Accelerating Heterogeneous Agent Collaboration in Dynamic Edge Networks

Abstract

Deploying large language models (LLMs) at the network edge is hindered by their enormous cost, yet the reasoning quality they provide remains indispensable. Heterogeneous collaboration between edge small models and a server LLM has emerged as a promising direction, but existing methods fail under the dynamic conditions of multi-user contention, autoregressive generation, and time-varying resources. This paper puts forward a process reward model (PRM)-aided two-stage decoupled acceleration (PRADA) framework, which is built on a fundamental change of perspective: instead of querying a PRM online, which cripples multi-user systems with prohibitive latency, we use the PRM solely as an offline teacher. Its reasoning-quality intuition is fully distilled into a lightweight policy that screen each step locally, without any context upload, while a Lagrangian scheduler at the server resolves resource contention through a threshold-structured policy. Across diverse reasoning benchmarks, PRADA retains the vast majority of the LLM's accuracy while substantially reducing end-to-end latency. The results further reveal threshold effects for both server parallel capacity and total bandwidth: performance saturates beyond critical resource levels, after which the system bottleneck shifts from queuing to computation or from communication to contention. These structural findings provide actionable guidance for joint provisioning of computation and communication resources without requiring per-benchmark tuning.