AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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RG-ICL is introduced, a training-free inference framework that retrieves query-aligned demonstrations using frozen encoders, without task-specific parameter updates, and indicates that for medical image interpretation, curating which reference cases an MLLM sees is a practical alternative to retraining it.
Temporal Retinal U-Net is developed, a deterministic single-pass predictor conditioned on irregular imaging history and prediction horizon that achieved the strongest overall performance among the evaluated classical and deep-learning comparators on image-level and eye-specific progression measures.
A reasoning-driven vision-language framework that explicitly models the ophthalmologist's diagnostic workflow by generating structured clinical reasoning prior to diagnosis is developed, demonstrating that explicitly modeling expert clinical reasoning simultaneously improves interpretability and diagnostic performance.
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