Optically switched networks suit the regular communication of dense ML models, but MoE introduces sparse, runtime-dependent traffic. We show that efficient offline-optimized routing enables efficient MoE training and inference on direct-connect topologies without the need for MoE traffic matrix or dynamic topology reconfiguration. MoX constructs token-aware multicast trees to reduce bandwidth tax, then uses static, precomputed link weights to balance traffic by solving a restricted multicast tree-packing problem. Using recorded traffic from large MoE models, token-level traces, and ASTRA-sim, we find that MoX accelerates the full MoE block -- dispatch, expert computation, and combine -- by up to 1.8x over min-hop routing. Moreover, it attains nearly ideal packet-switched network performance in random expander topologies. On a 1,024-TPU model of Google's Boardfly topology, MoX reduces the dispatch bottleneck link load by up to 47%. These results show that high-performance MoE on static direct-connect fabrics can be achieved via optimized load-oblivious routing without demand-driven reconfiguration.
LLM agents increasingly rely on tool calling to act on external systems, and the Model Context Protocol (MCP) has quickly become its de facto interface. Operating MCP at cloud scale, however, becomes difficult. On the tool provider side, legacy services are not directly callable…
Network routing scalability is hard to achieve when forwarding state is driven by external entities such as end users or multicast groups. Named Data Networking (NDN) faces this challenge acutely: it fetches data by name, which ties forwarding state to an unbounded number of appl…
Designing deployable and resilient network topologies from natural language requirements remains a challenging problem in network automation. This work investigates the ability of Large Language Models (LLMs) to generate structurally valid and constraint-compliant network topolog…
Reliable and low-latency NR-V2X communications are essential for smart mobility in dense urban environments. However, limited Road-Side Unit (RSU) density, frequent non-line-of-sight conditions, and highly dynamic vehicular topologies often prevent many Connected and Automated Ve…
We give explicit counterexamples to two rearrangeability conjectures for shuffle-type networks. First, for every $N\ge2$ we construct a simple $N$-regular ordered two-stage graph $L_N$ with $F(L_N)=2$ and $R(L_N)\ge N$, refuting the graph-theoretic Beneš inequality $R(L)\le2F(L)$…
Rural areas exhibit low population density and highly variable connectivity needs shaped by both household usage and field operations such as planting, harvesting, and mining. These field activities often occur in isolated locations requiring temporary connectivity, whereas rural…