Lookahead Outline Revision: Predicting Full-Document Quality to Select and Revise Candidate Outlines Before Generation

Large language models can generate outlines before drafting long-form documents, but the first outline produced is not necessarily the one most likely to support a coherent, complete, and effective final document. Conventional outline-based generation generally constructs a structure and then expands it into prose, while revision is often performed reactively after the document has been generated. This paper proposes Lookahead Outline Revision (LOR), a conceptual framework for comparing alternative outlines according to their predicted effects on the quality of the complete document before full generation begins. Given a writing task, LOR generates a set of candidate outlines, predicts the downstream consequences of each candidate, identifies structural weaknesses, and selects or revises the outline with the highest estimated utility under a computational budget. The evaluator considers argument completeness, logical coherence, coverage of task requirements, evidence allocation, redundancy, audience suitability, narrative progression, and estimated generation cost. Unlike a method that ranks outlines exclusively by their immediate readability, LOR evaluates an outline as a prospective plan for a future document. The framework combines candidate generation, document-level quality prediction, uncertainty-aware ranking, targeted structural revision, and optional feedback-based learning. A mathematical formulation, optimization objectives, inference algorithm, training strategy, and experimental protocol are presented. The proposed framework is designed to be compatible with existing large language models and applicable to academic papers, analytical reports, technical explanations, proposals, and other structured writing tasks. No experimental results are claimed. The central hypothesis is that explicit prediction and revision of downstream document quality can improve the quality-efficiency trade-off relative to single-outline generation, direct outline ranking, and post-generation revision under comparable computational budgets. The novelty and effectiveness of this formulation must be established through systematic comparison with existing planning, outline-generation, and long-form text-generation methods.