SALT is a subspace-adaptive plug-in for GRPO that decomposes group-relative coefficients into shared and residual channels using mini-batch Gram geometry and amplifies residuals to mitigate signed cancellation in RLVR.
Multimodal survival prediction in advanced pancreatic cancer using machine learning
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MSB is a late-fusion stacking framework for multimodal survival prediction under blockwise missingness that improves C-index over baselines on the PIONeeR lung cancer immunotherapy dataset.
Intrinsic dimension of quantum trajectories serves as an unsupervised probe sensitive to chaos, integrability, and ergodicity breaking in dissipative quantum systems.
citing papers explorer
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SALT: When More Rollouts Don't Help in Group-Based Policy Optimization and How to Make Them Matter
SALT is a subspace-adaptive plug-in for GRPO that decomposes group-relative coefficients into shared and residual channels using mini-batch Gram geometry and amplifies residuals to mitigate signed cancellation in RLVR.
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Multimodality Stacking with Blockwise missing values and application to the PIONeeR biomarkers study for prediction of resistance to immunotherapy
MSB is a late-fusion stacking framework for multimodal survival prediction under blockwise missingness that improves C-index over baselines on the PIONeeR lung cancer immunotherapy dataset.
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Complexity of Quantum Trajectories
Intrinsic dimension of quantum trajectories serves as an unsupervised probe sensitive to chaos, integrability, and ergodicity breaking in dissipative quantum systems.