Lookahead Sentence Revision: Estimating the Downstream Effects of Alternative Sentence Edits Before Revising the Document

Sentence revision is a fundamental operation in document editing, academic writing, summarization, and long-form text generation. Conventional revision systems typically generate or rank alternative sentences according to local fluency, grammaticality, semantic similarity, or an estimated improvement in writing quality. However, a locally attractive revision may introduce downstream problems, including broken references, redundant explanations, inconsistent terminology, disrupted argumentation, and increased editing requirements in subsequent sentences. This paper proposes Lookahead Sentence Revision (LSR), a conceptual framework that estimates the downstream effects of alternative sentence edits before committing to a revision. Rather than evaluating an edited sentence exclusively in isolation, LSR treats sentence revision as a document-level sequential decision problem. At each editing step, a candidate generator proposes alternative revisions, a lookahead evaluator estimates their consequences for the remaining document, and a decision mechanism selects the revision with the highest predicted utility under a computational budget. The framework considers grammatical quality, semantic preservation, discourse coherence, argument continuity, redundancy, factual consistency, revision propagation, and computational cost. It supports learned outcome prediction, temporary document rollouts, and hybrid evaluation, with optional uncertainty-aware selection and iterative policy improvement. A mathematical formulation, training objectives, inference algorithm, and experimental protocol are presented. The proposed framework is intended to complement existing language models and document-editing systems rather than replace their underlying generation capabilities. No experimental results are claimed. Its effectiveness and novelty must be established through systematic comparisons with local revision, direct quality prediction, document-level evaluation, and planning-based editing methods.