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View a PDF of the paper titled Do Transformers Need Three Projections? Systematic Study of QKV Variants, by Ali Kayyam and 2 other authors
View PDF HTML (experimental)Abstract:Transformers have become the standard solution for various AI tasks, with the query, key, and value (QKV) attention formulation playing a central role. However, the individual contribution of these three projections and the impact of omitting some remain poorly understood. We systematically evaluate three projection sharing constraints: a) Q-K=V (shared key-value), b) Q=K-V (shared query-key), and c) Q=K=V (single projection). The last two variants produce symmetric attention maps; to address this, we also explore asymmetric attention via 2D positional encodings. Through experiments spanning synthetic tasks, vision (MNIST, CIFAR, TinyImageNet, anomaly), and language modeling (300M and 1.2B parameter models on 10B tokens), we discovered that our transformers perform on par or occasionally better than the QKV transformer. In language modeling, Q-K=V projection sharing achieves 50% KV cache reduction with only 3.1% perplexity degradation. Crucially, projection sharing is complementary to head sharing (GQA/MQA): combining Q-K=V with GQA-4 yields 87.5% cache reduction, while Q-K=V + MQA achieves 96.9%, enabling practical on-device inference. We show that Q-K=V preserves quality because keys and values can occupy similar representational spaces and attention operates in a low-rank regime, whereas Q=K-V breaks attention directionality. Our results systematically characterize projection sharing as an underexplored instance of weight tying in attention, with direct, quantifiable inference memory benefits, particularly valuable for edge deployment. The code is publicly available at this https URL
Submission history
From: Anusha Madan Gopal [view email][v1] Mon, 1 Jun 2026 20:59:05 UTC (2,017 KB)
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Frequently Asked Questions
Whether Transformers need all three query, key, and value projections, systematically evaluating Q-K=V, Q=K-V, and Q=K=V sharing variants across synthetic tasks, vision, and language modeling.
Transformers with shared projections perform on par or occasionally better than standard QKV. Q-K=V achieves 50% KV cache reduction with only 3.1% perplexity degradation in language modeling.
Q-K=V plus GQA-4 yields 87.5% cache reduction, and Q-K=V plus MQA achieves 96.9%, enabling practical on-device inference.
arXiv paper 2606.04032 by Ali Kayyam and co-authors, submitted June 1, 2026, with code publicly available.
