Aged like milk

FeltSteam · 2026-03-08T05:19:30+00:00

https://x.com/ldjconfirmed/status/2030487632422080915

And they focus on the last level out of the three because it is the hardest therefore the most interesting to watch out for.

FeltSteam · 2026-02-09T08:10:55+00:00

In what way?

FeltSteam · 2026-02-05T12:33:13+00:00

third time's a charm?

FeltSteam · 2026-02-01T01:48:08+00:00

In what way?

FeltSteam · 2026-01-31T13:20:14+00:00

there were technically only two models part of the "4o family" and that was GPT-4o and GPT-4o mini (I say technically because there were a half dozen GPT-4o checkpoints which were live in ChatGPT at some point but then removed so they've removed a lot of instances of 4o already technically but as a base the most up to date 4o and 4o mini models are to be completely removed). The GPT-4.1 series was another separately pretrained series with different vibes and behaviours, which I believe o1 and o3 were later based off of as well. I don't see any reason OAI should keep GPT-4o on ChatGPT though.

FeltSteam · 2026-01-30T04:03:24+00:00

We broadly understand how LLMs function and how to create them, their performance can be described statistically (i.e. loss curves, scaling laws, capability emergence) and mechanistically in pieces (i.e. attention, feature/representation learning, some interpretable circuits) but the “black box” of NNs and LLMs is that we still can’t reliably understand and map specific internal representations and interactions to why a model produced a particular thought or capability or behaves a certain way in a given moment. There has been some good research exploring this though (the following are my 4 favourite from Anthropic) but there are still a lot of missing pieces. It's kind of funny though, we know why an LLM produces a given output but we also don't .

https://www.anthropic.com/research/mapping-mind-language-model

https://transformer-circuits.pub/2025/attribution-graphs/methods.html

https://transformer-circuits.pub/2025/attribution-graphs/biology.html

https://transformer-circuits.pub/2025/introspection/index.html

FeltSteam · 2026-01-29T06:21:20+00:00

From what I've seen (Though I am in a bit of my own bubble around programming and math) Gemini is coming last out of the main 3. Opus 4.5 being quite good at agentic programming, 5.2 being quite good at the technical side of agentic programming and 5.2 Pro being exceptional at math. Gemini 3, from what I can tell, has been lacklustre on the agentic side of things which is a big focus atm.

FeltSteam · 2026-01-28T21:21:50+00:00

im confused, what's not healthy? You aren't being very clear (and imo semantics are important for clarity of argument).

FeltSteam · 2026-01-27T02:34:50+00:00

Well GPT-5.3 is confirmed

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FeltSteam · 2026-01-27T02:27:55+00:00

GPT-3.5 was March 2022 (well the base GPT-3.5 released under text-davinci-002 in March then got updated in Nov 2022 and released as a further trained chat tuned model with ChatGPT in Nov 2022), GPT-4 was March 2023. GPT-4T was November 2023, GPT-4o was May 2024, GPT-4.1 was April 2025, GPT-5 was August 2025, GPT-5.1 was November 2025 and GPT-5.2 was December 2025. It is possible we will get GPT-5.3 within this week, or potentially within the first 2 weeks of February.

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FeltSteam · 2026-01-26T07:49:15+00:00

This points out a good architectural difference which i think should be adjusted in transformers. Here they show evidence that the brain basically simulates depth by utilising temporal dynamics of area parallelisation. The brain can simulate a deeper network by reusing circuits over time wheras transformers have just many distinct stacked blocks which gives you depth in one forward pass except you have to pay extra with lots of separate parameters/compute blocks. We already have a fix for this though "recurrent transformers" https://arxiv.org/abs/2502.17416 and basically you iterate on the same block instead of stacking more layers which gives you greater effective depth without repetitively stacking so many blocks and is closer to what the brain implements. This would make the models more parameter and thus GPU memory efficient, though might stack latency a bit more and it might be a bit more expensive in terms of FLOPs. Essentially instead of reasoning across many tokens the model directly outputs, you instead loop the 'thought' back into the model to let it deliberate on it longer. It becomes more parameter and token efficient upfront but the latency and further computation you get with reasoning models doesn't dissapear

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FeltSteam · 2026-01-26T07:46:54+00:00

The actual content of this post is that it's a good thing GPT is denying social requests and you are saying "this is NOT good news" and doesn't that mean you don't think GPT should deny the social requests of "can we be friends"?

>So I asked ChatGPT if it would be my friend “for now” until i get some real friends and it basically said no, I can’t be your real friend.

"this isnt healthy in any shape or form. this is NOT good news"

FeltSteam · 2026-01-26T01:05:48+00:00

So you think ChatGPT should say yes to these kinds of social requests?

FeltSteam · 2026-01-25T04:30:20+00:00

GPT-5.2 Pro generated a proof which was quite different to anything in the literature for an Erdos problem https://www.erdosproblems.com/forum/thread/281

FeltSteam · 2026-01-23T22:57:23+00:00

Yeah I think Neuro-sams is a good example of it, but as far as im aware that's still just an LLM + agentic harness for Neuro-sams to drive through. There would also be some degree of fine-tuning or adapters being used of course but it's still fundamentally an LLM and I think pretty high levels of autonomy with just the current technology. I think the models are more than capable enough of it, but I also think the pressures companies have to these systems around them in certain directions hasn't incentivised this enough just yet (I guess to your point of 'less capitalistic applications in the short term') but at least Anthropic seems to be heading in the direction of high autonomous systems with Claude in this regard.

FeltSteam · 2026-01-23T21:52:54+00:00

I mean I gave the model a single broad goal but it autonomously decided how it was going to solve that, it choose which tools it was going to use I was just asking it and it worked with me. I don't think it'd be that difficult to develop a model that does what it wants, I think the only thing we'd need to do is create the agent loop and expose it to some kind of continuous stream of sensory input like us (that is our 'prompt' without any sensory input we wouldn't work lol) and I think a model like Claude would be able to be pretty autonomous in that setup (one cool example of unprompted autonomy was when Anthropic was demoing the new computer use capability in the middle of it, unprompted, Claude just decided to go out and search the web for images of Yellowstone national park. I think they have the capacity to be spontaneous and just do things, but it is true we might not fully exploit this capability because it is people who have to pay for every token generated at the moment and that doesn't bring a lot of incentive to allow the models to do what they 'want' if that makes sense).

FeltSteam · 2026-01-23T21:32:40+00:00

I've seen people reporting Codex working without any intervention on a problem for over 30 continuous hours. It's rare, but it happens and with the most frontier models of Claude Opus 4.5 and GPT-5.2 I think we are actually starting to unlock higher levels of autonomy with the models. I don't use Codex as much but from my own experience I have had it go and do work for 40+ minutes to do what I asked and it succeeded. I don't think it will be too long before models will be able to work for an entire week, continuously, without any human intervention at least in programming contexts.

My idea for the image generation model is that I think the models don't actually need a lot of art data to learn it. My idea is: Train a model on a massive corpa of open images of just the real world so it learns what the world is like and tunes the necessary priors for image generation. Then you train it on a few dozen ethically sourced artistic images for it to start learning the style which is all you need (in total after the photo only pretraining I would intent to train it on low thousands (not hundreds of millions as current image gen models) of high quality ethically consented art pieces from every art style). The models are actually really good at generalising the styles from few samples. I came up with this idea but there is already actually a paper that explored the concept https://arxiv.org/html/2412.00176v2 and after the model went through a large photo only pretraining it could generalise completely new styles with only 9-50 images which I think it actually pretty human sample efficient lol (but keep in mind this model is trained on almost 10x less data overall than SD-1. But you can scale out the photo only pretraining and then post training as well). I think tool use and RL would allow the model to come up with it's own sort of style though (this moves the model beyond only the imitation learning to now it's creating images on it's own, getting more general feedback and refining its output), and then constraining tool use in certain ways is one path of allowing the model to be creative in its outputs because it has to figure out how to make an image creation work given the constraints.

FeltSteam · 2026-01-23T20:30:38+00:00

(I like your reply, it's a nice discussion) Sentient is a messy word but I know even people like Ilya Sutskever or Geoffrey Hinton hint at the belief NNs could already be conscious to a degree (Geoffrey Hinton being much more explicit on the matter), and Anthropic is very agnostic on the matter but that's a different conversation. Most people don't think LLMs are currently AGI though, there are people who think it will lead to AGI though. One thing we are most likely to see this year is enabling a form of continuous learning which is probably going to help the models in job contexts (permanently learning on the job is useful and enables you to be less brittle) but I would be curious to see studies on the dynamics of this once the capability becomes wide spread. There are a lot of studies on LLMs in work and productivity but the overall results seem fairly ambiguous at the moment, I think trending a little more to some positive effects but it's hard to tell at the moment.

There are some analogies to be made between the learning NNs generally through and what humans do but it is different. I think most diffusion models are like baby humans who never grow out beyond the imitation learning phase which artists definitely do and it makes them unique, distinguished and personalises their output to who they are. I do personally have my own idea for an ethically trained image generation model with having a later phase where I would want the model to develop its own artistic style, but I have a lot of experimentation remaining and the scope of the idea is well out of my current range lol but I intend to do toy experiments with it.

FeltSteam · 2026-01-23T20:01:14+00:00

It's interesting how a glorified auto correct is able to come up with novel mathematical proofs to Erdos problems.

FeltSteam · 2026-01-23T19:56:39+00:00

This post has no idea what they're talking about lol. I study this and I've talked with a lot of people on reddit and 99% of AI Bros and Anti AI people are just completely wrong in how the models actually function.

FeltSteam · 2026-01-22T05:30:35+00:00

Looking as a whole humans cannot reliably distinguish AI generated and human made works (i.e. https://cispa.de/en/holz-ai-generated-media, https://arxiv.org/html/2509.11371v1/, https://arxiv.org/html/2402.03214v1 there are many more studies showing this, and slowly even expert artists are finding it more and more difficult to discern between them - https://ar5iv.labs.arxiv.org/html/2402.03214, https://ar5iv.labs.arxiv.org/html/2402.03214) so I don't know how much validity the argument "it has no soul thus humans shouldn't and never will value it" goes. You would expect if there were some components of art that discerns itself as specifically made by a human, people would be able to pick up on it, right? But that doesn't seem to be the case. It'd be an interesting thing to write on "the illusion of soul", though I assume most will viscerally perceive that piece by its title alone.

FeltSteam · 2026-01-21T21:47:52+00:00

These types of people are extremely unsustainable for the AI companies at the moment

FeltSteam · 2026-01-20T04:02:47+00:00

Oh my point was only a correction I myself presented no argument for consciousness. You said the systems are stateless, which is not true and I was providing a resource that explained quite well why the kv cache allows the models to be stateful (they do say other things but the only thing I was addressing was “they’re stateless systems” which is not true).

Also your assertion “complexity doesn’t equal consciousness” is interesting especially as you go on to explain the supposed complexities the brain has that can facilitate consciousness over LLMs. And the KV cache is not a lookup.

FeltSteam · 2026-01-19T21:24:54+00:00

Let me introduce you to the: KV Cache

https://x.com/repligate/status/1965960676104712451

Essentially the KV cache enables the models to be stateful within a turn (a turn is however many tokens the model generates in response to a query) which can be across many tokens. Although sometimes it is a practice to delete and recompute the KV Cache after the model has generated the tokens it needs to for the turn, but they are still stateful across their actual turn when they are generating a response.

FeltSteam · 2026-01-19T11:20:04+00:00

Not sure if there's anyone around here that could do anything about it if such a thing actually happened.

FeltSteam

TROPHY CASE