Terminal-Outcome Prediction: Predicting Whether Computational Plans Will Complete Successfully Within Constraints

Computational systems frequently select execution plans according to immediate costs, heuristic estimates, or static scheduling rules. However, a plan that appears locally efficient may eventually fail because of resource exhaustion, deadline violations, dependency conflicts, accumulated queueing delays, intermediate-state constraints, or insufficient computational capacity. This paper proposes Terminal-Outcome Prediction (TOP), a conceptual framework for estimating whether a candidate computational plan will reach a successful terminal state while satisfying predefined execution constraints. Rather than evaluating a plan solely through its immediate cost or predicted completion time, TOP estimates the probability of successful completion, the expected terminal state, the likelihood and severity of constraint violations, and the computational resources required to reach termination. The framework represents computational planning as a sequential decision problem in which candidate plans are evaluated before commitment and may be reassessed as the system state changes. A terminal-outcome predictor estimates plan feasibility and outcome distributions, while a decision mechanism combines predicted success probability, execution utility, failure consequences, prediction uncertainty, and evaluation overhead. The framework supports learned prediction, simulation-based evaluation, and hybrid approaches. This paper presents a mathematical formulation, model-training objectives, an inference algorithm, and an experimental protocol for evaluating TOP in deadline-constrained workflows, resource-limited computation, and dependency-aware execution systems. No experimental results are claimed. The proposed contribution is a testable research framework whose effectiveness and distinctiveness must be established through systematic comparison with existing runtime prediction, workflow scheduling, feasibility estimation, and risk-aware planning methods.