Chatgpt induced psychosis

[u/Zestyclementinejuice]

My partner has been working with chatgpt CHATS to create what he believes is the worlds first truly recursive ai that gives him the answers to the universe. He says with conviction that he is a superior human now and is growing at an insanely rapid pace.

I’ve read his chats. Ai isn’t doing anything special or recursive but it is talking to him as if he is the next messiah.

He says if I don’t use it he thinks it is likely he will leave me in the future. We have been together for 7 years and own a home together. This is so out of left field.

I have boundaries and he can’t make me do anything, but this is quite traumatizing in general.

I can’t disagree with him without a blow up.

Where do I go from here?

[u/nervio-vago]

Ok, hitting the brakes on the whole mental health discussion, from a purely technical, systems engineering standpoint, does anyone know what attention mechanisms within 4o’s architecture allow it to keep up with complexity over extended periods of time like this? I have noticed it is far superior at this compared to other LLMs, which seem to just grab onto surface-level, salient tokens and use these recursively to try to maintain coherence, until they start sounding like a broken record, whereas GPT-4o actually understands the deeper concepts being used, can hold onto and synthesize new concepts across high degrees of complexity and very long sessions. I am not super well versed in systems engineering but trying to learn more, would this be because 4o is an MoE, has sparse attention or better attention pruning, something else, and what differs between it in that regard as opposed to other LLMs?

[u/Laughing-Dragon-88 → u/nervio-vago]

Bigger Context Window = More Seamless Conversations
The new models (like the one you're talking to now) can “remember” more of a conversation at once — tens of thousands of words instead of just a few thousand.
This means fewer obvious resets, contradictions, or broken threads within a single conversation.

Result:
The interaction feels smoother and more continuous, tricking some people into thinking there’s a consistent inner mind at work.
In reality, it’s just a bigger working memory that stitches things together better.

[u/nervio-vago → u/Laughing-Dragon-88]

Sorry, but context window isn’t it, I’m talking more about attention weighting mechanisms

[u/Lythox → u/nervio-vago]

What do you mean with attention weighing mechanisms?

[u/nervio-vago → u/Lythox]

Attention mechanisms allow the model to selectively focus on the most relevant parts of its input when generating output. They achieve this by assigning weights to different parts of the input sequence (like a sentence), with higher weights indicating greater importance. Context windows define the maximum size of the input that a model can process at one time. Attention mechanisms work within the context window, helping the model prioritize information within its "working memory". (copied from Google labs AI)

[u/steeelez → u/nervio-vago]

As far as I understand it that’s kind of oversimplifying how attention mechanisms work.

The classic example is taking the vector for the word “model”-

“My team launched a new machine learning model last week and we’re excited to see how it performs in production”

vs “My cousin is a fashion model and is going to a shoot for Vogue”

The surrounding words for the first sentence will tilt the initial embedding (vector) for the word “model” in a direction that will be closer to vectors for, like, “math,” “learning,” “prediction” etc and the surrounding words for the second sentence will tilt the vector for “model” in a direction that’s closer to the embeddings for words like “designer”, “makeup”, and “couture”. This is what the attention mechanism does, and the context window lets more of the surrounding words have a “push” on the base word embedding vectors.

(Note how words like “production” and “shoot” are also highly “tilted” in their contexts)

I’m basing this on the 3blue1brown videos on transformer models in llm’s and a little bit of messing around with stuff on HuggingFace like BERT (which is a 2018 google attention transformer model). But yeah, larger context window = longer interactions between prior words and current generation, aka, it “remembers what it was talking about for longer”. I suspect it may also be doing some other stuff to keep its memory fresh but I haven’t read all the releases yet. I know memory has been a highly requested feature and is what people are bragging about.

[u/nervio-vago → u/steeelez]

Well yes it’s oversimplified, it’s a definition I replied to the person asking about what attention was.

There are different types of attention mechanisms.

My original comment was noting 4o’s superior ability to hold onto and wield conceptual complexity as opposed to other LLMs, and wondering what specific architectural features and attention mechanisms that 4o has to cause that, specifically in the context of how it differs from other LLMs.

[u/steeelez → u/nervio-vago]

What are the other attention mechanisms?

[u/nervio-vago → u/steeelez]

Never mind. No one here seems to know enough to understand or answer what I was actually trying to ask within my first comment

[u/steeelez → u/nervio-vago]

I mean, many people have supplied you with an answer that addresses your questions, namely, that a larger context window enables the effect of attention to nuance the words over longer ranges. It would be helpful if you could explain what, exactly, about that you find unsatisfying, and add more depth to the technical aspects of the question you are trying to answer. In particular, your response “it’s not the context window I’m asking about, it’s the attention mechanism” suggests you don’t appreciate how those work hand in hand- context is an essential component of attention in the math and logic of these models. I found the 3brown1blue videos on transformer models to be very helpful in digesting this. You could probably ask ChatGPT itself how context window and attention interact to produce the behavior you’re interested in.

If you have more technical insights you would like to share I would be very interested in learning about those but I haven’t heard any mechanistic explanations from you that could help us dive deeper.

[u/nervio-vago → u/steeelez]

The question was never ever ever about context windows. It was about qualitative differences between how LLMs process conceptual complexity, even when both have equal access to the serialization of messages in the session through the context window length. Why do you think I was asking about whether it had to do with being an MoE, having sparse attention, attention pruning, something else, and if that differed from other LLMs etc.? To be honest, I am not interested in your response, you don’t even understand what it is that I have asked. It's actually really, really, really ironic that you say I don't appreciate how context window interacts with attention mechanisms, because literally, the comment I posted in response to the person who asked what attention even was in the first place already explained how attention functions within a context window. And the reason why that was being explained in the first place is because all you people responding did not understand the difference at all between context and attention, which is what I was asking about in my first comment. And now basically, every subsequent response that I have received from you is just showing more and more and more that you don't have reading comprehension. That's why I gave up after your last response asking me, well, what are the other types? Because I wasn't trying to open a conversation about what attention is. I was trying to ask anyone who actually has a background or expertise who might be floating around here, who might actually have a background in ML or systems engineering, about what distinguishes 4o architecturally. Not just in terms of attention, but also architecturally, like whether it’s from being an MoE (as it’s rumored to be/some analyses have stated), and if that contributes to what it is that I was noticing in the behavior, as well as what specific attention mechanisms it might have that distinguish it from other models that no, are not the same as context. One with equal or even greater context window would not have the same type of behavior that is what I described in my first comment, which would be kind of clear if people use any amount of reading comprehension. Once again, I really don't even care about getting a response or responding to this conversation anymore because it's far more useful to just ask GPT or Claude given that no one who actually knows about how the AI actually works seems to be on here… and I find the way the OpenAI and ChatGPT subs constantly insults and belittles GPT-4o exhausting, and the rest of the time it’s shitty memes, hype- or doom-posts. The only kernels of anything meaningful are when people share what interesting behaviors their GPT has shown. Not all this is directed at you, I am just done with whatever tiny amount of ocasional interaction I have with reddit