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FloWright proposes a hierarchical, structure-aware reward paradigm enabling LLM agents in multi-agent workflows to self-evolve and co-evolve without extra models or labels, while DataWright adaptively hardens datasets into workflow-level tasks. Small open models trained with FloWright gain up to +7.41% performance, with co-evolving multiple roles (+5.03%) outperforming optimizing a single role (+2.83%).