The Residual-Load Rebound Amplification Framework (RLR-AF): A Domain-General Conditional Theory of Residual-State Persistence, Temporary Output Attenuation, Systemic Reconfiguration, and Amplified Problem Recurrence

AbstractThis manuscript presents the Residual-Load Rebound Amplification Framework (RLR-AF), a proposed domain-general framework for analyzing conditions under which temporary relief from a problem may be followed by a recurrence that exceeds a comparable reference level.The framework is organized around a central distinction between observable problem attenuation and sufficient modification of the condition that generates or maintains the problem. It proposes that when an intervention reduces the observable output without adequately resolving the relevant generative condition, residual problem-related states may persist. If the intervention or subsequent systemic changes also modify the conditions governing future responses, the residual state may be expressed more strongly when the relevant constraint, control process, or suppressive influence no longer operates in the same way.The framework does not assume that residual load necessarily produces amplification. Amplified recurrence is a conditional outcome requiring an effective pathway that increases the subsequent expression of the residual state and a net effect that exceeds the system's relevant restorative or control processes.The manuscript introduces the Residual-State Conditional Amplification Principle (RSCAP), the expanded Residual-Load-Compensation-Rebound Mechanism (RLCR+), and the primary case of Emotional-Distress Suppression and Amplified Rebound (EDS-AR). It develops a domain-neutral state-space architecture, operational definitions, conditional propositions, a causal-comparison strategy, candidate amplification pathways, cross-domain mapping rules, counterexamples, and an empirical validation roadmap.The framework is a proposed theoretical contribution rather than an established universal law. Its conceptual coherence, mathematical consistency, causal mechanisms, empirical validity, and applicability to domains beyond its primary case must be evaluated separately.