MeVe: A Modular System for Memory Verification and Effective Context Control in Language Models

Retrieval-Augmented Generation (RAG) systems typically face constraints because of their inherent mechanism: a simple top-k semantic search [1]. The approach often leads to the incorporation of irrelevant or redundant information in the context, degrading performance and efficiency [10][11]. This paper presents MeVe, a novel modular architecture intended for Memory Verification and smart context composition. MeVe rethinks the RAG paradigm by proposing a five-phase modular design that distinctly breaks down the retrieval and context composition process into distinct, auditable, and independently tunable phases: initial retrieval, relevance verification, fallback retrieval, context prioritization, and token budgeting. This architecture enables fine-grained control of what knowledge is made available to an LLM, enabling task-dependent filtering and adaptation. We release a reference implementation of MeVe as a proof of concept and evaluate its performance on knowledge-heavy QA tasks spanning biomedical, legal, Wikipedia, and HotpotQA domains [22] [26] [27]. Our results demonstrate that by actively verifying and refining contextual information, MeVe achieves substantial gains in both accuracy and efficiency: Exact Match improves by around 40%, F1 by 34%, and usable context by over 40% while processing time rises only barely ( 12%). These improvements translate into large reductions in contextual redundancy (up to 57% on Wikipedia and 75% on HotpotQA) and consistent accuracy gains across all evaluated domains. Component ablation further highlights the importance of Hybrid Fallback, Coherence Filtering and Adaptive Verification in sustaining these benefits [25]. This work provides a framework for more scalable and reliable LLM applications. By refining and distilling contextual information, MeVe offers a path toward better grounding and more accurate factual support [16].