AgentCPM-Report: Interleaving Drafting and Deepening for Open-Ended Deep Research

Researchers from the CPM team recently introduced AgentCPM-Report, a novel framework designed to enhance open-ended deep research reports, via a technical paper published on the arXiv preprint server on February 11, 2025. This development addresses the inherent limitations of current large language models, which often struggle with the synthesis of complex, insight-driven analysis for long-form reporting. By moving away from rigid, pre-defined structures, the researchers aim to bridge the gap between simple information retrieval and the high-level reasoning required for professional-grade research documentation. At the heart of the paper is the critique of the traditional "plan-then-write" paradigm that dominates existing AI research systems. In these legacy models, the quality of the final output is inextricably linked to the initial outline; if the starting plan is flawed or narrow, the resulting report fails to capture the necessary depth or breadth of the topic. AgentCPM-Report introduces a more dynamic methodology called "interleaving drafting and deepening," which allows the agent to iteratively refine its understanding and structure as it acquires new data from large-scale information sources. This iterative approach mimics the workflow of human researchers, who often adjust their thesis and structural focus as they uncover new evidence during the investigation phase. By allowing the model to pivot and deepen its inquiry during the drafting process, AgentCPM-Report overcomes the reasoning bottlenecks that typically cause AI systems to produce superficial or repetitive content. This advancement represents a significant step toward autonomous AI agents capable of producing comprehensive, high-quality technical and academic reports with minimal human intervention.