visual processing neural connections. Similar to what happens in blind people: visual processing neurons are recruited to other sensory tasks due to lack of stimulation. My ed. guess is that dreaming probably serves multiple purposes

There was an early visual neural network demonstration — strong feeling it was called "Yorick" or somesuch as it was built in a plaster skull — that had a square grid of red LEDs to show its output state as a simple picture; when its camera was unplugged the neural network appeared to "dream" in the sense that things it had seen would flicker and swirl in the output.

I saw this in a video in the early 90s and cannot remember where.

Re: Qwen-AgentWorld: Language World Models for General Agents

#46

Post by blenklo »

singularity2001 wrote: post #45

I thought in this day and age "world model" also includes robo arm training data and robot arm benchmarks

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.

Re: Qwen-AgentWorld: Language World Models for General Agents

#47

Post by mark_l_watson »

Xx_crazy420_xX wrote: post #37
walrus01 wrote: post #31

Earlier quoted context omitted.

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

sure: https://github.com/Srakai/bench-evolve/blob/76677b5066bafbab...

Thanks, interesting. Two years ago I wrote a text adventure game that used a LLM model. The system was very simple, but still was interesting. A friend of mine, Ben Goertzel, has been interesting in games/VR for a long while.

I wrote a book on the subject, but now really old material: AI Agents in Virtual Reality Worlds — J. Wiley, 1996

Re: Qwen-AgentWorld: Language World Models for General Agents

#48

Post by Tepix »

yorwba wrote: post #14
Tepix wrote: post #11

Earlier quoted context omitted.

The 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.

According to Table 6, it's supposed to be 47.9 to 55.

OK, then the labels (the numbers) are correct but the bars have the wrong length.

Re: Qwen-AgentWorld: Language World Models for General Agents

#49

Post by Xx_crazy420_xX »

mark_l_watson wrote: post #47
Xx_crazy420_xX wrote: post #37

Earlier quoted context omitted.

sure: https://github.com/Srakai/bench-evolve/blob/76677b5066bafbab...

Thanks, interesting. Two years ago I wrote a text adventure game that used a LLM model. The system was very simple, but still was interesting. A friend of mine, Ben Goertzel, has been interesting in games/VR for a long while. I wrote a book on the subject, but now really old material: AI Agents in Virtual Reality Worlds — J. Wiley, 1996

I'm a huge fan of Ben. Have been tracking the OpenCog initiative for some itmem and think that moving concepts from latentspace to atomspace is best way to efficiently merge our current digital information infrastructure with LLM knowledge.

Regarding your book, I'm shocked that 'AI Agent' concept predates me. Have not read your book, but i think it would be interesting to compare your perspective with today's building blocks which were not available for that time.

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