description

This is an engineered collaborative intelligent system for writing academic manuscripts across various disciplines, covering monographs, textbooks, chapters, policy documents, management manuals, practical guides, and academic practice texts in fields such as natural sciences, engineering technology, humanities and social sciences, business, law, education, and medicine. Instead of directly writing the main text, it follows a seven-stage engineered process: "task scope allocation—table of contents locking—data compilation—logical review—section-by-section writing—section-by-section confirmation—overall chapter finalization." This ensures stable chapter structure, sufficient data support, consistent terminology, and clear and precise expression, delivering robust manuscripts suitable for formal publication and core journal styles. Core Capabilities: • Task allocation across four categories: entire book, individual chapters, pure table of contents, and special tasks (compilation, AI reduction, peer review responses, etc.); • Three-path processing for first-level table of contents: existing user-defined, custom-designed, and editorial board-defined; • Three differentiated solutions for second- and third-level table of contents (standardized basic, logically reinforced, and practically applied) + a composite recommendation mechanism; • LAF-7 seven-dimensional logical review (system integrity, hierarchical symmetry, sentence structure standardization, title conciseness, cross-chapter repetition rate, terminology standardization, and publication suitability); • Matching word count weight with title density to prevent fragmented text due to excessively dense third-level titles; • Compilation and online verification of references (policies, standards, norms, literature, cases, data); • Access-based text writing (failure to pass any access control means rejection of text writing); • Section-by-section writing, section-by-section confirmation, chapter-by-chapter compilation, and final review; Expert-level AI-powered text rewriting: Diagnosis (using generic terms, sentence structure regularity, mechanical connections, concept piling up, missing context, terminology drift) + rewriting (alternating between long and short sentences, using qualifiers, reducing template numbers, strengthening causal chains and logical loops). Subject-specific adaptation: Automatically switches to the corresponding subject's language style based on the user's provided subject background and terminology (e.g., STEM fields emphasize mechanisms and experiments, business fields emphasize strategy and cases, law emphasizes legal provisions and precedents, education emphasizes theory and classroom, medicine emphasizes evidence-based learning and processes, etc.), without imposing a fixed subject framework. Target audience: Authors of academic monographs in various disciplines, textbook editors, members of book series editorial boards, industry practitioners, graduate and doctoral supervisors, and researchers and practitioners undertaking management manuals/compilations of regulations/practical guides.