Irreversibility-Aware Computation: Accounting for the Cost of Undoing Operations and Recovering from Their Consequences

Computational systems frequently select operations according to their expected immediate benefits, execution costs, or predicted effects on future system states. However, two operations with similar immediate utility may differ substantially in their reversibility. An operation that is inexpensive to execute may create persistent state changes, invalidate previous computations, consume nonrenewable resources, or require substantial effort to undo. Conventional cost-aware decision mechanisms do not necessarily represent these consequences as explicit components of action selection. This paper proposes Irreversibility-Aware Computation (IAC), a conceptual framework for evaluating computational operations according to their expected execution cost, reversibility, recovery cost, and downstream consequences. Rather than treating reversibility as a binary property, IAC models the degree to which an operation can be undone, the resources required to restore a desired state, and the residual loss that remains after recovery. The framework represents computational decision-making as a sequential process in which candidate operations are evaluated before execution. Its proposed components include operation reversibility estimation, recovery-path prediction, consequence-aware cost modeling, uncertainty-aware action selection, and adaptive preservation of recovery options. A mathematical formulation, training objectives, decision algorithm, and experimental protocol are presented. The framework is intended to support applications ranging from task scheduling and resource management to database operations, automated agents, and generative computing. No experimental results are claimed in this paper. The proposed contribution is a testable research framework whose novelty, practical value, and effectiveness must be established through systematic comparison with conventional cost-aware planning, rollback mechanisms, checkpointing strategies, and risk-sensitive decision methods.