arXiv:2509. 10534v3 Announce Type: replace-cross Abstract: The attention mechanism in a Transformer architecture matches key to query based on both content -- the what -- and position in a sequence -- the where.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2511. 17388v3 Announce Type: replace-cross Abstract: Position information is essential for language modeling.
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.
arXiv:2609.38109v1 Announce Type: cross Abstract: The attention operation is naively position invariant. However, positional information is fundamental to natural language, and therefore a variety of...
The paper proposes a content‑based addressing scheme for long‑context models that replaces the growing token counter in Rotary Position Embedding (RoPE) with unit‑level addresses derived from the content of each unit. By dividing the token stream into units, the method preserves local RoPE behavior while allowing new units to be addressed via learned content maps, avoiding positional mismatches when extending context length. Experiments on character‑level Tiny Shakespeare show that a model trained on 256‑character contexts achieves lower perplexity at 4096 characters using this scheme, and a second diagnostic demonstrates retrieval of multiple serialized facts.
arXiv:2607. 19363v1 Announce Type: new Abstract: Rotary Position Embedding (RoPE) is widely adopted in Transformers to encode positional information, yet standard implementations enforce a uniform frequency schedule and scaling across all attention heads.
arXiv:2607. 10134v1 Announce Type: new Abstract: Rotary Positional Encodings (RoPE) are currently the most popular positional encodings used in modern language models.