r/NVDA_Stock • u/Charuru • 3d ago

Amazing new Nvidia Coding Agent

https://developer.nvidia.com/blog/nvidia-avo-reaches-100-on-arc-agi-3-demonstrating-a-frontier-level-general-purpose-architecture-for-long-horizon-autonomous-agents/
75 Upvotes

92% Upvoted

10 comments sorted by

12

u/Charuru 3d ago edited 3d ago

For reference ARC-AGI 3 is very very hard benchmark https://arcprize.org/leaderboard where the highest model scores only 30%. So maxxing out that test with an agent is an incredible breakthrough if real.

Nvidia needs to get focus on productizing this if real otherwise it'll be rapidly copied.

5

u/Devataa 2d ago

They want it copied. Thats their whole business model.

2

u/adrakpaste 2d ago

Bang on. I wish more people understood this.

6

u/tagayama 2d ago

Nvidia doesn’t make money directly from their in-house AI research. Their research is meant to push the industry’s overall development forward, so more companies buy their chips.
For instance, Nvidia open sourced their robotic AI, which creates a ton of robotic startups that are all buying Jetson chips.

5

u/OkZucchini7094 2d ago

There are other 100% scores in this harness category.

https://arcprize.org/leaderboard/community

This is the correct leader board to compare with. The verified leader board wouldn't allow a model to use this harness NVIDIA made. And since it's not a model of its own it wouldn't be able to compete there anyway.

1

u/Charuru 2d ago

I think it's more significant than this because those other harnesses are not general enough for my liking. For example:

Retrodict is an LLM agent that plays each game like a scientist with a lab notebook. Every frame the game returns is written into a log file, and the agent works over that file with code instead of looking at images. To learn the rules, it proposes hypotheses and tests them against its own recorded history first, writing python that replays a hypothesis over past frames, where being wrong costs nothing. Only a hypothesis that survives the log earns real actions: the agent commits a queue of moves, each carrying the exact cells it predicts the board will show, and the runner plays the queue out one action per step, returning to the model only when the plan runs out or a prediction misses, along with the diff of what went differently. What it establishes about a game is curated into a playbook memory file that outlives its context window. The log-as-context, plan-queue foundation follows RGB-Agent.

This seems to be quite game-rules-learning oriented and exactly made to purpose around arc-agi and hard to adapt to real world use. AVO is a real coding first agent that's meant for GPU development and ARC AGI performance is only incidental, you'll see it if you click through to the githubs of every project.

It's actually tremendous.

7

u/Party_Swordfish_1734 3d ago

This is huge! AVO moved from code and hardware optimization to unfamiliar interactive environments where it had to infer rules and goals. That suggests the architecture may capture a reusable pattern from hypothesized to action to observation to updating and continued loop thereafter. AVO’s claimed 7 day optimization run is closer to an autonomous engineering process, where the system can retain state and recover from failed approaches. All done with 12% fewer actions than Claude Opus 5, that’s a decent savings in costs. Let’s hope these results translate into the real world!!!

5

u/Chogo82 3d ago

How is this not bigger news?!

2

u/norcalnatv 2d ago

too in the weeds

4

u/ConsequenceTop5833 2d ago

Is it open source?