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1 Pro introduces a hybrid architecture to solve the most difficult reasoning benchmarks like ARC-AGI-2. By combining linear attention with sparse MoE, it achieves massive leaps in fluid intelligence while keeping inference costs low.

Paper2026-02-19•Source ↗•38 attns346 checkouts

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Gemini 3.1 Pro: A Smarter Baseline for Complex Reasoning on ARC-AGI-2

Authors

Google DeepMind Team

ABSTRACT

We introduce Gemini 3.1 Pro, an enhanced version of the Gemini 3 series optimized for rigorous logic and long-horizon problem solving. Built on a hybrid architecture that fuses linear attention with a sparse mixture-of-experts (MoE) comprising 397B total parameters, the model activates only 17B parameters per forward pass, maintaining high efficiency. We evaluate 3.1 Pro on ARC-AGI-2, a benchmark for fluid intelligence and novel pattern recognition, where it achieved a verified score of 77.1%. This represents more than a 2x improvement in reasoning performance over its predecessor, signaling a significant step toward generalizable intelligence in autonomous agents.

#foundation-models #reasoning #cs-ai #cs-cl

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