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Tailoring medical image segmentation models to dataset-specific requirements can boost performance by over 10% compared to traditional architecture-first approaches.
LLMs struggle to navigate the complex, multi-turn justification and response dynamics of real-world patent examination, revealing critical gaps in legal reasoning and technical novelty judgment.
Today's best multimodal agents still fall into "blind execution" traps when building websites from ambiguous, non-expert user instructions, highlighting a critical gap in intent recognition and adaptive interaction.
Despite the promise of transcriptomic models for predicting immunotherapy response, existing models fail to generalize across independent patient cohorts, raising serious questions about their clinical utility.