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Current LLMs falter in domain-specific reasoning, revealing critical gaps that Benchmark Agent's evolving benchmarks can help address.
Existing image-to-image evaluations miss a critical aspect: whether the output image actually preserves the content of the input.
Current Full-Duplex Speech Language Models stumble in multi-round conversations, struggling to maintain consistent performance across turns and various evaluation dimensions.
