arXiv AI By Cutter Dawes, Nick Alonso, Tom Figliolia, Beren Millidge

How Local Mixing Encodes Relative Position in Global NoPE Attention

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arXiv AI
Jun 8

RePo: Language Models with Context Re-Positioning

arXiv:2512. 14391v3 Announce Type: replace-cross Abstract: In-context learning is fundamental to modern Large Language Models (LLMs); however, prevailing architectures impose a rigid and fixed contextual structure by assigning linear or constant positional indices.

By Huayang Li, Tianyu Zhao, Deng Cai, Richard Sproat
arXiv AI
Sep 3

Give it Space! Explicit Disentangling of Positional and Semantic Representations in Encoders

The paper proposes an encoder Transformer that explicitly separates semantic, absolute positional (AP), and relative positional (RP) information, restricting the masked‑language‑modeling objective to the semantic stream. This disentanglement reveals that the AP subspace collapses into a low‑frequency two‑dimensional manifold reflecting document structure, that attention heads specialize into structure‑ and semantic‑oriented groups with RP supporting only the latter, and that standard positional encodings fail to robustly encode macroscopic structure. The approach preserves positional encoding and improves performance on 49 out of 65 linguistic phenomena in the Flash‑Holmes probing benchmark.

By Pierre-Antoine Lequeu, Camille Barboule, Benjamin Piwowarski
arXiv Computation and Language
3d ago

Scaling Parameter and Context in Attention: Native Sparse Attention from Mixture-of-Head

The paper introduces NAMOH, a native sparse attention mechanism that activates only a subset of heads per token, allowing each head to attend to a limited subsequence of tokens. By scaling the number of heads while keeping the active heads per token fixed, the method shortens head histories and reduces key‑value access without increasing overall storage. Experiments demonstrate that NAMOH can outperform fully activated models with the same parameter count and enable more efficient long‑context inference than smaller dense models.

By Zizhuo Fu, Runsheng Wang, Meng Li