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When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation

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Re: When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation

#41

Post by slopinthebag »

warkdarrior wrote: post #29
tsunamifury wrote: post #20

Earlier quoted context omitted.

I'm sorry, we know exactly how LLMs work, this myth that we "dont know how they work" was perpetuated by executives that dont know how they work. We know exactly how attention layers work and how they produce the next word as well as draw them from larger feature spaces.

If you know all this, can you explain how these models produce advanced mathematical proofs? (as recently done by OpenAI, for example) I tried to generate the next word to the best of my ability, starting with a mathematical problem, but I did not create a valid proof. How do these LLMs work when they create math proofs to problems not yet solved?

How did you generate the next word? Did you first read pretty much every written work ever published, including blog posts, forum posts, books, etc? Learn how to imagine everything as a point in a gigantic abstract space where similar meanings cluster together? How did you manage training with gradient descent? And then did you do a lifetime of matrix multiplication for each token you predicted?

Re: When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation

#42

Post by freejazz »

logicchains wrote: post #13
freejazz wrote: post #9

Earlier quoted context omitted.

Can't call it AI like that without discrediting yourself. You mean LLMs?

Talk about moving the goalposts. Pray tell, exactly what must an LLM do before you're willing to consider it AI? Be specific, otherwise you're just woo-mongering.

Everyone here is talking about LLMs, why bother calling them something else

Re: When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation

#43

Post by freejazz »

scotty79 wrote: post #26
freejazz wrote: post #9

Earlier quoted context omitted.

Can't call it AI like that without discrediting yourself. You mean LLMs?

When a Big Killing Robot comes to murder you be sure to always call it BKR and don't discredit yourself by calling it AI.

That's a bit hyperbolic when we're all just posting on HN

Re: When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation

#44

Post by slopinthebag »

otterdude wrote: post #2

This seems to be the end of the road for LLM's. There's only so much accuracy on a highly non-linear space you can get from a regression. If the Pareto rule is any indicator, 80% of results come from 20% of causes. It seems that we have alot more to learn about intelligence. I am reminded of a statement on truth from an ancient philosopher, this sentiment seems to be exactly the opposite of the LLM training paradigm…

Idk about end of the road, I’m sure they can squeeze out some more performance by curating even more data and doing even more RL.

But I would bet that pretty much all of the improvement we’ve seen over the last year with coding has come from RL, not from the models becoming particularly stronger. And this makes sense, if models grow sublinearly with compute. And it seems like they do.

Re: When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation

#45

Post by soiax »

tsunamifury wrote: post #20
adrianN wrote: post #8

Earlier quoted context omitted.

I really don’t think we know enough about what „intelligence“ is or how LLMs actually work to confidently say that this is the end of the road for LLM.

I'm sorry, we know exactly how LLMs work, this myth that we "dont know how they work" was perpetuated by executives that dont know how they work. We know exactly how attention layers work and how they produce the next word as well as draw them from larger feature spaces.

Right, but we have no clue why, and how the emergent behavior they show works.

If we would know that, there would be no need for interpretability research.

Re: When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation

#46

Post by malfist »

heaney-555 wrote: post #18
otterdude wrote: post #2

This seems to be the end of the road for LLM's. There's only so much accuracy on a highly non-linear space you can get from a regression. If the Pareto rule is any indicator, 80% of results come from 20% of causes. It seems that we have alot more to learn about intelligence. I am reminded of a statement on truth from an ancient philosopher, this sentiment seems to be exactly the opposite of the LLM training paradigm…

>This seems to be the end of the road for LLM's This is an amazingly ignorant thing to say given the current pace of progress.

Can you quantify this rate of progress? Because someone always comes around and says there's been exponential progress in the past every time someone complains that models just aren't very good. Both can't be true

Re: When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation

#47

Post by malfist »

hiddencost wrote: post #19
otterdude wrote: post #2

This seems to be the end of the road for LLM's. There's only so much accuracy on a highly non-linear space you can get from a regression. If the Pareto rule is any indicator, 80% of results come from 20% of causes. It seems that we have alot more to learn about intelligence. I am reminded of a statement on truth from an ancient philosopher, this sentiment seems to be exactly the opposite of the LLM training paradigm…

Weird moment for this take. We're seeing some of the fastest and most impressive progress ever right now. Frontier labs have categorically different & better set ups for evaluation, they're fine. It's work but it's not a crisis.

I've heard that every week of every month for the past three years. And yet, ask an LLM about a seahorse emoji and see what happens.

Re: When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation

#48

Post by pasquinelli »

heaney-555 wrote: post #18
otterdude wrote: post #2

This seems to be the end of the road for LLM's. There's only so much accuracy on a highly non-linear space you can get from a regression. If the Pareto rule is any indicator, 80% of results come from 20% of causes. It seems that we have alot more to learn about intelligence. I am reminded of a statement on truth from an ancient philosopher, this sentiment seems to be exactly the opposite of the LLM training paradigm…

>This seems to be the end of the road for LLM's This is an amazingly ignorant thing to say given the current pace of progress.

unless you assume endless progress, fast pace of progress will get you quicker to the plateau.

Re: When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation

#49

Post by gertlabs »

nwienert wrote: post #28
gertlabs wrote: post #16

I started thinking about this back after the Llama 4 release, and since then our team has put a lot of thought into designing evaluations that don't saturate, are resistant to contamination, and can scale. What has worked best for us is using multi-agent environments with open-ended cooperative or competitive goals. Mostly designed as multiplayer games. The results tend to align with our experience for coding better…

If you're ranking Opus > Fable you're ranking "do [clearly defined thing with easy to grade endpoint]" too much. Real world doesn't value that nearly as much and it's why benchmarks are maxxed.

That's a different problem than benchmark saturation, and it's something that we are actively working on measuring objectively.

I agree that Opus 5 is not a great model, despite being clearly intelligent. It seems like a personality problem in user-driven agentic coding workflows, not a real capability issue. Not incorporating unspoken user intent, going off topic, incorporating some of the pedantry you find in GPT 5.x models, etc.

That's also likely why Opus 5 ranks low on our "Social Intelligence" benchmark (https://gertlabs.com/rankings?mode=decision), although sample sizes on this one are still low.

Re: When AI Benchmarks Plateau: A Systematic Study of Benchmark Saturation

#50

Post by Certhas »

malfist wrote: post #46
heaney-555 wrote: post #18

Earlier quoted context omitted.

>This seems to be the end of the road for LLM's This is an amazingly ignorant thing to say given the current pace of progress.

Can you quantify this rate of progress? Because someone always comes around and says there's been exponential progress in the past every time someone complains that models just aren't very good. Both can't be true

In research we are still seeing massive jumps. Subjects that LLMs were completely useless for half a year ago are now definitely in scope.

And there are benchmarks that cleanly separate the SOTA models:

https://epoch.ai/MirrorCode

Saturation of benchmarks is a property of benchmarks just as much as of the models.

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