Trend-Aware Predictive Generative Inference Caching: A Novel Proactive Framework for Forecasting Future User Queries and Precomputing Responses and Computational States in Large Language Models

The increasing deployment of Large Language Models (LLMs) has introduced substantial computational costs associated with repeated inference, particularly when large numbers of users independently submit identical or semantically similar questions. Conventional response caching mechanisms primarily operate reactively, storing previously generated answers after user requests have already occurred. Although semantic caching can reduce redundant generation, existing approaches generally depend on historical requests or previously accumulated response collections rather than systematically anticipating future information demand from emerging external trends. This paper proposes Trend-Aware Predictive Generative Inference Caching (TPGIC), a novel proactive framework designed to anticipate future high-frequency user questions and prepare corresponding computational resources before actual requests arrive. To the best of our knowledge, this study is the first to propose this particular integrated framework combining external trend-driven future query forecasting, LLM-based candidate question generation, probability- and cost-aware response precomputation, and coordinated Response Cache and KV Cache preparation within a unified predictive inference architecture. The central idea is to transform generative AI serving from a predominantly reactive inference paradigm into a predictive computational preparation paradigm. The proposed framework continuously analyzes emerging information signals, including news developments, search trends, social media discussions, and historical request patterns. Based on these signals, an LLM predicts questions that users are likely to submit within a future time window. Selected questions are processed in advance to generate, validate, and store reusable responses. Simultaneously, compatible computational states, including shared prompt-prefix KV caches, may be prepared to reduce redundant inference computation. When an actual request arrives, the system first attempts to identify an appropriate precomputed response. If semantic, contextual, and temporal compatibility requirements are satisfied, the cached answer is returned directly without invoking the complete generation process. Otherwise, the system may reuse compatible computational states or execute normal inference. The paper develops the conceptual architecture, mathematical optimization formulation, predictive scheduling mechanism, cache management strategy, and experimental methodology. It also identifies the technical constraints associated with prediction uncertainty, semantic mismatch, response freshness, and KV cache compatibility. The proposed framework provides a research direction for reducing unnecessary LLM inference expenditure by predicting not only what information users may request, but also which computational tasks should be completed before those requests occur. Its effectiveness and originality as an implemented system remain subject to empirical validation and comprehensive comparison with existing methods.