MRG-Router: Pre-Rendering Routing via Capability Probes for Multi-Reference Image Generation

Multi-reference image generation (MRIG) requires preserving visual entities across multiple exemplars while following textual instructions, with applications in personalized advertising, e-commerce display, and creative editing. However, diverse generative models exhibit query-dependent complementarity alongside differences in inference costs, meaning that statically deploying a single model is either expensive or sub-optimal in quality. To address this issue, we study pre-rendering routing, selecting the optimal generator prior to rendering to maximize capability gains without candidate rendering overhead. Distinct from existing routers relying on static model identities or coarse global rankings, we propose MRG-Router to address the multimodal evidence mismatch prior to rendering by representing generators through empirical behavior rather than identity. Specifically, MRG-Router profiles candidate generators on a compact Capability Probe Bank and uses Query-Conditioned Probe Reweighting (QPR) to align probe-level evidence with incoming multimodal queries. For systematic evaluation, we construct MR-RouteBench, a dense benchmark systematically covering twelve representative compositional scenarios with associated compute traces. Evaluations across MR-RouteBench and two external benchmarks demonstrate that under quality-driven routing, MRG-Router outperforms the single strongest fixed generator. Under practical deployment constraints, it preserves visual quality comparable to the best fixed model while reducing generation costs by up to 96.0%, achieving superior quality-cost trade-offs over competing routing baselines. Furthermore, MRG-Router onboards unseen generators without parameter updates simply by appending probe profiles, demonstrating the potential of our routing framework to scale alongside advancements in generative modeling.