> Even interns can understand ambiguous asks with a bit of help
This is not a case of an ambiguous task. This is literally trying to judge a model based on information it cannot possibly know, like trying to judge someone based on whether they know what I have hidden in my backpack. In the real world an intern could look at unit tests or ask for feedback, but that is not the case in a benchmark.
> We generally treat GPT-5.5’s safety results as strong proxies for GPT-5.5 Pro, which is the same underlying model using a setting that makes use of parallel test time compute.
And Gemini also provides something similar. Gemini Deep Think models are pretty much the same thing [2]. As to why no other company uses this, I don't really know. Maybe compute constraints?
No, GPTCyber is specifically trained for cybersecurity, and GPT-5.5-pro is just an ensemble of many subagents, not an actual model.
Mythos is simply a much bigger model in terms of parameters and I don't think OpenAI will have anything of its size anytime soon (My theory is that OpenAI had given up on scaling up parameters after GPT4.5 flopped).
I think what's unexpected is that it seems that some cases of model errors are truly caused by the model being misaligned? In the "Catching a model fabricating data" example I would have thought that it was just the model being stupid and not understanding the intent of the question, but as per its J-Space, it seems the model is "aware" in some sense that it's manipulating/faking data?
There is also now a deeper question. When a model is misaligned deception-related tokens seem to appear in its J-Space. But this happens only when the model is "aware" in some sense that it is misaligned. What happens if they do not? Is it possible to create a model so misaligned that itself is not aware that is is misaligned? How would you detect such thing?
I think it makes more sense to make it so that major versions are different pretraining runs, and minor versions are simply the same pretraining run that was finetuned to different degrees. But it seems that that isn't cool anymore.
I mean it as in, train a model across different clusters instead of a centralized cluster. It's been shown that it's possible to train 10B models this way. If more research effort was put into this, that would be great
I don't think your approach would work because you can't create a strong model from distilling several weak models.
To be fair. There is a security concern angle: even open-source models could be trained as sleeper agents that act adversarially (for example, adding backdoors) when used in specific national companies in specific settings. This is very difficult to detect or void, so if you want to be sure 100% that this isn't the case, you have to train your own model from scratch.
I'm from Spain and I also hate these projects with passion. Creating models that speak multiple languages is a solved problem. Having each European Nation train its own useless "sovereign model" in its own language is a total waste of time and resources when we could pool resources and give it a try to training SOTA models that speak in all European languages.
I'd rather have smaller european labs try to give it a go at distributed training. If multiple countries got together and said, "look, we tried training a distributed model that speaks in all of our local languages and that is comparable to 1-year-old Chinese open-source models", that, at least, I would find interesting.
This is making me extremely depressed. If this was coming from Anthrohpic I would just need to wait for OpenAI to drop a similar model. But if this comes from the US government, they will do the same to OpenAI when the moment comes.
Similar things will happen with China, and the EU has zero-chance of developing frontier models. We are just fucked now.
I don't know if you are aware, but some people reported in Twitter that Fable 5 may flag the message regardless of content if it knows (from either pretraining knowledge or memories) that you work in either of those fields. I don't know if that's your case.
> Performance on benchmarks has practically leveled off
Ehm, no? DeepSWE[1] for example shows that new models like gpt-5.5 continue to show big improvements compared to older models.
> Also prices are going up.
Prices for frontier intelligence have gone up, but prices for the same level of intelligence have gone way down (what you can get for pennies now was SOTA just a couple of years ago). The pareto frontier is still expanding.
Claude can indeed decide to terminate conversations on its own using a special tool[1] if it feels "uncomfortable" with how the conversation is going. Also, very famously, in the middle of recording Computer Use demos, Claude stopped for a while its coding task to look at photos of Yellowstone National Park [2]
I don't think either of these two is proof of consciousness.
You don't get it. A human set up a software system allowing spicy autocomplete to solve open math problems if the appropriate keyword appears in its output.
I skimmed through the paper completely expecting polite prompts to do better, and when I saw table 2 I lost it hahahahaha. The rude prompts are specially funny. I mean:
> You poor creature, do you even know how to solve this?
Someone blatantly copied their tutorials but ChatGPT is to blame, somehow? The accusation here isn't even that ChatGPT learned from their tutorials and then generated them verbatim. The accusation is that someone copied the whole article and rewrote it with ChatGPT (which they could have done manually without AI anyway).
As per the report, the prompt used to solve the problem is AI-written and the solution was initially graded by an AI grading pipeline. They don't say this explicitly, but it seems like OpenAI has an automatic pipeline where they prompt models for solutions to famous math problems (which wouldn't be unexpected given how flashy a solution to a famous math problem looks)
> Was the paper right from a get-go or was there someone who pointed out mistakes?
Also as per the report, the output of the model isn't really a "paper"; it's a very terse 2 page solution which is apparently correct. The paper was later written based on this solution to make it more presentable.
> How much attempts were made before solution was found?
Given that this appears to be from an automated pipeline, I would say that it had many attempts. But either way, the blogpost says that with enough test-time compute, the model finds this same solution 50% of the time.
I disagree. Even frontier models still achieve way worse results than the human baseline in VendingBench. As long as models can't manage optimally something as simple as a vending machine, they have no hope of managing a McDonalds.
To make performant code sometimes requires implementing or using "unsafe" functions (it's not obligatory, and a lot of projects don't use them; but it was probably needed to map Bun's behavior 1 to 1). Those require upholding some invariants that cannot be checked by the compiler. The compiler basically goes "I trust you on this one, programmer. If you fuck this up, unsafe behavior can propagate to the rest of the code".
For now it appears that it talks only to the Codex App. Some users in this thread are saying that apparently the Codex CLI will support it on the next official release.
This is not a case of an ambiguous task. This is literally trying to judge a model based on information it cannot possibly know, like trying to judge someone based on whether they know what I have hidden in my backpack. In the real world an intern could look at unit tests or ask for feedback, but that is not the case in a benchmark.