The paper introduces Spectra, an algorithm for scheduling a single AI traffic demand matrix across multiple parallel optical circuit switches while accounting for reconfiguration delays. Spectra decomposes the demand into weighted permutations, assigns them load‑aware across switches, and then equalizes load via controlled permutation splitting. Experiments on GPT and Qwen MoE workloads show Spectra reduces schedule makespan by 1.4×, 2.7×, and 2.4× respectively compared to state‑of‑the‑art baselines, approaching newly derived lower bounds.
arXiv:2511.11628v2 Announce Type: replace-cross Abstract: Modern operating system process schedulers typically enforce a static, ``one-policy-fits-all'' design, which inevitably compromises performan...
arXiv:2607. 05272v1 Announce Type: cross Abstract: Inference serving systems must balance throughput and latency under bursty, heterogeneous workloads, yet the industry standard remains static batching policies that require manual tuning and cannot adapt to shifting traffic.
arXiv:2607. 08782v1 Announce Type: cross Abstract: Expert parallelism has become the prevailing paradigm to serve Mixture-of-Experts (MoE) models.
arXiv:2608. 07964v1 Announce Type: cross Abstract: Load Balancing has emerged as a critical problem in expert-parallel distributed inference of Mixture-of-Experts (MoE) models.
arXiv:2606. 01839v1 Announce Type: cross Abstract: LLM-based agents resolve a user task through many turns of dependent inference and tool calls, producing a workload whose total cost is unknown when the task arrives.
arXiv:2608. 13057v1 Announce Type: cross Abstract: In expert-parallel (EP) MoE serving, every layer synchronizes at the slowest GPU.
Inference serving systems must balance throughput and latency under bursty, heterogeneous workloads, yet the industry standard remains static batching policies that require manual tuning and cannot adapt to shifting traffic. We investigate whether reinforcement learning (RL) can learn adaptive batching and routing policies that outperform these heuristics, training REINFORCE and PPO agents on a discrete-event simulator validated against queuing theory and production traces (Azure Functions, BurstGPT).
arXiv:2606. 19004v1 Announce Type: cross Abstract: Reinforcement learning (RL) post-training of Diffusion Transformers (DiTs) is prohibitively expensive, requiring thousands of high-end GPUs.
Dynamic Expert Quantization (DynaExq) is a runtime-aware mixed-precision serving system designed for single‑GPU Mixture‑of‑Experts (MoE) inference under a hard high‑bandwidth memory (HBM) envelope. It treats the problem as an online, budget‑constrained precision allocation task, keeping the most frequently used experts at higher precision while relegating the rest to low‑precision fallbacks. By estimating expert hotness from router traces and asynchronously promoting or demoting experts, DynaExq maintains a fully materialized expert set during the forward pass, improving accuracy and throughput compared to static post‑training quantization and offloading/prefetch baselines. whyItMatters":"DynaExq enables efficient deployment of large MoE models on memory‑limited GPUs by dynamically allocating precision based on runtime expert usage, thereby reducing memory footprint and latency while boosting accuracy and throughput."
arXiv:2512. 22560v2 Announce Type: replace-cross Abstract: Agentic Reinforcement Learning (RL) trains LLMs through multi-turn interactions with environments, producing workloads that mix compute-bound prefill, bandwidth-bound decoding, CPU-heavy environment execution, and bursty reward evaluation.
arXiv:2606. 11440v1 Announce Type: new Abstract: Existing multi-agent LLM orchestration methods, ranging from brute-force ensembles to learned routers, select models and topologies based on task and model features.
Prefetch‑Steered Routing (PSR) is a deterministic, cache‑aware method for selecting experts in Mixture‑of‑Experts models that limits physical weight copies to a logical cache of at most *n* experts per layer and token. On several large‑scale models (OLMoE‑1B‑7B, Moonlight‑16B‑A3B, Qwen3.6‑35B‑A3B) PSR achieves 2–2.3× faster decoding than exact routing with LRU and outperforms the Cache‑Prior baseline by 25–30% in many benchmarks, while maintaining comparable accuracy. The approach also shows that speculative block‑level expert selection can be faster than autoregressive decoding when GPU memory is constrained.