From Design to Learning: Evolving Research Writing Harnesses from Expert Papers

Turning unstructured research materials into coherent manuscripts is an important part of AI-assisted research. Existing writing systems rely on predefined agentic workflows and skills shaped by a small number of designers, leaving open whether better writing practices can be discovered through broader writing experience. We introduce EvoWriter, a meta-harness that enables research writing systems to learn from the writing experience embodied in expert papers. EvoWriter operates around a writing harness that manages manuscript planning, drafting, and revision, and iteratively improves its agentic workflows and skills through writing practice. Specifically, it compares generated manuscripts with expert papers to identify potential improvements, translates these findings into reusable workflow and skill updates, and retains updates that improve subsequent writing. We study this evolution process using 100 research writing tasks drawn from distinguished papers across five major AI conferences and eight contribution types, with separate subsets for evolution, update selection, and final evaluation. Through this process, EvoWriter evolves the initial writing harness into WritingHarness, an executable system for generating complete manuscripts from new research materials. Across automatic and independent human evaluations, the WritingHarness outperforms baseline writing systems, producing clearer scientific arguments and better-organized evidence while preserving the supplied results and claim boundaries. These results show that writing experience distributed across expert papers can guide the continued improvement of research writing systems beyond predefined human designs.