Categories: FAANG

MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains

Recent advances in large language models (LLMs) have increased the demand for comprehensive benchmarks to evaluate their capabilities as human-like agents. Existing benchmarks, while useful, often focus on specific application scenarios, emphasizing task completion but failing to dissect the underlying skills that drive these outcomes. This lack of granularity makes it difficult to deeply discern where failures stem from. Additionally, setting up these environments requires considerable effort, and issues of unreliability and reproducibility sometimes arise, especially in interactive tasks. To…
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What tools would you use to make morphing videos like this?

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DeepMind introduces AI agent that learns to complete various tasks in a scalable world model

Over the past decade, deep learning has transformed how artificial intelligence (AI) agents perceive and…

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