# FloWright

The work proposes FloWright, which uses the workflow itself as a harness and a hierarchical, structure-aware reward to localize a workflow's single sparse outcome to individual roles, enabling one role to self-evolve and two or more roles to co-evolve, together with DataWright, which hardens existing single-agent datasets into workflow-level tasks; across document, slide, chart, code, math, and finance tasks, small open models trained with FloWright improve performance by up to 7.41%, with co-evolving (+5.03%) gaining more than optimizing one role alone (+2.83%).

The work proposes FloWright, which uses the workflow itself as a harness and a hierarchical, structure-aware reward to localize a workflow's single sparse outcome to individual roles, enabling one role to self-evolve and two or more roles to co-evolve, together with DataWright, which hardens existing single-agent datasets into workflow-level tasks; across document, slide, chart, code, math, and finance tasks, small open models trained with FloWright improve performance by up to 7.41%, with co-evolving (+5.03%) gaining more than optimizing one role alone (+2.83%).