Lookahead Redundancy Removal: Predicting the Downstream Effects of Sentence Deletion to Preserve Meaning and Improve Document Efficiency

Redundancy removal is an important component of document editing, summarization, information compression, and natural language generation. Conventional approaches identify repeated words, overlapping sentences, or semantically similar passages and remove or merge content according to local similarity scores or predefined thresholds. However, semantic similarity does not necessarily imply redundancy: two sentences may express related information while serving different argumentative, explanatory, evidential, or discourse functions. Conversely, a sentence with little lexical overlap may repeat information already established elsewhere. Furthermore, deleting a sentence can have downstream consequences for coherence, referential interpretation, causal explanation, evidence coverage, and the interpretation of subsequent paragraphs. This paper proposes Lookahead Redundancy Removal (LRR), a framework that evaluates sentence deletion according to its predicted effects on the complete document rather than relying exclusively on local redundancy detection. At each editing step, LRR constructs candidate operations, including deletion, retention, merging, and rewriting; predicts the resulting document's redundancy, semantic coverage, coherence, evidential adequacy, readability, and length; and selects an operation according to a constrained utility function. The framework combines sentence-level semantic analysis, document-level information coverage, counterfactual document evaluation, uncertainty-aware decision-making, and optional feedback-based policy learning. A mathematical formulation, optimization objective, training strategy, inference algorithm, and experimental protocol are presented. The proposed method is intended to support long-form documents, academic writing, technical explanations, reports, and other text containing repeated or partially overlapping information. No experimental results are claimed in this paper. The effectiveness and novelty of LRR remain hypotheses that must be established through controlled comparisons with existing redundancy detection, semantic deduplication, document compression, and text-editing methods.