MoFlow: Multi-Objective Agentic Workflow Generation

Researchers proposed MoFlow, a method that generates agentic workflows optimized across multiple objectives at once by formulating workflow generation as a multi-objective Markov decision process and solving it with Convex-Hull Monte Carlo Tree Search with optimistic set-valued backups. MoFlow stores a set of reachable trade-offs at each node rather than a single weighted score, so one search approximately covers the Pareto front and returns a workflow for any preference by lookup without retraining. Evaluated against six strong baselines on six benchmarks spanning mathematics, code, and question answering, MoFlow achieved the highest average hypervolume even when baselines were rerun for each testing preference that MoFlow never saw.

arXiv:2609.38294v1 Announce Type: new Abstract: We study the generation of agentic workflows that jointly optimize multiple objectives, such as accuracy, cost, latency, robustness, and consistency. Existing methods for workflow generation typically optimize accuracy alone or a weighted sum of objectives, so each trained generator commits to one fixed trade-off and must be retrained from scratch when preferences change. To alleviate this, we propose MoFlow, which generates workflows optimized across varied preferences. Specifically, MoFlow formulates workflow generation as a multi-objective Markov decision process and solves it by leveraging Convex-Hull Monte Carlo Tree Search with optimistic set-valued backups, where every node stores a set of reachable trade-offs rather than one weighted score. A single search thus approximately covers the Pareto front, from which MoFlow can return a workflow for any preference by lookup without retraining. We evaluate MoFlow against six strong baselines on six benchmarks spanning mathematics, code, and question answering. Since the baselines are single-scalar optimizers by design, an apples-to-apples comparison is difficult. We instead adopt an evaluation setup that favors the baselines, in that they are rerun for each testing preference, which MoFlow never sees. Even under this stringent setup, MoFlow achieves the highest average hypervolume.