Foundation models for single-cell RNA sequencing (scRNA-seq) have shown promising capabilities in capturing gene expression patterns. However, current approaches face critical limitations: they ignore biological prior knowledge encoded in gene regulatory relationships and fail to leverage multi-omics signals that could provide complementary regulatory insights. In this paper, we propose GRNFormer, a new framework that systematically integrates multi-scale Gene Regulatory Networks (GRNs) inferred from multi-omics data into RNA foundation model training. Our framework introduces two key innovations. First, we introduce a pipeline for constructing hierarchical GRNs that capture regulatory relationships at both cell-type-specific and cell-specific resolutions. Second, we design a structure-aware integration framework that addresses the information asymmetry in GRNs through two technical advances: (1) A graph topological adapter using multi-head cross-attention to weight regulatory relationships dynamically, and (2) a novel edge perturbation strategy that perturb GRNs with biologically-informed co-expression links to augment graph neural network training. Comprehensive experiments have been conducted on three representative downstream tasks across multiple model architectures to demonstrate the effectiveness of GRNFormer. It achieves consistent improvements over state-of-the-art (SoTA) baselines: 3.6\\% increase in drug response prediction correlation, 9.6\\% improvement in single-cell drug classification AUC, and 1.1\\% average gain in gene perturbation prediction accuracy.

Mixture-of-Experts (MoE) has emerged as a promising approach for scaling large language models efficiently. However, how to design a desired MoE architecture given performance, efficiency, or safety goals remains absent. Existing benchmarks often focus on isolated aspects (e.g., reasoning, efficiency, safety), and there is a lack of consensus on optimal design choices, such as the number and size of experts, the type of routers, and the regularization during pre-training, or strategies like freezing, learning rate adjustments, and limiting expert collaboration during fine-tuning, with prior works often yielding conflicting conclusions. Motivated by this research gap, we introduce MoEBench, the first comprehensive assessment of MoE designs across the three dimensions of reasoning ability, efficiency, and safety. Our benchmark systematically evaluates optimal architectural choices during both pre-training and fine-tuning phases. We evaluate two popular MoE backbones across four dimensions of design choices on over eight metrics. Our empirical findings uncover hidden underlying correlations among MoE design choices. Specifically, we observe that (1) token-level routing and z-loss regularization improve reasoning performance; (2) shared experts enhance training stability but reduce specialization; and (3) collaboration-constrained routing and freezing strategies significantly influence load balance, specialization, and safety alignment. Furthermore, we propose three “sweet point” combinations of optimal strategies tailored to different scenarios. We hope this study provides actionable insights for building more robust, efficient, and secure MoE models. Code, checkpoints, and raw data will be released upon acceptance of the paper.