NVIDIA AVO Achieves 100% on ARC-AGI-3 Benchmark

ArticleTerry Chen, Yeyin (Eva) Zhu, Zhifan Ye, Jean-Francois Puget and Humphrey ShiResearch

NVIDIA has introduced Agentic Variation Operators (AVO), an agent architecture that enables Claude Opus 5 to reach a 100% completion rate on the ARC-AGI-3 interactive reasoning benchmark.

Key points

  1. AVO completed all 25 environments and 183 levels of the ARC-AGI-3 public set with a 100.00 RHAE score, requiring 6,624 environment actions.
  2. Compared to the VISTA system using the same Claude Opus 5 model, AVO required approximately 12% fewer actions.
  3. The architecture relies on persistent memory to retain execution history and a supervisor to redirect the main agent when progress stalls.
  4. In an earlier seven-day autonomous run on DGX B200 systems, AVO evolved attention kernels that outperformed FlashAttention-4 by up to 10.5% and cuDNN by up to 3.5%.
  5. The agent operated purely on text in ARC-AGI-3, processing observations via a 64x64 text grid without image tokens.

Why it matters

These results illustrate that frontier long-horizon performance is driven by the overall agent system—including state management and error recovery—rather than base model capability alone.

Analysis

By successfully applying identical agent mechanisms to both GPU kernel engineering and interactive rule discovery, NVIDIA shows that systematic iteration, hypothesis testing, and supervisory intervention can transfer cleanly across disparate problem domains.

Found in

Summary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.

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