Predictive Computation Verification: Assessing the Validity, Compatibility, and Reusability of Precomputed Results under Future Computational Conditions

The increasing adoption of predictive computing in generative artificial intelligence (AI) introduces a new computational opportunity: anticipating future workloads and preparing computational results before corresponding requests arrive. By performing selected computations in advance, AI systems may reduce repeated inference expenditure, improve resource utilization, and potentially provide faster responses when anticipated requests materialize. However, the usefulness of predictive computation depends on an important condition that has received comparatively less attention: a result that was valid when originally computed is not necessarily valid, compatible, or reusable when a future request actually arrives. Changes in input data, model versions, parameters, software dependencies, execution environments, contextual requirements, and external information may invalidate previously prepared computational results. This paper introduces Predictive Computation Verification (PCV), a conceptual and systems-oriented framework for assessing whether precomputed results remain suitable for reuse under future computational conditions. The proposed framework integrates seven interconnected functions: (1) computational provenance tracking, (2) dependency and execution-state identification, (3) future computational condition prediction, (4) temporal and contextual validity assessment, (5) computational compatibility verification, (6) confidence-aware reuse authorization, and (7) selective recalculation and result maintenance. Rather than treating a precomputed result as permanently reusable once generated, PCV models its validity as a time-dependent and task-dependent property. A probabilistic validity formulation is developed to estimate the likelihood that a stored result remains suitable for a future computational task, while explicit compatibility constraints prevent probabilistic confidence from replacing mandatory correctness checks. The framework further distinguishes between complete result reuse, partial computational reuse, conditional reuse, and selective recalculation. An integrated optimization formulation is proposed to balance verification expenditure, expected reuse benefits, recomputation costs, invalid-result risks, and service-level requirements. In particular, the framework investigates whether inexpensive verification procedures can replace unnecessary full recomputation while maintaining appropriate reliability guarantees. This may produce faster response delivery and lower computational expenditure when the verification and retrieval costs are sufficiently smaller than the costs of executing the original workload. The research also establishes an experimental methodology for evaluating validity prediction, compatibility detection, false reuse rates, verification overhead, computational savings, and response latency. The proposed framework does not assume that precomputed results remain reusable under arbitrary future conditions; its effectiveness depends on the completeness of provenance information, the accuracy of dependency tracking, the detectability of relevant changes, and the reliability of verification procedures. By treating future-condition validity as an independent research problem, this paper extends predictive computing from the anticipation and preparation of computational workloads toward the trustworthy verification and reuse of their previously prepared results.