ARC-AGI-3
Beyond Market Intelligence keeps ARC-AGI-3 in one place: 3 stories so far. The section currently leads with “How small AI models closed the gap on a test built for humans”, “Explore how GPT-6 turns raw data into actionable insight without the complexity.”, and “Schema proves process can match model power in ARC reasoning”. For the first time, small local AI models have matched average human performance on a benchmark built specifically to prove human superiority, and it happened in just the last 30 days. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every ARC-AGI-3 story on Beyond Market Intelligence, newest first.
How small AI models closed the gap on a test built for humans
For the first time, small local AI models have matched average human performance on a benchmark built specifically to prove human superiority, and it happened in just the last 30 days. Kagglers, limited to modest hardware, are now seeing their models beat the human baseline in a controlled harness. That's not hype; it's a measured shift. For a deeper look at how AI is redefining productivity tools, see our coverage of "How We Built IQRAX to Give AI Models Both Freedom and Verifiable Authority."
Explore how GPT-6 turns raw data into actionable insight without the complexity.
GPT-6 is here, and its benchmark results are hard to ignore. On ARC-AGI-3, it scores around 60% without a harness, a figure that climbs significantly when one is applied. President Greg Brockman's pre-launch comment that we may now be in the AGI era feels less like hype and more like a statement of direction. That raises a practical question we should sit with: if models outperform human baselines on tests like GDPval-AA v2, why are knowledge workers still employed?
Schema proves process can match model power in ARC reasoning
A 99% score on ARC‑AGI‑3 is the kind of number that makes you stop and ask how. Schema, a harness built around Claude Opus 4.8 and Fable 5, claims exactly that without touching model weights. It reshapes the process: turning observations into working game models, testing predictions against interaction history, and revising plans on the fly. The fallback rule is refreshingly simple, rerun below 80 with a different model and keep the higher score. That's practical engineering, not magic.