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Despite the rise of multimodal models, even the most advanced struggle with fine-grained visual tasks in pathology, revealing critical gaps in their understanding.
RAG pipeline performance hinges so heavily on the task that optimization strategies successful in one setting can completely fail in another.
LLMs can compress context better than dedicated compression modules, simply by prompting them to "think" about the task.
By reflecting on its own reasoning, ReflectRM achieves a +10.2 improvement in mitigating positional bias compared to leading generative reward models, making it a far more stable evaluator.
Securing Spiking Neural Networks against adversarial attacks can be achieved by moving neuron membrane potentials away from thresholds and introducing noise.
