General Predictive Computing: A Unified Decision-Theoretic Framework for Anticipating, Preparing, and Reusing Future Computational Tasks

Modern computational systems predominantly operate under a reactive execution paradigm in which computational tasks are executed after their corresponding requests, instructions, or triggering events become available. Although existing technologies have introduced workload forecasting, caching, speculative execution, prefetching, materialized computation, and predictive resource allocation, these approaches generally address particular computational environments or specific forms of advance execution. A broader theoretical question remains: how should an intelligent computational system determine which future computations are worth performing before their actual demand materializes, how much of each computation should be completed in advance, and how previously prepared computational results can be reused across multiple future tasks? This paper introduces General Predictive Computing (GPC), a unified decision-theoretic framework for anticipating future computational tasks and selectively preparing computational results before actual execution requirements arise. Unlike approaches that focus exclusively on predicting computational demand, GPC investigates the relationship between future task probability, expected computational benefit, preparation expenditure, uncertainty, computational dependencies, partial execution, and cross-task reuse. The proposed framework introduces four principal preparation decisions: (1) complete precomputation, in which an anticipated task is executed before its request arrives; (2) partial computation preparation, in which selected intermediate operations or computational components are completed in advance; (3) shared computational preparation, in which intermediate results are prepared for potential reuse across multiple future tasks; and (4) deferred execution, in which computation is intentionally postponed because its expected preparation benefit is insufficient. The architecture integrates future computational task prediction, probabilistic demand estimation, computational dependency analysis, expected-value-based preparation decisions, uncertainty-aware execution control, partial result preparation, cross-task computational reuse, and adaptive task evaluation. A unified mathematical formulation is developed to maximize the expected net benefit of predictive computation while accounting for preparation costs, future reuse probability, task dependencies, resource constraints, computational deadlines, result validity, and prediction errors. Particular attention is given to the distinction between predicting that a computation may be required and establishing that performing it in advance is economically or operationally justified. The framework further investigates whether computational systems can move beyond preparing isolated future tasks toward constructing reusable computational foundations that support multiple possible future execution paths. This capability may be particularly relevant to agentic AI, database operations, machine learning pipelines, scientific simulations, distributed computing, and other environments characterized by recurring or partially predictable computational structures. An experimental methodology is proposed to evaluate predictive task accuracy, preparation efficiency, partial computation benefits, cross-task reuse, computational overhead, and total system performance. The proposed framework does not assume that predictive execution is universally advantageous; its effectiveness depends on task predictability, computational reuse opportunities, preparation expenditure, uncertainty, and the validity of prepared results. By establishing a general decision-theoretic foundation for future computational task preparation, this research investigates a broader computational paradigm in which systems do not merely execute tasks after receiving requests, but anticipate possible future computational requirements and selectively prepare the computations that are most likely to provide measurable benefits.