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This paper proposes a Partial Information Decomposition framework to select the most informative pair of MRI contrasts for brain tumor segmentation using lightweight 3D U-Nets, reducing computational cost while maintaining performance.
A benchmark study comparing traditional machine learning methods (Random Forest, XGBoost, SVM, Logistic Regression) against lightweight transformer variants (DistilBERT, TinyBERT, MobileBERT) for on-device fault detection across three public datasets. Traditional ML offers competitive accuracy at far smaller resource footprints, while TinyBERT-4L is the most deployment-friendly transformer.
Latent Memory introduces a compressed representation approach for external memory in question answering, reducing token consumption and storage requirements while maintaining competitive performance across text-only and multimodal benchmarks.
This paper introduces GNOVA, a GRU-Neural ODE Variational Autoencoder framework for reconstructing and forecasting Alzheimer's disease cognitive trajectories from routine clinical data without expensive neuroimaging or biomarkers, achieving low error and uncertainty estimation on the ADNI dataset.
This paper studies failure modes in shared-state collaborative reasoning for resource-constrained visual agents, introducing CoSee, an auditing framework that formalizes read-write-verify loops. It finds that naive shared workspaces can amplify hallucinations and identifies noise reinforcement and policy collapse as dominant failure modes.
TIDE is a lossless inference system for diffusion large language models that leverages temporal stability of expert activations to reduce I/O overhead and computation, achieving up to 1.4-1.5x throughput improvements on single GPU-CPU systems.