Before last year we didn't have reasoning. It came with QuietSTaR, then we got it in the form of O1 and then it became practical with DeepSeek's paper in January.
So we're only about a year since the last big breakthrough.
I think we got a second big breakthrough with Google's results on the IMO problems.
For this reason I think we're very far from hitting a wall. Maybe 'LLM parameter scaling is hitting a wall'. That might be true.
IMO is not breakthrough, if you craft proper prompts you can excel imo with 2.5 Pro. Paper : https://arxiv.org/abs/2507.15855. Google just put whole computational power with very high quality data. It was test-time scaling. Why it didn't solve problem 6 as well?
Yes, it was breakthrough but saturated quickly. Wait for next breakthrough. If they can build adapting weights in llm we can talk different things but test time scaling coming to end with increasing hallucination rate. No sign for AGI.
It wasn't long ago that test-time scaling wasn't possible. Test-time scaling is a core part of what makes this a breakthrough.
I don't believe your assessment though. IMO is hard, and Google have said that they use search and some way of combining different reasoning traces, so while I haven't read that paper yet, and of course, it may support your view, but I just don't believe it.
We are not close to solving IMO with publicly known methods.
test time scaling is based on methods from pre-2020. If you look details of modern LLMs its pretty small prob to encounter method from 2020+(ROPE,GRPO). I am not saying IMO is not impressive, but it is not breakthrough, if they said they used different paradigm then test-time scaling I would say breakthrough.
> We are not close to solving IMO with publicly known methods.
The point here is not method rather computation power. You can solve any verifiable task with high computation, absolutely there must be tweaks in methods but I don't think it is something very big and different. Just OAI asserted they solved with breakthrough.
Wait for self-adapting LLMs. We will see at most in 2 years, now all big tech are focusing on that I think.
Layman's perspective: we had hints of reasoning from the initial release of ChatGPT when people figured out you could prompt "think step by step" to drastically increase problem solving performance. Then yeah a year+ later it was cleverly incorporated into model training.
We still don't have reasoning. We have synthetic text extrusion machines priming themselves to output text that looks a certain way by first generating some extra text that gets piped back into their own input for a second round.
It's sometimes useful, it seems. But when and why it helps is unclear and understudied, and the text produced in the "reasoning trace" doesn't necessarily correspond to or predict the text produced in the main response (which, of course, actual reasoning would).
Boosters will often retreat to "I don't care if the thing actually thinks", but the whole industry is trading on anthropomorphic notions like "intelligence", "reasoning", "thinking", "expertise", even "hallucination", etc., in order to drive the engine of the hype train.
The massive amounts of capital wouldn't be here without all that.
i think this is more an effect of releasing a model every other month with gradual improvements. if there was no o-series/other thinking models on the market - people would be shocked by this upgrade. the only way to keep up with the market is to release improvements asap
I don't agree, the only thing thing that would shock me about this model is if it didn't hallucinate.
I think the actual effect of releasing more models every month has been to confuse people that progress is actually happening. Despite claims of exponentially improved performance and the ability to replace PhDs, doctors, and lawyers, it still routinely can't be trusted the same as the original ChatGPT, despite years of effort.
this is a very odd perspective. as someone who uses LLMs for coding/PRs - every time a new model released my personal experience was that it was a very solid improvement on the previous generation and not just meant to "confuse". the jump from raw GPT-4 2 years ago to o3 full is so unbelievable if you traveled back in time and showed me i wouldn't have thought such technology would exist for 5+ years.
to the point on hallucination - that's just the nature of LLMs (and humans to some extent). without new architectures or fact checking world models in place i don't think that problem will be solved anytime soon. but it seems gpt-5 main selling point is they somehow reduced the hallucination rate by a lot + search helps with grounding.
I notice you don't bring any examples despite claiming the improvements are frequent and solid. It's likely because the improvements are actually hard to define and quantify. Which is why throughout this period of LLM development, there has been such an emphasis on synthetic benchmarks (which tell us nothing), rather than actual capabilities and real world results.
i didnt bring examples because i said personal experience. heres my "evidence" - gpt 4 took multiple shots and iterations and couldnt stay coherent with a prompt longer than 20k tokens (in my experience). then when o4 came out it improved on that (in my experience). o1 took 1-2 shots with less iterations (in my experience). o3 zero shots most of the tasks i throw at it and stays coherent with very long prompts (in my experience).
heres something else to think about. try and tell everybody to go back to using gpt-4. then try and tell people to go back to using o1-full. you likely wont find any takers. its almost like the newer models are improved and generally more useful
I'm not saying they're not delivering better incremental results for people for specific tasks, I'm saying they're not improving as a technology in the way big tech is selling.
The technology itself is not really improving because all of the showstopping downsides from day one are still there: Hallucinations. Limited context window. Expensive to operate and train. Inability to recall simple information, inability to stay on task, support its output, or do long term planning. They don't self-improve or learn from their mistakes. They are credulous to a fault. There's been little progress on putting guardrails on them.
Little progress especially on the ethical questions that surround them, which seem to have gone out the window with all the dollar signs floating around. They've put waaaay more effort into the commoditization front. 0 concern for the impact of releasing these products to the world, 100% concern about how to make the most money off of them. These LLMs are becoming more than the model, they're now a full "service" with all the bullshit that entails like subscriptions, plans, limits, throttling, etc. The enshittification is firmly afoot.
not to offend - but it sounds like your response/worries are based more on an emotional reaction. and rightly so, this is by all means a very scary and uncertain time. and undeniably these companies have not taken into account the impact their products will cause and the safety surrounding that.
however, a lot of your claims are false - progress is being made in nearly all the areas you mentioned
"You can use these filters to adjust what's appropriate for your use case. For example, if you're building video game dialogue, you may deem it acceptable to allow more content that's rated as Dangerous due to the nature of the game. In addition to the adjustable safety filters, the Gemini API has built-in protections against core harms, such as content that endangers child safety. These types of harm are always blocked and cannot be adjusted."
now id like to ask you for evidence that none of these aspects have been improved - since you claim my examples are vague but make statements like
> Inability to recall simple information
> inability to stay on task
> (doesn't) support its output
> (no) long term planning
ive experienced the exact opposite. not 100% of the time but compared to GPT-4 all of these areas have been massively improved. sorry i cant provide every single chat log ive ever had with these models to satisfy your vagueness-o-meter or provide benchmarks which i assume you will brush aside.
as well as the examples ive provided above - you seem to be making claims out of thin air and then claim others are not providing examples up to your standard.
Big claims of prs and shipped code then links to people who are financially interested in hype claims.
Not saying things are not getting better but i have found that those that claim amazing results are from people who are not expert enough in the output of the given domain to comment on the actual quality of output.
I love vibing out rust and it compiles and runs but i have no idea if it is good rust because well, i barely understand rust.
> now id like to ask you for evidence that none of these aspects have been improved
You're arguing against a strawman. I'm not saying there haven't been incremental improvements for the benchmarks they're targeting. I've said that several times now. I'm sure you're seeing improvements in the tasks you're doing.
But for me to say that there is more a shell game going on, I will have to see tools that do not hallucinate. A (claimed, who knows if that's right, they can't even get the physics questions or the charts right) reduction of 65% is helpful but doesn't make these things useful tools in the way they're claiming they are.
> sorry i cant provide every single chat log ive ever had with these models to satisfy your vagueness-o-meter
I'm not asking for all of them, you didn't even share one!
Like I said, despite all the advances touted in the breathless press releases you're touting, the brand new model is just a bad roll away from like the models from 3 years ago, and until that isn't the case, I'll continue to believe that the technology has hit a wall.
If it can't do this after how many years, then how is it supposed to be the smartest person I know in my pocket? How am I supposed to trust it, and build a foundation on it?
Interesting thread. I think the key around hallucinations is analogous to compilers. In order for output to be implicitly trusted it has to be as stable as a compiler. Hallucinations mean i cannot yolo trust the output. Having to manually scan the code for issues defeats the fundamental benefit.
Compilers were not and are not always perfect but i think ai has a long way to go before it passes that threshold. People act like it will in the next few years which the current trajectory strongly suggests that is not the case.
ill leave it at this: if “zero-hallucination omniscience” is your bar, you’ll stay disappointed - and that’s on your expectations, not the tech. personally i’ve been coding/researching faster and with fewer retries every time a new model drops - so my opinion is based on experience. you’re free to sit out the upgrade cycle
you dont remember deepseek introducing reasoning and blowing benchmarks led by private american companies out of the water? with an api that was way cheaper? and then offered the model free in a chat based system online? and you were a big fan?
Isn't the fact that it produced similar performance about 70x more cheaply a breakthrough? In the same way that the Hall-Héroult process was a breakthrough. Not like we didn't have aluminum before 1886.
I think the llm wall was hit a while ago and the jumps have been around finessing llms in novel ways for a better result. But the core is still very much the same it has been for a while.
The crypto level hype claims are all bs and we all knew that but i do use an llm more than google now which is the there there so to speak.
This does feel like a flatlining of hype tho which is great because idk if i could take the ai hype train for much longer.
It's seemed that way for the last year. The only real improvements have been in the chat apps themselves (internet access, function calling). Until AI gets past the pre-training problem, it'll stagnate.
It is easier to get from 0% accurate to 99% accurate, than it is to get from 99% accurate to 99.9% accurate.
This is like the classic 9s problem in SRE. Each nine is exponentially more difficult.
How easy do we really think it will be for an LLM to get 100% accurate at physics, when we don't even know what 100% right is, and it's theoretically possible it's not even physically possible?
GPT5 doesn't add any cues to whether we hit the wall, as OpenAI only needs to go one step beyond the competition. They are market leaders and more profitable than the others, so it's possible are not showing us everything they have, until they really need to.
Not really, it's just that our benchmarks are not good at showing how they've improved. Those that regularly try out LLMs can attest to major improvements in reliability over the past year.
