# DAGRPO

DAGent introduces Evaluate-then-Grow incremental planning, in which an Orchestrator grows the task DAG one batch at a time conditioned on confidence and uncertainty signals from completed nodes, paired with a hierarchical context layer that propagates compact QueryDocs by default while preserving full execution traces for on-demand recall, and with DAGRPO, a DAG-conditioned RL adaptation combining topology-conditioned credit with structural compliance regularization; it surpasses the strongest open-source baseline by 5.3/5.8/2.0 points on BrowseComp-Plus, GAIA and xbench-DeepSearch at the Qwen3-235B-A22B scale, improves over a same-budget outcome-only GRPO baseline by 3.

DAGent introduces Evaluate-then-Grow incremental planning, in which an Orchestrator grows the task DAG one batch at a time conditioned on confidence and uncertainty signals from completed nodes, paired with a hierarchical context layer that propagates compact QueryDocs by default while preserving full execution traces for on-demand recall, and with DAGRPO, a DAG-conditioned RL adaptation combining topology-conditioned credit with structural compliance regularization; it surpasses the strongest open-source baseline by 5.3/5.8/2.0 points on BrowseComp-Plus, GAIA and xbench-DeepSearch at the Qwen3-235B-A22B scale, improves over a same-budget outcome-only GRPO baseline by 3.