Nvidia AVO scores 100% on the ARC-AGI-3 interactive reasoning benchmark

Underlying article should be the link:

https://developer.nvidia.com/blog/nvidia-avo-reaches-100-on-...

There you will find the extremely important qualifier it's the public set, not the private set (with the risk of overfitting, ie the results not repeating when submitted to be run in competition), and the detail that this is essentially a harness added to Opus 5, not Nvidia's own models.

Obviously still impressive, you would think.

I thought ARC-AGI-3 was explicitly a test of raw model performance excluding the harness? Adding the harness back in doesn't tell us anything new. We've known that agents are capable of long horizon reasoning with sufficient harnesses. GPT-4(?) was capable of beating Pokemon 18 months ago but models only became capable of beating it without a harness in the last six months...?

The 100% score was achieved on the 25 public set, not on the semi-private or private sets.

Verified high score is just 40%: https://arcprize.org/leaderboard

The blog post: https://developer.nvidia.com/blog/nvidia-avo-reaches-100-on-...

Using Claude Opus 5, but it can use others:

AVO is also designed to operate across frontier models. While our full public-set result used Claude Opus 5, we additionally paired AVO with GPT-5.6 Sol on a challenging subset of games. In these limited experiments, Sol reached matched levels faster in wall-clock time in several cases, while Opus used fewer environment actions in matched-level comparisons. These preliminary results suggest complementary operating profiles across models, and we leave a broader systematic comparison to future work

None of the tweets, nor the press release, seems to mention how long time it actually took E2E to complete the evaluation, but they do mention it took "12% fewer actions" compared to just Opus 5 without AVO. Feels a bit suspicious they don't break down the timing involved, looking at the diagram from the press release, it gives the impression there is a lot of machinery here, and given they claim fewer actions, each action must be more carefully considered, doesn't it?

Curious to read more about it though, seems the paper for it is here: https://arxiv.org/pdf/2603.24517, I'm not sure I understand if it's better than just Codex with a /goal, as they talk about "can discover performance-critical micro-architectural optimizations" but leave Codex alone for a day or two and you'll get the same results without doing "additional autonomous adaptation" at all.

I wonder if these benchmarks swap words, meaning and more because you might as well be benchmaxxing for specific words. I notice a lot of recurring just structural sentences coming back in smaller LLM models where they're fit for a specific task which is fine because most of the work we do is repetitive and there are patterns to learn but they should be word agnostic which I wonder if LLM can really fix.

AVO: Agentic Variation Operators for Autonomous Evolutionary Search

https://arxiv.org/html/2603.24517v1

Thoughts on Nvidia releasing AVO or even open sourcing it? They've been very open with their models.

Is this AGI? I don't think I can score 100% on ARC AGI.

Can we please prioritize links to the papers, github repos, press releases, or blog articles for these types of posts? I don't use Twitter and I don't think anyone else should either.

Now we’re talking

The next year is going to be wild folks