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Even the top-performing LLM struggles with realistic user interactions, achieving only 61% success in complex task scenarios.
Current AI models for liver fibrosis staging can match expert radiologists in some settings, but real-world clinical deployment is still hampered by data heterogeneity and label imbalance.
State-of-the-art video generation and editing now hinges on a surprisingly simple division of labor: MLLMs for semantic planning, diffusion models for photorealistic rendering.
Current image difference captioning benchmarks fail to capture semantic consistency and penalize hallucinations, but DiffCap-Bench offers a robust alternative that aligns with human expert judgments and predicts downstream utility for image editing.
