SOME-Flow: Utility-guided Structural Agentic Inference

As agentic reasoning shifts from static workflows toward query-adaptive topologies, reasoning structures can now adapt dynamically to individual queries. Yet structural adaptivity alone does not ensure that the resulting computation achieves favorable accuracy–cost utility, nor does it determine whether further computation remains worthwhile after an initial execution. Efficient structural inference therefore requires two coupled decisions: *how to allocate high-utility structural computation to each query initially*, and *whether additional structural computation is worth its cost after the initial execution*. To address these decisions, we introduce **SOME-Flow** (**S**tructural **O**ptimization, **M**atching, and **E**volution; hereafter **SOME**), which constructs a reusable, utility-shaped structural action space through offline topology discovery and amortizes per-query structural optimization into lightweight cost-aware matching. After initial execution, SOME selectively evolves the active structure according to predicted marginal utility, allocating additional computation only when it is worthwhile. Across five reasoning and code-generation benchmarks, SOME achieves the highest macro-average performance of 82.57%, outperforming the best automated structural baseline by 2.76 percentage points. Notably, initial structural matching alone reaches 81.41%, only 1.16 points below the full method, while reducing relative inference cost by 43.2%; selective post-execution evolution then allocates additional computation to obtain the remaining performance gains. The code is available at https://anonymous.4open.science/r/SOME-EC4B.