Lookahead Long-Document Consistency: Predicting Whether a Proposed Passage Conflicts with Definitions or Claims Elsewhere in a Long Document

Long-document generation presents a consistency problem that cannot be reduced to the grammatical correctness or local coherence of individual passages. A proposed passage may be logically plausible in isolation while contradicting a definition, assumption, factual statement, methodological decision, or conclusion introduced elsewhere in the document. Existing approaches to factual consistency, natural language inference, document-level coherence, and retrieval-augmented generation provide relevant foundations for detecting such inconsistencies. However, detecting an existing contradiction and predicting whether a proposed passage will introduce a contradiction before incorporating it into a document are distinct decision problems. This paper proposes Lookahead Long-Document Consistency (LLDC), a conceptual framework for evaluating proposed passages against the evolving global state of a long document. Rather than relying exclusively on nearby text or checking consistency after a document has been completed, LLDC retrieves relevant definitions and claims, represents their logical relationships, evaluates candidate passages for contradiction and compatibility, and estimates their downstream effects on document consistency. The framework includes claim extraction, document-state representation, contradiction retrieval, relation-aware passage evaluation, uncertainty estimation, and revision-oriented decision-making. A mathematical formulation, training objectives, inference algorithm, and experimental protocol are presented. The proposed method is intended to support long-form scientific writing, technical documentation, reports, policy documents, and other applications in which consistency across distant sections is important. No experimental results are claimed in this paper. The contribution is presented as a testable research hypothesis whose novelty and effectiveness must be established through systematic comparisons with natural language inference, retrieval-augmented consistency checking, and existing long-context language models.