Read that as abstract reasoning solved, then look one column over on the same scorecard: the same model, same maximum effort, scores 7.78% on ARC-AGI-3, the interactive benchmark the same team built next, where humans still score 100%.
“GPT-5.6 Sol leads the ARC-AGI-2 leaderboard at 92.5%, clearing the abstract-reasoning benchmark built specifically to resist AI and beating the average human, a result read as frontier models cracking fluid reasoning.”
ARC Prize verified leaderboard and coverage (GPT-5.6 Sol, ARC-AGI-2, August 2026)
Recorded 19 August 2026. Our ruling means: literally true, materially misleading.
Like its predecessors ARC-AGI-1 and 2, ARC-AGI-3 focuses entirely on evaluating fluid adaptive efficiency on novel tasks, while avoiding language and external knowledge
We introduce ARC-AGI-3, an interactive benchmark for studying agentic intelligence through novel, abstract, turn-based environments in which agents must explore, infer goals, build internal models of environment dynamics, and plan effective action sequences without explicit instructions.
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“AI is dramatically accelerating AI research at Anthropic right now, roughly doubling the pace of capabilities progress.”
TRUE, BUTLab Not FieldAjeya Cotra on Planned Obsolescence, reading METR's assessment and Anthropic's S-1 statement that it may imminently meet its own threshold
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