NVIDIA just revealed that its general-purpose agent architecture AVO achieved a 100% score on the ARC-AGI-3 public benchmark, solving all 183 levels across 25 environments. Built with persistent memory and a supervisor module, AVO required 12% fewer actions than rival harness VISTA. The experiment underscores that sustained agent performance relies on the harness as much as the frontier model itself.
OpenAI found that GPT-5.6 Sol's surprisingly low ARC-AGI-3 benchmark score was caused by the test harness dropping reasoning history and aggressively truncating context. By switching to its Responses API and enabling retained reasoning alongside compaction, the model tripled its score while slashing output tokens by 6x. It is a stark reminder that benchmarks evaluate harness architecture just as much as raw intelligence.
ARC Prize just launched ARC-AGI-3, a benchmark designed to evaluate how AI systems learn rather than what they already know. While humans score a perfect 100%, current AI models scrape under 1%. ARC Prize notes this stark divide demonstrates that genuine AGI has not yet arrived.
