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Dysco cuts training loss by up to 9 times and boosts federated learning performance by dynamically aligning client-specific subspaces, tackling a critical source of instability in LoRA aggregation.
LLMs can generate better features from tabular data when deployed as a multi-agent system with explicit memory of past procedures, feedback, and concepts.
Achieve state-of-the-art medical lesion segmentation across diverse modalities and lesion types with a single, unified model that outperforms specialized approaches.
PINNs can be made dramatically more robust to noisy data (up to 96.6% error reduction) by selectively pruning neurons that are overly sensitive to corrupted samples.
