Shifting Mechanisms: How Positional Encoding Choice Shapes In-Context Retrieval

Abstract:Language models increasingly use architectures that vary attention span and positional encoding across layers, such as applying RoPE with sliding-window attention and NoPE with global attention (SWA NoPE). However, how these choices shape in-context retrieval remains unclear. To study this question, we take a mechanistic view, tracing how positional encoding (PE) choice shapes the internal mechanisms models use for in-context retrieval. Across 22 open-weight models spanning eight families, we find that standard RoPE models rely primarily on positional retrieval, while PE hybrids shift toward semantic retrieval. We further show on a controlled pre-training ablation that confining positional encoding to local layers produces this semantic shift, degrading representations of positional information. Finally, we show that the reported long-context gains of PE hybrids mask a retrieval trade-off: SWA NoPE improves over RoPE on multiple-target retrieval and QA, but degrades when distinguishing competing keys. We show that these behavioral differences better track the mechanism shift from positional toward semantic mechanisms than a uniform improvement in long-context retrieval.

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2609.38530 [cs.CL]

(or arXiv:2609.38530v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.38530 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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