自动化写作
literature review hypothesis generation experimentation manuscript writing
- 文献综述分解
关键词检索 确定关键文献 关键文献追溯参考文献、被引文献和推荐文献 头脑风暴：创新和研究空白 补充文献 确定论文目标 确定综述文献 文献全文分析汇总 研究设计 研究结果 论文架构 论文撰写 审稿 润色
- agent
Literature Review: This module autonomously conducts comprehensive research analysis by simulating human-like interactions with academic databases and journal platforms. Unlike API-dependent systems, it navigates various digital environments to search, access, and manage relevant literature—even overcoming subscription barriers. Proposal Generation: Following literature analysis, this module formulates a comprehensive research proposal articulating a precise problem statement, well-defined objectives, and innovative hypotheses poised to advance the field. It develops detailed methodological frameworks and experimental protocols optimized for both virtual simulations and physical implementation, establishing a clear investigative roadmap.
Experimentation: This module orchestrates the experimental phase of the research process, encompassing
precise planning, resource optimization, and trial execution across both virtual and physical
environments. Equipped with advanced robotics and AI technologies, the system performs physical
manipulations, collects empirical data, and conducts virtual experiments. Furthermore, it dynamically
refines experimental designs through continuous analysis of real-time results and feedback.
Manuscript Preparation: Following experimental completion, this module synthesizes findings into a publication-ready manuscript. It performs comprehensive data analysis, interprets results, and formulates substantive conclusions. The system structures the document according to standard academic conventions—with methodological details, result presentations, and theoretical discussions—while conducting internal quality assessments and engaging with peer review mechanisms to ensure scholarly rigor and publication readiness.
Reflection and Feedback: This module transcends the conventional research workflow by enabling continuous system-wide improvement. It establishes communication channels between functional components for real-time adjustments while integrating external input from human collaborators and simulated peer evaluations. Through systematic analysis of this feedback, the system refines hypotheses, methodologies, and experimental approaches, ensuring research remains responsive to emerging developments and maximizing the ultimate impact and quality of scientific outputs.
- 论文
