Deep Research (DR) systems iteratively plan, retrieve evidence, and synthesize long-form reports in response to complex, open-ended queries. Existing approaches suffer from context overload, role entanglement, and error propagation over long horizons, while the most capable DR systems remain locked behind massive proprietary models. We present DeepDiver-DR (DD-DR), a multi-agent framework that decomposes the DR process into three specialized roles—planner, information seeker, and writer—coordinated through a persistent, file-based shared workspace. Each role is further equipped with fine-grained, DR-specific tools. To support rigorous evaluation, we introduce RubricWrite-Bench (RW-Bench), a benchmark of 100 rubric-based writing queries with a two-phase assessment of global report quality and point-level rubric fulfillment. Experiments show that DD-DR (30B) establishes a new state of the art among models at the same parameter scale on the DeepResearch Bench, and achieves performance competitive with leading proprietary DR systems on RW-Bench. Ablations further confirm the contribution of the architectural components.

“Joint entity recognition and relation extraction are important tasks in natural language process-ing. While some previous work has recognized the importance of relation information in jointextraction, excessively focusing on relation information without utilizing entity information maylead to information loss and affect the identification of relation tuples. Additionally, ignoring theutilization of original information may result in the loss of hierarchical and semantic information,further reducing the richness of information.To address these issues, we propose a bidirectionalinformation updating mechanism that integrates entity and relation information, iteratively fus-ing fine-grained information about entities and relations. We introduce a long-term memory gatemechanism to update and utilize original information using feature information, thereby enhanc-ing the model’s ability for entity recognition and relation extraction. We evaluated our approachon two Chinese datasets and achieved state-of-the-art results.”