Lookahead Argument Planning: Predicting Argument-Sequence Quality Before Drafting the Full Response

Large language models can produce fluent explanations while still organizing their arguments inefficiently, introducing supporting evidence too late, repeating similar points, overlooking counterarguments, or reaching conclusions that are insufficiently supported by the preceding discussion. These problems suggest that response quality depends not only on which arguments are included, but also on the order in which they are presented and the relationships established among them. This paper proposes Lookahead Argument Planning (LAP), a conceptual framework that predicts the downstream quality of alternative argument sequences before the complete response is drafted. Instead of immediately generating a full explanation, LAP constructs a structured representation of candidate arguments, generates alternative argument sequences, estimates their expected final explanatory quality, and selects a promising sequence under a computational budget. The evaluator considers relevance, logical validity, evidential support, completeness, coherence, redundancy, counterargument coverage, audience suitability, and computational cost. The framework further incorporates uncertainty-aware ranking, dependency constraints, partial-order planning, receding-horizon replanning, and optional preference-based learning. A mathematical formulation, model architecture, training objectives, inference algorithm, and experimental protocol are presented. The proposed method is designed to complement existing language models and can be evaluated in explanatory question answering, analytical writing, scientific argumentation, decision support, and long-form response generation. Prior research has already investigated content planning, argument ordering, and planning before text realization; therefore, the contribution of LAP is framed as a specific, testable combination of downstream argument-sequence evaluation and adaptive pre-drafting selection, rather than a claim that argument planning itself is new. No experimental results are claimed. Its effectiveness, computational efficiency, and distinctiveness must be established through systematic comparison with existing planning-based generation methods.