QV May Be Enough: Toward the Essence of Attention in LLMs

arXiv cs.AI / 3/18/2026

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Key Points

The paper derives the QKV mechanism's essence from first principles and POS/syntactic analysis, offering a unified framework to explain the effectiveness of QKV-based architectures such as MQA, GQA, and MLA, and outlining their trade-offs and optimization directions.
It introduces the QV paradigm with empirical evidence and proposes the QV-Ka optimization scheme, which is validated experimentally.
The work provides an interpretable theoretical analysis of QKV, establishing a foundation for the future evolution of large language model architectures.
By connecting linguistic structure to attention mechanics, the paper discusses potential implications for model design, training efficiency, and downstream AI applications.

Abstract

Starting from first principles and a linguistic perspective centered on part-of-speech (POS) and syntactic analysis, this paper explores and derives the underlying essence of the Query-Key-Value (QKV) mechanism within the Transformer architecture. Based on this theoretical foundation, we provide a unified explanatory framework for the efficacy of contemporary architectures, including MQA, GQA, and MLA, while identifying their inherent trade-offs and potential optimization trajectories. We introduce the QV paradigm and provide empirical evidence for its validity. Building upon this, we propose the QV-Ka optimization scheme, which is further substantiated through experimental validation. The interpretable theoretical analysis of the QKV mechanism presented in this work establishes a robust foundation for the future evolution of large language model architectures.