Nature Biotechnology, Published online: 27 March 2026; doi:10.1038/s41587-026-03074-8

Unpaired data as a first-order challenge in single-cell and spatial proteomics

arXiv:2603.19310v2 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) have been driven by reinforcement-learning-based post-training, which requires multiple rollouts with rewards. However, obtaining ground truth labels for the calculation of rewards on a scale often requires expensive human labeling or time-consuming verification procedures. For instance, evaluating mathematical proofs demands expert review, and open-ended question answering lacks definitive ground truth. When ground truth labels are scarce, the effectiveness of reinforcement learning fine-tuning can be constrained. We introduce MemReward, a graph-based experience memory framework: an initial LLM policy generates rollouts for each query, each comprising a thinking process and a final answer, and these rollouts are stored as experience memory. Queries, thinking processes, and answers form nodes in a heterogeneous graph with similarity and structural edges; a GNN trained on labeled rollouts propagates rewards to unlabeled rollouts during online optimization. Experiments on Qwen2.5-3B and 1.5B in mathematics, question answering, and code generation demonstrate that MemReward, with only 20% labels, achieves 97.3% of Oracle performance on 3B and 96.6% on 1.5B, surpassing Oracle in out-of-domain tasks. Performance scales smoothly with label budget, reaching 99.4% of Oracle at 70% labels.

Nature Cancer, Published online: 25 March 2026; doi:10.1038/s43018-026-01136-z

Czernilofsky et al. identified factors that reprogram stromal cells into an inflammatory, dysfunctional state, leading to the structural disorganization of lymph nodes in B cell lymphoma at single-cell and spatial resolutions.

arXiv:2603.12591v1 Announce Type: cross Abstract: Federated learning on heterogeneous edge devices requires personalized compression while preserving aggregation compatibility and stable convergence. We present Curvature-Aware Heterogeneous Federated Pruning (CA-HFP), a practical framework that enables each client perform structured, device-specific pruning guided by a curvature-informed significance score, and subsequently maps its compact submodel back into a common global parameter space via a lightweight reconstruction. We derive a convergence bound for federated optimization with multiple local SGD steps that explicitly accounts for local computation, data heterogeneity, and pruning-induced perturbations; from which a principled loss-based pruning criterion is derived. Extensive experiments on FMNIST, CIFAR-10, and CIFAR-100 using VGG and ResNet architectures under varying degrees of data heterogeneity demonstrate that CA-HFP preserves model accuracy while significantly reducing per-client computation and communication costs, outperforming standard federated training and existing pruning-based baselines.