Researchers introduce MoFlow for multi-objective agentic workflows
Researchers at an unnamed institution have proposed MoFlow, a new method for generating agentic workflows that optimize multiple objectives at once. Current approaches typically focus on accuracy alone or a weighted sum of objectives, requiring retraining when priorities shift. MoFlow instead formulates workflow generation as a multi-objective Markov decision process and uses Convex-Hull Monte…
Key points
- MoFlow generates agentic workflows optimizing multiple objectives simultaneously, unlike prior methods that focus on accuracy alone
- Uses Convex-Hull Monte Carlo Tree Search to explore trade-offs and store sets of reachable outcomes per decision node
- Outperforms six baselines in six benchmarks, achieving highest average hypervolume under stringent evaluation conditions
The team evaluated MoFlow against six baselines across six benchmarks covering mathematics, code, and question answering. Since baselines are single-scalar optimizers, direct comparison is challenging. Instead, MoFlow was tested under a setup favoring baselines: they were rerun for each testing preference, which MoFlow had not seen. Even under these conditions, MoFlow achieved the highest average hypervolume, a metric measuring trade-off coverage.
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