Artificial Analysis Intelligence Index v4.2 (artificialanalysis.ai)

is any of this 'scientific'? does AA allow peer review of its processes? are these published in journals of at least medium impact? what are the sample sizes? how grounded is their mechanistic reasoning?

like they have words that are dressed in scientific language on their site like

"We estimate a 95% confidence interval for Artificial Analysis Intelligence Index of less than ±1% - based on experiments with >10 repeats on certain models for all evaluation datasets included in Artificial Analysis Intelligence Index v4.2."

but where's the outcome dataset justifying this? how did they get that probability? what was the specific methodology of the tests? what variables did they account for?

there's a major difference between scientific sounding and being truly empirically rigorous. the 'research' in AI intelligence feels somehow even less trustworthy than supplement-funded studies because those are at least subjected to scrutiny by peers without profit motives

In real life, there is always this feedback edge from the results to the methodology.

Theoretically it's unscientific to do tweaks like this but in reality this is what actual science is because you need to see the results understand the problems in your experimental designs.

Now the interesting thing is that you could repair the bias problem. The main issue is that you will do these tweaks after a bad result not after they look fine.

Maybe we could commit ahead of time that the experiment will be reanalyzed after results regardless of what they are. Instead of only when the results disprove the hypothesis.

So like artificial analysis committing to a fixed cadence of index updates instead of when the results start looking jank.

Given how many private benchmarks they're using now, its likely they just tested different combos until they got the result they wanted.

Completely discredits the index if it just gets modified to match social media vibes.

Hopefully this wakes people up from this addiction to benchmarks when discussing various AI models. Different model families have strengths and weaknesses in various domains, but those are never discussed.

It's a benchmark for brand new technology and you admit the old score was silly but you think updating it is unscientific?

What evidence do you have that they tweaked the index to fit what they thought people expected?

This matches what I've seen building anything that uses an LLM for narrow structured output rather than open ended chat, things like classifying a diff into a fixed set of categories or summarizing a change. The aggregate benchmark score barely predicts how it behaves in production. What actually breaks a pipeline is a confident wrong answer on the small slice of inputs that don't fit the pattern it saw during training, not a lack of raw capability. A model that says it isn't sure on the edge cases is far more useful to me than one that scores higher on average but never admits uncertainty, because the wrong but confident output is the one that slips through review unnoticed.

I don't agree. The main issue with their scoring/methodology is that the numbers make it seem like 5-6 models have little to no difference when in fact there is a significant difference between fable and opus and sol and astra for example. They are popular mainstream but most of their benchmarks are either not a representation of model strengths enough or they are not doing a good job of showcasing it properly. The fact that muse and 3.8 were high a day back shows they are just the modern version of lmareana for the mass audience and PR stunts.

Hallucinations are very damaging to a model’s utility. But doesn’t the Omniscience Index focus on knowledge-based queries? To me, using LLMs for their memorized knowledge is very 2023 and suboptimal.

IMO, what really makes a model useful is its ability to process information within its context reliably and faithfully. I don’t care if it hallucinates George Washington’s favorite color, but I do care about it hallucinating the results of tool calls.

GPT-6 has a looped transformer / recurrent depth architecture, so it gets some internal CoT reasoning "for free" with no output tokens.

This also makes it much harder to monitor its reasoning.

The apparent advantage is exaggerated by them running Astra at six different effort levels, and almost everything else at just the maximum available effort.

I don't really understand why they keep doing this. Either run and report everyone at multiple effort levels, or run everyone at only one.

But alsi, token efficiency seems pretty artificial? For example tokenizers are different from model to model. The cost/perf Pareto frontier seems a lot more meaningful (and Astra does very well at that too, just to be clear. It seems to be a great model.)

[delayed]

The timing is related to the fact that their benchmark was saying it was the same as Sol, and below Opus 5, when anecdotal reports and other benchmarks strongly disagree. It looked bad for them for their benchmark to disagree with people's lived experience so hard.

It's very different than it was earlier today, so pretty sure it's v4.2.

Images from their X account