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No existing model can effectively ground the spatial structure of student reasoning in multi-page handwritten homework, revealing a significant gap in automated assessment capabilities.
Preserving skill-level attention structures in MLLMs can dramatically reduce forgetting while adapting to new tasks without relying on replay mechanisms.
Optimizing data mixtures through gradient descent can drastically reduce training costs and improve model performance without the need for extensive simulations.
MemNovo rebalances peptide and spectral contributions during decoding, leading to up to 39.1% improvement in peptide precision without additional computational costs.
A 440MB multilingual translation model now rivals commercial APIs, opening the door for performant on-device translation.
By incorporating language guidance into federated learning, SurgFed tackles the long-standing problem of tissue and task heterogeneity in surgical video understanding, leading to improved segmentation and depth estimation across diverse surgical settings.
Stop struggling with inconsistent benchmarks for mass spec prediction: FlexMS offers a flexible framework to build and evaluate diverse deep learning architectures, revealing key factors that drive performance.
VecFormer slashes the computational cost of graph transformers while boosting out-of-distribution generalization by operating attention on quantized "graph tokens" instead of individual nodes.
