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

“The seeker after truth is not one who studies the writings of the ancients and, following his natural disposition, puts his trust in them, but rather the one who suspects his faith in them and questions what he gathers from them, the one who submits to argument and demonstration and not the sayings of human beings whose nature is fraught with all kinds of imperfection and deficiency. Thus the duty of the man who investigates the writings of scientists, if learning the truth is his goal, is to make himself an enemy of all that he reads, and, applying his mind to the core and margins of of its content, attack it from every side. he should also suspect himself as he performs his critical examination of it, so that he may avoid falling into either prejudice or leniency.” - ibn al-Haytham

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 than any non-aggregator benchmark, and likely at lower cost to run.

Data at https://gertlabs.com/rankings

It appears y combinator has removed this interesting paper from its top trending position. Gee I wonder why?

37 authors and contributors need to be named up top?

Oh! I got my name on a paper! I don't think there is much reward for it these days.

As a PC gamer who grew up in the 00s, this has been something I’ve tried to warn ardent LLM and model enthusiasts about for quite some time.

Benchmarks are handy when they’re new, novel, and constantly changing. The second you let even a single aspect of it stagnate, it becomes a gameable score rather than a useful metric. In PC Gaming, we saw vendors optimize for specific titles, benchmark tools, and scenarios at the expense of general performance, and eventually the industry had a “come to Jesus” moment where we had to collectively decide how to move forward from an industry built on thoroughly gamed benchmarks, with entities like Gamers’ Nexus and Digital Foundry being the end results of that falling out.

LLMs were always going to end up the same way, because the people building the benchmarks - well-intentioned as they were - ultimately fell into the exact same traps with fixed scoring rubrics, known test questions, and believing in some form of “completeness” that could be attained or achieved. The net result are models consistently scoring better on benchmarks but also seeing diminishing returns and rising vulnerabilities, because actual improvement or utility isn’t what they’re being optimized for so much as bragging rights. It’s why there’s so much growing interest in things like MoE execution on unified memory platforms as a means of porting larger models to consumer kit, or ternary models (shoutout to Bonsai) as a means of reducing overall size: both take leading edge, benchmark-saturating models and show that with minimal score loss, they function about as well as frontier models might.

Building a new benchmark won’t solve the problem, either. To move forward, we must evaluate LLMs objectively and with continuously evolving workloads. More “pelican on a bicycle” stuff, but from varying perspectives and use cases. Radiologists putting models through their paces with usable sample data they don’t share with AI labs, or IT folks tasking agents with bootstrapping specific, real-world workloads. To prove general intelligence, we need more specialists evaluating them specifically and generally in ways that are transparent to consumers but difficult or impossible for AI companies to prepare against.

Only then will scoring values matter.

Check out https://agents-last-exam.org/ there is still room for improvements!

I think its been pretty clear that in abnsense of clear use cases that are monetizable many model providers have been benchmaxxing on abstract or low utility average user performance.

This results in a lot of "oh wow it can do math I dont care about" and "it can't code a lot, but not well" outcomes instead of the core needs:

1) Cheaper faster and real time 2) Long walk capable without losing attention while rescoring goals over updated enviroment 3) Specific domain knowledge that can be trained quickly into the model (how we do work in this specific case)

This is AI slop. They didn't even change the plots default template.

Slop

> We find that nearly half of the our bench- marks exhibit saturation