arXiv (All)
open access
WaveAlign: Cache-Aware Query-Row Scheduling for Sparse Attention in Long-Video Generation
Long-video generation with diffusion transformers (DiTs) produces extremely long token sequences, making attention a dominant inference bottleneck. Dynamic sparse attention reduces computation, but its realized speedup remains limited because irregular query-row execution degrades L2 cache locality
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
- WaveAlign: Cache-Aware Query-Row Scheduling for Sparse Attention in Long-Video Generation#1108706
- DASC: Decay-Aware State Compression for Hybrid Linear-Attention Serving#799590
- Decoupling Logical Masks from GPU Execution for Dynamic Block-Sparse Attention#1061497
- NOSA: Native and Offloadable Sparse Attention#1129124
- OCC4M: Object-Centric 4D Memory for Spatiotemporal Reasoning in Long-Horizon Manipulation#1076454
Record · ID 1129430
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