arXiv (All)
open access
Empowering Hybrid Attention Models on NPUs
Hybrid attention models have emerged as a crucial architecture for Large Language Models (LLMs) (e.g., the Qwen3.5 and Kimi series). Their memory and computational efficiency make them highly attractive for on-device inference, forming a promising synergy with edge Neural Processing Units (NPUs). Ho
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Multilinguality in Hybrid Attention LLMs#1129969
- Beyond Pairwise Attention: Higher-Order Modular Attention for Efficient Sequence Learning#1076829
- TinyCeNN-LM: Quality-Gated Conversion of Pretrained Attention with CeNN-Inspired Cellular-Recurrent Layers#1035107
- Neural Language Models’ sensitivity to long-distance dependencies and their island constraints is not universal: a comparison between English and Dutch humans and models#331597
- Representation Dynamics Reveal Semantic Saliency and Similarity for Visual Token Pruning in MLLMs#1132117
Record · ID 1116445
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive