Current frontier AI benchmarks increasingly function as optimization drivers, encouraging the scaling of large language models toward general-purpose behavioral imitation. While effective in the short term, this paradigm incentivizes expensive data annotation, shifts focus away from research-driven AI development, and lacks formal guarantees against manual inspection or leakage of private benchmark components during internal evaluation cycles. We introduce ARCTIC (The Abstraction and Reasoning C