NVIDIA’s coding agent, AVO, cleared every level of the ARC-AGI-3 public set, scoring 100 percent across all 183 levels in 25 public games. The same underlying model, Anthropic’s Claude Opus 5, scored only 30 percent on its own. The jump came from the harness around the model, the software layer that plans, acts, observes, and corrects, not from a change to the model itself.
What is AVO and how was it built?
AVO is a coding agent that NVIDIA built as a harness, a software wrapper, around Anthropic’s Claude Opus 5. The system receives no rules, no prior instructions, and no stated goals. Instead it learns by trying actions, observing the results, and correcting itself.
AVO was originally designed for a different job: optimising CUDA GPU kernels. In that earlier run it worked autonomously for seven days, explored more than 500 directions, and produced kernels that beat FlashAttention-4 by up to 10.5 percent. To test it on ARC-AGI-3, NVIDIA did not change the core agent architecture. It swapped the GPU engineering tools for the ARC-AGI-3 task interface.
How did AVO perform on ARC-AGI-3?
On the ARC-AGI-3 public set, AVO solved all 183 levels, a perfect 100 percent score. Claude Opus 5 on its own scored 30 percent on the same set. The gap shows what the harness layer adds: the ability to plan a sequence of moves, watch what happens after each one, and revise the plan when an action fails.
AVO also finished the set more efficiently than the earlier VISTA agent. It cleared the 183 levels in 6,624 actions, about 12 percent fewer than VISTA’s 7,542 actions on the same tasks.
What is not yet known about AVO?
ARC-AGI-3 does not allow external harnesses to run against its hidden private set, so AVO’s performance on the private evaluation is unknown. The 100 percent figure applies only to the public set, which contains 183 levels across 25 games.
Why does the harness matter so much?
The result points to a clear pattern in agent design. The model inside AVO, Claude Opus 5, is the same model that scored 30 percent when used directly. Moving to 100 percent required no model retraining, just the surrounding software that lets the model take actions, observe outcomes, and try again.
That same harness pattern powered AVO’s earlier CUDA work, where it ran for seven days, explored over 500 directions, and produced kernels faster than FlashAttention-4. In both cases the agent architecture, not the base model, did the heavy lifting.
FAQ
What did NVIDIA’s AVO score on ARC-AGI-3?
AVO scored 100 percent on the ARC-AGI-3 public set, clearing all 183 levels across 25 public games.
Why does AVO score so much higher than Claude Opus 5 alone?
AVO is a harness, a software wrapper, around Anthropic’s Claude Opus 5. It tries actions, observes results, and corrects itself. Claude Opus 5 on its own scored only 30 percent on the same set.
Is AVO’s ARC-AGI-3 private-set score known?
No. ARC-AGI-3 does not allow external harnesses to run against its hidden private set, so AVO’s performance on the private set is unknown.