arXiv Computation and Language
Oct 9

TokenRouter: Efficient Serving System for Token-Level LLM Routing

TokenRouter is a new serving system designed for token‑level routing of large language models (LLMs). It adopts a request‑centric programming model and launches a dedicated subserver for each LLM, using delayed‑batching schedulers whose hyperparameters are derived from a throughput model. Across various routing algorithms, workloads, and model pairs, TokenRouter delivers 2.01‑64.15× higher decoding throughput than existing systems, markedly improving the efficiency of token‑level LLM inference.

By Tianyu Fu, Tengxuan Liu, Ruoxi Wang, Yixin Dong, Yi Ge, Yichen You, Yu Wang
arXiv AI
Oct 27

Multi-Agentic AI for Fairness-Aware and Accelerated Multi-modal Large Model Inference in Real-world Mobile Edge Networks

The paper proposes a Multi-Agentic AI framework to enable fairness‑aware and accelerated inference of large, heterogeneous multi‑modal language models on mobile edge networks. It introduces a Global Planning Agent, a Prompt Scheduling Agent, and multiple on‑node LM Deployment Agents that jointly optimize prompt routing and model deployment using natural language reasoning over telemetry and historical data. Experiments on a city‑wide testbed show a 42.9% reduction in end‑to‑end latency versus a state‑of‑the‑art baseline and an 80.6% reduction versus a full‑activation baseline, while preserving high fairness (Jain's index 0.925).

By Haiyuan Li, Hari Madhukumar, Shuangyi Yan, Yulei Wu, Dimitra Simeonidou