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News/Krause Synchronization Transformers
arxiv
PublishedMay 16, 2026 at 4:00 AM
▲bullish

Krause Synchronization Transformers

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2602.11534v3 Announce Type: replace-cross Abstract: Self-attention in Transformers relies on globally normalized softmax weights, causing all tokens to compete for influence at every layer. When composed across depth, this interaction pattern induces strong synchronization dynamics that favor

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  • 01meta-llama logo
    Llama-3.1-70B
    meta-llama/Llama-3.1-70B
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Mentioned models
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  • 01
    Llama-3.1-70B
    meta-llama/Llama-3.1-70B
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    Qwen
  • 03
    ViT
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arxiv
Read original ↗All from arxiv →
Tags
04
#transformers#attention#efficiency#scalability

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Mentioned models
03
  • 01
    Llama-3.1-70B
    meta-llama/Llama-3.1-70B
  • 02
    Qwen
  • 03
    ViT
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#transformers#attention#efficiency#scalability

Related coverage

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arxivOptimal Hydra: Model Heterogeneous Federated Learning for LLMs using Data-Informed Hydra-Nets19harxivJudging the Judges: Confidence and Disagreement in Rubrics as Rewards19harxivContent associations with answer selection and engagement in supply chain community question answering19harxivORACLE-SWE: Quantifying the Contribution of Critical Information Signals on SWE Agents19h
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