Plotting ARC-AGI model scores reveals a strong relationship across all three benchmarks.
摘要
绘制 ARC-AGI 模型分数后发现，三项基准之间存在强关联。 例如，幂曲线 v2 = v1^4.53 可解释 ARC-AGI-2 分数约 98% 的变异。在 ARC-AGI-1 上得分相同的模型，在 ARC-AGI-2 上仍可能相差超过 20 个百分点。
摘要由模型根据原文撰写，以原文为准。
正文 · 原文
Plotting ARC-AGI model scores reveals a strong relationship across all three benchmarks.
For example, the power curve v2 = v1^4.53 explains about 98% of the variation in ARC-AGI-2 scores. Models tied on ARC-AGI-1 can still differ by over 20 percentage points on ARC-AGI-2.
来源：ARC Prize · x.com
引用原文
arcprize. Plotting ARC-AGI model scores reveals a strong relationship across all three benchmarks.[EB/OL]. (2026-10-29)[访问日期]. https://x.com/arcprize/status/2115475078175395986.