NodeGrad: Attribution-Guided Recursive Self-Improvement of Multi-Agent Workflows

Improving a multi-agent workflow requires identifying where to intervene and whether the resulting gain justifies the cost of discovery. End-to-end feedback offers little guidance, while removing a structurally necessary node can invalidate the workflow. We introduce NodeGrad, an attribution-guided research loop for recursive workflow improvement. Calibrated, interface-preserving interventions distinguish sensitivity to degradation from upgrade potential, and joint probes reveal conditional benefits between nodes. A language-model controller uses these signals to propose edits, while external tests select versioned updates. Attribution is recomputed after accepted edits so that search follows changing workflow bottlenecks. We further evaluate controller updates through fresh research episodes with equal budgets and fixed evaluation rules. On SWE-bench Lite, repeated attribution improved the resolved rate from 66.0% to 82.3%, and the recursive controller reached 86.0%. Independent patch tests showed that attribution helped prioritize promising nodes, while budget and transfer analyses characterized the conditions under which recursive improvement remained effective. NodeGrad thus connects local diagnosis to tested workflow and controller updates with explicit accounting of research and deployment costs.