Lookahead Instruction Interpretation: Generating Candidate Interpretations of Ambiguous Requests and Predicting Which Best Matches the User's Likely Intent

Natural language instructions frequently admit multiple plausible interpretations because users omit contextual information, rely on implicit assumptions, combine several objectives, or express requests whose operational requirements are not fully specified. Conventional instruction-following systems often interpret a request and proceed directly to execution, potentially committing to an incorrect interpretation before its consequences become apparent. This paper proposes Lookahead Instruction Interpretation (LII), a conceptual framework that generates candidate interpretations of an ambiguous request, predicts the consequences of acting under each interpretation, and selects the interpretation expected to best match the user's likely intent. Instead of treating ambiguity resolution exclusively as a linguistic classification problem, LII formulates it as a prospective decision problem involving interpretation generation, intent prediction, constraint extraction, hypothetical execution, consequence evaluation, and adaptive commitment. Each candidate interpretation is represented by an explicit set of inferred objectives, constraints, assumptions, and required actions. A lookahead evaluator estimates semantic fidelity, task completion probability, constraint satisfaction, expected user satisfaction, execution risk, and the cost of clarification or further computation. An uncertainty-aware decision mechanism then determines whether to execute a candidate interpretation, gather additional context, ask a clarifying question, or defer commitment. The framework distinguishes between ambiguity that can be resolved reliably from context and ambiguity involving consequential choices that require user confirmation. A mathematical formulation, candidate-generation procedure, evaluator-training objective, decision algorithm, and experimental protocol are presented. The proposed approach is designed to complement existing language models and instruction-following systems rather than replace them. No experimental results are claimed. Its effectiveness and novelty must be established through controlled comparisons with direct interpretation, self-consistency, confidence-based selection, and clarification-oriented baselines.