AdaRoPE: Not All Attention Heads Should Rotate and Scale Equally
Using simplified retrieval tasks and length generalization scenarios, we show -- both empirically and theoretically -- that heads with different functional roles require distinct frequency ranges and attention scaling factors to operate effectively. Ignoring this structure leads to suboptimal utilization of embedding dimensions and degraded performance, particularly under long-context settings. To address these limitations, we propose AdaRoPE, which equips each attention head with learnable rotation frequencies and attention scaling factors.
- ▪Using simplified retrieval tasks and length generalization scenarios, we show -- both empirically and theoretically -- that heads with different functional roles require distinct frequency ranges and attention scaling factors to operate eff
- ▪Ignoring this structure leads to suboptimal utilization of embedding dimensions and degraded performance, particularly under long-context settings.
- ▪To address these limitations, we propose AdaRoPE, which equips each attention head with learnable rotation frequencies and attention scaling factors.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2607.19363 |
| Publication time | Thu, 23 Jul 2026 00:00:00 -0400 |
| Retrieval time | 2026-07-23T04:57:27.408Z |
| Last seen | 2026-07-23T04:57:27.408Z |
| Headline source | Publisher (no WeSearch rewrite) |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | HmRE2-eWPqZu |
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| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2607.19363 (cs) [Submitted on 5 Jun 2026] Title:AdaRoPE: Not All Attention Heads Should Rotate and Scale Equally Authors:Shaowen Wang, Yuke Zheng, Tansheng Zhu, Shuang Chen, Shaofan Liu, Suncong Zheng, Jian Li View a PDF of the paper titled AdaRoPE: Not All Attention Heads Should Rotate and Scale Equally, by Shaowen Wang and 6 other authors View PDF HTML (experimental) 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.
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