SReF: Structure–Resource Factorization for Multi-Agent LLM Orchestration

Multi-agent systems built from heterogeneous large language models decide both what computation to perform and which model should perform it. Coupling these decisions can make changes in the available experts unnecessarily affect an otherwise useful planning policy. We introduce SReF, a Structure-Resource Factorization framework that separates a model-identity-agnostic Graph Planner from a descriptor-conditioned Expert Router. The Planner generates computation graphs and issues Patches, while the Router assigns available experts to reached nodes. To train these distinct decisions, the Planner receives feedback over verified segments along executed dependencies, and the Router learns from model-relative comparisons under matched node contexts. Segment-level verification supports local training feedback and history-preserving correction of pending computation without verifying every node independently. This separation supports resource-local adaptation through the Router while retaining the learned Planner and its ability to Patch pending computation in response to execution feedback. Across four benchmarks, SReF improves Pass@1 over the strongest routing or orchestration baselines by 1.8-4.5 percentage points and over its NoPatch variant by 1.6-4.9 percentage points. Planner-credit experiments compare terminal-only and segment-level Planner credit under the same verification and Patch protocol, and examine the additional training time of finer node-level feedback. Under expert-pool insertion and removal, direct transfer with the Planner retained approaches the performance obtained by Router-only and joint adaptation. Our code for SReF is released at https://anonymous.4open.science/r/SReF-007E