When I look up that quote, it leads back to Hacker News comments. It is also a strange way to make a citation. You make blanket statements that are easily argued against, and now you respond with this nonsense. I accuse you of being an LLM bot.
https://pmc.ncbi.nlm.nih.gov/articles/PMC4874898/
And btw, that’s not a blanket statement that’s an empirical statement that wipes away quite a bit of LLM relevance. I’d say it destroys the approach.
Do the research. And an apology is in good order.
This paper does little to dismiss LLMs. LLMs can use a different medium than text and that would not take away from its underlying mathematical models based on neuroscience. LLMs only understand language representations implicitly through statistical analysis, and that may instead show a commonality with how the human brain thinks as written in this paper.
I will not apologize for how you keep pushing an agenda despite how poorly supported it is. I have tried to be intellectually honest about the state of the industry and its flaws. I would implore you to instead do the research about LLMs so you can better refine your critique of them.
https://docs.google.com/document/d/1cXtU97SCjxaHCrf8UVeQGYaj...
There is a baseline expectation of how quotes and citations are supposed to work within Western intellectual circles. The fact that you do not know them and refuse to accept it means either you are not familiar with Western academia or you are an intellectually dishonest Internet troll or an LLM bot.
Spatial reasoning and world models are a research topic because elements of them were found in video and agentic models, and investors want to further refine either of them.
I do not have the time to read through this entire Google doc, but from what I have skimmed, I can see that the most substantial critiques are from academia being honest of the current state of AI and its limitations. That is fine.
However, the opening paragraphs aren't impressive. Language is arbitrary, yes, but they must also be intelligible by other humans. It is like a canvas to pattern match and create all sorts of inductive reasonings. There isn't much to explain how pattern matching math would be inherently incapable of pattern matching the written language. This reads like a basic understanding of postmodernist philosophy as if it is proof of math becoming a failure when applied to a socially constructed reality. However, philosophy and other social sciences do not surrender and give up as if their fields are fundamentally flawed. They make do and continue matching patterns to make observations of social reality.
The burden is ultimately on you to prove that the limitations of current AI/LLM cannot be overcome or that there is something that cannot make world models or spatial reasoning possible. Simply having a mountain of text to read is not an argument. There has to be some summary or point that can be used at the thrust of your position. As they say, brevity is the soul of wit.
For example, "LKNs Gaussian frontier" is another random phrase you have pulled out as if you are an LLM hallucinating something.
The bubble is in whether the investors will get their return on time. This is orthogonal to the underlying technology. Investor interests are not in progressing technology but to get a profit. Hope you enjoy the ride too because this is going to affect all of us.
Laughing all the way at the AI clown show.
Is "task demand" what the LLM would expect to do in order to respond to the user prompt? It seems incredulous that semantics would only exist here. As I have mentioned before, semantics is already embedded in the input and output for the LLM to implicitly discover and model and reason with.
https://arxiv.org/html/2507.05448v1 This paper is an interesting overview of semantics in LLMs. Here's an interesting quote, "Whether these LLMs exhibit semantic capabilities, is explored through the classical semantic theory which goes back to Frege and Russell. We show that the answer depends on how meaning is defined by Frege and Russell (and which interpretation one follows). If meaning is solely based on reference, that is, some referential capability, LLM-generated representations are meaningless, because the text-based LLMs representation do not directly refer to the world unless the reference is somehow indirectly induced. Also the notion of reference hinges on the notion of truth; ultimately it is the truth that determines the reference. If meaning however is associated with another kind of meaning such as Frege’s sense in addition to reference, it can be argued that LLM representations can carry that kind of semantics."
As for reference-based meaning reliant on truth, this was mentioned earlier in the paper, "An alternative to addressing the limitations of existing text-based models is the development of multimodal models, i.e., DL models that integrate various modalities such as text, vision, and potentially other modalities via sensory data. Large multimodal models (LMMs) could then ground their linguistic and semantic representations in non-textual representations like corresponding images or sensor data, akin to what has been termed sensorimotor grounding (Harnad, 1990). Note however, that such models would still not have direct access to the world but a mediated access: an access that is fundamentally task- driven and representational moreover. Also, as we will argue, the issue is rather that we need to ground sentences, rather than specific representations, because it is sentences that may refer to truth. But attaining the truth is not an easy task; ultimately, future LMMs will face the same difficulties as we do in determining truth."
In other words, this is the approach Fei-Fei Li and other multimodal models are using to create the world model.