residual-learning

XOResNet introduces OR-ADD shortcut connections and XOR meta-residuals to address spike redundancy and information loss in deep spiking neural networks, achieving state-of-the-art results on Fashion-MNIST, CIFAR-10, CIFAR-100, and miniImageNet.

Introduces Multi-Agent Residual In-Context Learning (MARICL), an agentic framework that uses LLM agents to analyze residuals from a base model on tabular data, hypothesize missing structure, and produce explicit correction terms via textual gradient optimization. Across nine benchmarks, MARICL consistently improves over its base model and demonstrates mechanistic generalization in cell-free protein predictions.