Modality-Aware Mixture-of-Experts Routing with Uncertainty and Cost Constraints

TL;DR: Can we make MoE models smarter by routing based not just on modality, but also on uncertainty and computational cost? Let’s add market-inspired or uncertainty-driven routers to the MoE, improving efficiency and safety, especially for underrepresented modalities.

Research Question: How can uncertainty estimation and economic cost considerations be incorporated into MoE routing to optimize expert utilization in multimodal models?

Hypothesis: A routing policy that considers both epistemic uncertainty and compute cost will outperform naive modality-based routing, yielding more robust and efficient multimodal models—especially in resource-constrained or safety-critical scenarios.

Experiment Plan: Integrate uncertainty-aware routing (e.g., from Agora by Zhang et al. 2026) and cost-awareness into the MoE router of a multimodal model. Measure improvements in expert specialization, computational efficiency, and performance under tight resource budgets or adversarial (e.g., safety-critical) inputs. Compare to standard MoE and AsyMoE architectures on safety and efficiency benchmarks (e.g., Lingua-SafetyBench).

References:

• Shengbang Tong, D. Fan, J. Nguyen, et al. (2026). Beyond Language Modeling: An Exploration of Multimodal Pretraining.
• Jusheng Zhang, Y. Fan, K. Cai, J. Yang, J. Yao, J. Wang, G. Qu, Z. Chen, K. Wang. (2026). Why Keep Your Doubts to Yourself? Trading Visual Uncertainties in Multi-Agent Bandit Systems. arXiv.org.
• Heng Zhang, H. Hu, Y. Shen, W. Yu, Y. Yuan, H. You, G. Cheng, Z. Zhang, L. Gan, H. Wei, H. Zhang, J. Huang. (2025). AsyMoE: Leveraging Modal Asymmetry for Enhanced Expert Specialization in Large Vision-Language Models. arXiv.org.
• Enyi Shi, P. Shao, Y. Zhang, C. Cui, J. Lyu, X. Xie, X. Xia, F. Shen, T.-S. Chua. (2026). Lingua-SafetyBench: A Benchmark for Safety Evaluation of Multilingual Vision-Language Models. arXiv.org.