RoVE: New Attention Mechanism Improves Position Sensitivity in Large Language Models
Researchers have proposed RoVE (Rotary Value Embeddings), a parameter-free modification to the widely used RoPE attention mechanism that extends position-sensitivity to the value pathway in transformer models. Current RoPE-based architectures encode relative position information in attention scores but leave value tokens position-blind, meaning the content a token contributes is the same regardless of its distance from the query. The work demonstrates consistent empirical improvements on language models up to 354M parameters, particularly on tasks requiring long-range information aggregation.
Rotary Position Embeddings (RoPE) are a standard component in modern large language models, encoding relative positional information into attention scores between query and key tokens. However, the value pathway — which determines what information is actually aggregated — remains unaffected by position, a gap that RoVE addresses by applying the same rotary transformation to values simultaneously with keys. The authors show this modification is parameter-free and reframes RoPE attention as a form of attentive convolution, a perspective they argue unifies previously independent formulations across computer vision, robotics, and LLM research. Experiments on GPT-2 models at 124M and 354M parameter scales show consistent gains over standard RoPE on few-shot in-context learning, out-of-distribution perplexity, and long-context retrieval benchmarks. The improvements are most pronounced on tasks that require aggregating information across long distances in the input sequence. The paper was submitted to arXiv on June 9, 2026, and has not yet undergone formal peer review.
What's missing
The study evaluates models only up to 354M parameters (GPT-2 scale), leaving open whether gains persist at the billion-parameter or larger scales typical of deployed LLMs. The paper does not report wall-clock training or inference overhead, which is relevant given that rotating values adds computation. It is also unclear whether RoVE interacts with other positional or architectural modifications common in modern LLMs (e.g., grouped-query attention, sliding window attention). As a preprint, the results have not been independently replicated or peer-reviewed.
What different sources said
- arXiv cs.AICenter
RoVE: Rotary Value Embeddings Attention for Relative Position-dependent Value Pathways
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