I've had the feeling that labs aren't pelicanmaxxing specifically but that they do have some sort of RL environment for SVGs that they are letting the AIs overcook in. Specifically I'm thinking of the gemini 3.1 pro annoucnement that seemed to have a huge leap in animated SVG performance but not much else impressive about it.
So they aren't pelicanmaxxing but they are benchmaxxing in a way. The benefit of the pelican was originally that uplift on the pelican signaled an overall uplift on intelligence. I don't believe that is the case anymore and it is just another jagged edge of model intelligence.
My point is that if I made someone "smarter" they wouldn't suddenly know "What day, month, and year was Carrie Underwood’s album “CryPretty” certified Gold by the RIAA?" which is an example of a question in the SimpleQA benchmark.
So (in my opinion) knowledge benchmarks stagnating for small models is not evidence that small model agentic coding performance improvement will stagnate soon. Small models do not struggle with syntax, the barrier is not knowledge. The barrier is long context coherence and problem solving, which I don't see a bottleneck on improvements for small models in the near horizon as we get more and more high quality reasoning traces to train upon.
Knowledge benchmarks can't really be improved upon via distillation or RL. It requires those facts be added to the training corpus and for the model to memorize them better. Neither distillation or RL really do that and thus we shouldn't expect improvements on SimpleQA unless some other interventions are being made.
Model intelligence and knowledge aren't necessarily directly related. If we can pack greater intelligence and agency at the cost of it forgetting factoids, that would actually be a good thing. We don't need LLMs to memorize facts, we need them to learn how to interact with the world such that they can find the facts that are necessary and surface them to the user.
If we could distill all of the knowledge out of an LLM and just be left with a very agentic model that only knows facts in it's context, I think some very interesting stuff would happen.
My threshold for asking for help might be a little higher than the median, but, I like this operating style personally. Maybe it's just how I was raised, but the thought of not trying to figure something out for myself first is unthinkable. I fill like I get plenty of human connection at work and collaborate with peers and other disciplines plenty.
I'm not in tech, I'm in civil engineering so maybe it's just a difference in the types of problems we have in different industries.
I do find it very frustrating when an EIT asks me how to do something and it's clear that they haven't even read the instructions page to the excel sheet that they literally have open. I have time to mentor peers and subordinates but I want them to treat my time with the same respect that they treat their own.
The null hypothesis isn't just the opposite of whatever your opposition believes.
For LLMs the null hypothesis would be that there is no relationship between the input and output tokens. Something that is so obviously not true that it's not even worth calculating the number of sigmas away from the null hypothesis that LLMs are.
So clearly we discarded the null hypothesis sometime in 2017. Now we have a system that is really really good at pattern matching and seems to understand consequences. Is that "seeming" just a ruse or does it really understand stuff? A proper scientists would look at that evidence and put forward the hypothesis that maybe it really does understand stuff and begin working on experiments that would disprove that alternative hypothesis, moving forward with the assumption that the hypothesis is true until disproven or a better hypothesis is proposed that explains previous evidence more accurately. Naysayers saying "you haven't proven that pattern matching becomes understanding to my satisfaction" is not a rebuttal. They need an alternative hypothesis that can make predications that better fit the model and can be tested.
The only rebuttals I've heard are "AI can't actually understand stuff and therefore can't do X" which is a testable hypothesis at least. But Invariably AI eventually does X, just in a different way than anyone really expected.
Sending an AI response to a question that someone asks you is insulting because it's a bit like sending them a link to letmegooglethat where it just animates typing the question you have into google.
I think it's only appropriate when you are trying to insult the asker. Like if an employee asks a really dumb question that indicates that they didn't even bother googling the question or asking AI first, then sending them back an AI response is appropriate specifically because it's a bit insulting to do.
Personally, If I'm asking for help it's because I've surely exhausted other avenues of approach like googling it or asking chatGPT. I've come to the person because I need their input specifically. The people I work with are professional enough and I've developed such a relationship with them that I don't have the problem the OP is discussing very much.
If your AI alignment strategy is so fickle that it breaks if people simply discuss potential problems with the strategy then you didn't really have an alignment strategy to begin with.
It makes scams like that scalable. Once you discover one vector of scamming an AI bookkeeper, you can scam all of the users of that AI, using your own AI to scale it for you.
Well did they sell the website too? Or just the domain? Because the domain doesn't generate ad revenue, the original website did. Like just because I sell the domain name for my blog doesn't mean you also get the content of my blog too.
The downside of redding is that some portion of the world probably dies and you now have to live in that worse world that if you and 50% of the rest of the world has just blued, would not have happened.
Instagram follows is not a good way to hire football players but it's probably a good way to hire instagram influencers. The football analogy is a little unfair because VCs are investing in more than just a company's ability to "play football" they are investing in the brand, the marketing, and the vision. GitHub stars are at least an indication of a startup having a promising brand or some ability to market themselves.
Nevertheless, VCs are in fact pretty dumb sometimes and it'd be stupid to invest soley based on stars.
Agreed. I think the starting comparison actually works here. It's a bit like the automobile. The advice of "just don't" doesn't work for cars. It takes a deliberate effort on every scale of society to accomplish, it's not something an individual can just do and succeed at. An American can't just not have a car the same way someone from the netherlands might be able to.
So they aren't pelicanmaxxing but they are benchmaxxing in a way. The benefit of the pelican was originally that uplift on the pelican signaled an overall uplift on intelligence. I don't believe that is the case anymore and it is just another jagged edge of model intelligence.