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How Local Mixing Encodes Relative Position in Global NoPE Attention

Paper recorded by Signals 4 on 2026-09-29 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-29 on arXiv · recorded by Signals 4 on 2026-09-30

Category: cs.AI · 人工智能 · first seen 2026-09-30

Abstract

The attention operation is naively position invariant. However, positional information is fundamental to natural language, and therefore a variety of explicit position encodings have been developed in transformer-based models, such as rotary position encoding (RoPE). Although explicit position encodings have long been assumed to be required, recent methods that interleave local mixing layers, such

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#10 most recent of 460 cs.AI papers we have recorded · ↑ newer: Stochastic World Models for Verifying Vision-Based Neural Feedback Sys · ↓ older: Do LLM Agents Execute the Plans They Declare? From Planning-Mode Decla
Cite this page: How Local Mixing Encodes Relative Position in Global NoPE Attention: the #10 most recent of 460 cs.AI papers we have recorded (as of 2026-09-29). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/how-local-mixing-encodes-relative-position-in-global-nope-attention.html
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