MoFlow paper proposes multi-objective agentic workflow generation
The paper introduces MoFlow, which frames agentic workflow generation as a multi-objective Markov decision process and uses a Convex-Hull Monte Carlo Tree Search with optimistic set-valued backups so each search node stores a set of reachable trade-offs. A single search approximates the Pareto front and lets MoFlow return workflows for different preference weights without retraining; it outperforms six baselines on six benchmarks (mathematics, code, question answering) by average hypervolume under an evaluation setup that favors the baselines.