Xuehang Guo

The work proposes InFlowOp, which prices every decision in one label-free cost that weighs how well an agent's competence meets a subtask's demands against how much that agent takes to run, using it before execution to bidirectionally set decomposition granularity and agent assignment and during execution to locate and locally correct faults, and introduces Braid, a benchmark whose tasks require multi-agent coordination, reporting gains up to +11.97% over single-agent baselines across eight domains and six backbones, with +9.64% from in-flow optimization.

The work proposes InFlowOp, which prices every decision in one label-free cost that weighs how well an agent's competence meets a subtask's demands against how much that agent takes to run, using it before execution to bidirectionally set decomposition granularity and agent assignment and during execution to locate and locally correct faults, and introduces Braid, a benchmark whose tasks require multi-agent coordination, reporting gains up to +11.97% over single-agent baselines across eight domains and six backbones, with +9.64% from in-flow optimization.

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%).