[D-Machine]

Obviously not. In actual thinking, we can generate an idea, evaluate it for internal consistency and consistency with our (generally much more than linguistic, i.e. may include visual imagery and other sensory representations) world models, decide this idea is bad / good, and then explore similar / different ideas. I.e. we can backtrack and form a branching tree of ideas. LLMs cannot backtrack, do not have a world model (or, to the extent they do, this world model is solely based on token patterns), and cannot evaluate consistency beyond (linguistic) semantic similarity.

[astrange]

There's no such thing as a "world model". That is metaphor-driven development from GOFAI, where they'd just make up a concept and assume it existed because they made it up. LLMs are capable of approximating such a thing because they are capable of approximating anything if you train them to do it.

> or, to the extent they do, this world model is solely based on token patterns

Obviously not true because of RL environments.

[D-Machine]

> There's no such thing as a "world model"

There obviously is in humans. When you visually simulate things or e.g. simulate how food will taste in your mind as you add different seasonings, you are modeling (part of) the world. This is presumably done by having associations in our brain between all the different qualia sequences and other kinds of representations in our mind. I.e. we know we do some visuospatial reasoning tasks using sequences of (imagined) images. Imagery is one aspect of our world model(s).

We know LLMs can't be doing visuospatial reasoning using imagery, because they only work with text tokens. A VLM or other multimodal might be able to do so, but an LLM can't, and so an LLM can't have a visual world model. They might in special cases be able to construct a linguistic model that lets them do some computer vision tasks, but the model will itself still only be using tokenized words.

There are all sorts of other sensory modalities and things that humans use when thinking (i.e. actual logic and reasoning, which goes beyond mere semantics and might include things like logical or other forms of consistency, e.g. consistency with a relevant mental image), and the "world model" concept is supposed, in part, to point to these things that are more than just language and tokens.

> Obviously not true because of RL environments.

Right, AI generally can have much more complex world models than LLMs. An LLM can't even handle e.g. sensor data without significant architectural and training modification (https://news.ycombinator.com/item?id=46948266), at which point, it is no longer an LLM.

[astrange]

> When you visually simulate things or e.g. simulate how food will taste in your mind as you add different seasonings, you are modeling (part of) the world.

Modeling something as an action is not "having a world model". A model is a consistently existing thing, but humans don't construct consistently existing models because it'd be a waste of time. You don't need to know what's in your trash in order to take the trash bags out.

> We know LLMs can't be doing visuospatial reasoning using imagery, because they only work with text tokens.

All frontier LLMs are multimodal to some degree. ChatGPT thinking uses it the most.

[D-Machine]

> Modeling something as an action is not "having a world model".

It literally is, this is definitional. See e.g. how these terms are used in e.g. the V-JEPA-2 paper (https://arxiv.org/pdf/2506.09985). EDIT: Maybe you are unaware of what the term means and how it is used, it does not mean "a model of all of reality", i.e. we don't have a single world model, but many world models that are used in different contexts.

> A model is a consistently existing thing, but humans don't construct consistently existing models because it'd be a waste of time. You don't need to know what's in your trash in order to take the trash bags out.

Both sentences are obviously just completely wrong here. I need to know what is in my trash, and how much, to decide if I need to take it out, and how heavy it is may change how I take it out too. We construct models all the time, some temporary and forgotten, some which we hold within us for life.

> All frontier LLMs are multimodal to some degree. ChatGPT thinking uses it the most.

LLMs by definition are not multimodal. Frontier models are multimodal, but only in a very weak and limited sense, as I address in e.g. other comments (https://news.ycombinator.com/item?id=46939091, https://news.ycombinator.com/item?id=46940666). For the most part, none of the text outputs you get from a frontier model are informed by or using any of the embeddings or semantics learned from images and video (in part due to lack of data and cost of processing visual data), and only certain tasks will trigger e.g. the underlying VLMs. This is not like humans, where we use visual reasoning and visual world models constantly (unless you are a wordcel).

And most VLM architectures are multi-modal in a very limited or simplistic way still, with lots of separately pre-trained backbones (https://huggingface.co/blog/vlms-2025). Frontier models are nowhere near being even close to multimodal in the way that human thinking and reasoning is.

[dnautics]

"LLMs cannot backtrack". This is exactly wrong. LLMs always see everything in the past. In this sense they are more efficient than turing machines, because (assuming sufficiently large context length) every token sees ALL previous tokens. So, in principle, an LLM could write a bunch of exploratory shit, and then add a "tombstone" "token" that can selectively devalue things within a certain timeframe -- aka just de exploratory thngs (as judged by RoPE time), and thus "backtrack".

I put "token" in quotes because this would obviously not necessarily be an explicit token, but it would have to be learned group of tokens, for example. But who knows, if the thinking models have some weird pseudo-xml delimiters for thinking, it's not crazy to think that an LLM could shove this information in say the closer tag.

[D-Machine]

> "LLMs cannot backtrack". This is exactly wrong.

If it wasn't clear, I am talking about LLMs in use today, not ultimate capabilities. All commercial models are known (or believed) to be recursively applied transformers without e.g. backspace or "tombstone" tokens, like you are mentioning here.

But yes, absolutely LLMs might someday be able to backtrack, either literally during token generation if we allow e.g. backspace tokens (there was at least one paper that did this) or more broadly at the chain of thought level, with methods like you are mentioning.

[dnautics]

a tombstone "token "doesnt have to be an actual token, nor does it have to be explicitly carved out into the tokenizer. it can be learned. unless you have looked into the activations of a SOTA llm you cant categorically say that one (or 80% of one, fir example) doesn't exist.

[D-Machine]

We CAN categorically say that no such token or cluster of tokens exists, because we know how LLMs and tokenizers work.

Current LLM implementations cannot delete output text, i.e. they cannot remove text from their context window. The recursive application is such that outputs are always expanding what is in the window, so there is no backtracking like humans can do, i.e. "this text was bad, ignore it and remove from context". That's part of why we got crazy loops / spirals like we did with the "show me the seahorse emoji" prompts.

Backtracking needs more than just a special token or cluster of tokens, but also for the LLM behaviour to be modified when it sees that token or token cluster. This must be manually coded in, it cannot be learned.

[dnautics]

without claiming this is actually happening, it is certainly possible to synthetically create a token that ablates the values retrieved from queries in a certain relative time range (via transformations induced by e.g. RoPE encoding)