Adaptive Context Filtering for Efficient Vision-Language Model Inference

Abstract

Vision-language models have become a central infrastructure for multimodal artificial intelligence, supporting applications that range from visual question answering and document understanding to robotics, accessibility tools, and scientific discovery. Despite their capabilities, these models remain expensive to run because they process large numbers of visual tokens alongside linguistic context, often invoking complex cross-modal attention and repeated memory transfers during inference. This paper examines adaptive context filtering as a system-level strategy for improving the efficiency of vision-language model inference without treating efficiency as a purely local optimization problem. Adaptive context filtering dynamically selects, compresses, reorders, or defers visual and textual context according to the query, the deployment environment, and the operational risk of the task. The paper argues that such filtering must be understood through architecture, infrastructure, governance, sustainability, fairness, and robustness rather than only through benchmark accuracy. It analyzes structural trade-offs between latency, memory bandwidth, energy consumption, and evidentiary completeness, and it discusses how filtering can reduce hallucination when guided by attention behavior, yet also introduce new failure modes when relevant evidence is discarded. The paper further considers policy implications for transparency, auditability, procurement, and carbon-aware deployment. Cross-domain case illustrations show that the same filtering mechanism can be beneficial in low-stakes search but dangerous in medical or autonomous settings unless uncertainty, fallback, and human oversight are designed into the system. The conclusion proposes that adaptive context filtering should be governed as a socio-technical capability, with evaluation that includes fairness, robustness, environmental cost, and accountability.