Two conversations can end with equally correct answers while requiring unequal user effort. One system may correct its own prior error. Another may succeed only after the user restores an instruction, referent, or evidential standard. Output-focused evaluation can assign these trajectories the same result and overlook who performed the work required for recovery. We propose a framework for measuring this difference through observable repair sequences in conversations between humans and large language models. Extending Pickering and Garrod’s Interactive Alignment Model, the framework combines four concurrent functional loops (Grounding, Continuity, Negotiation, and Epistemic) with a structural classification of who initiates and resolves repair and a temporal hypothesis about expectation stabilization. It generates testable predictions about system self-repair, epistemic signaling, and phase-dependent repair cost. In a pilot analysis of 30 dialogues, two human annotators achieved substantial agreement under an earlier turn-level rubric (Cohen’s κ = .75). The analysis revealed category-boundary problems that motivate the revised sequence-level framework presented here. Its evidential scope is limited to annotation feasibility. The resulting account makes repair burden measurable and specifies the evidence required for future validation.