GAIA benchmark shows humans at 92% accuracy on simple real-world questions far outperform current AI systems at 15%, proposing this gap as a key milestone for general AI.
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The paper proposes a category theory framework for comparing AGI architectures including RL, causal RL, and schema-based learning.
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GAIA: a benchmark for General AI Assistants
GAIA benchmark shows humans at 92% accuracy on simple real-world questions far outperform current AI systems at 15%, proposing this gap as a key milestone for general AI.
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Working Paper: Towards a Category-theoretic Comparative Framework for Artificial General Intelligence
The paper proposes a category theory framework for comparing AGI architectures including RL, causal RL, and schema-based learning.