SPIN Unprocessed October 15, 2026 ai_technology research
Joint Subspace Sparse Attention for Efficient Long-Context Decoding
View original on arxiv.orgOverview
arXiv:2610.19026v1 Announce Type: new Abstract: Sparse attention reduces the KV-cache traffic that dominates long-context LLM decoding, but practical methods largely operate at coarse granularity. Token-level selection offers finer control, yet presents two challenges: low-dimensional scoring may lose query--key structure needed for accurate ranking, while gathering scattered selected tokens can offset the resulting latency benefit. We introduce Joint Subspace Sparse Attention (JSSA) to address
SpinGraph analysis pending — check back after processing.
Ask AI about this story
Opens with the SpinGraph .md URL and structured context — one click, prompt included.
More from arXiv Computation and Language
View all →- Copy-Paste Language Models
- Do Syntax Heads Influence Model Behavior?
- Titan-Llama: Retrofitting Pre-trained LLMs with External Neural Memory Modules
- DUALVIEW: Leveraging Broad and Output-Specific Evidence for LLM-based Multi-Output Text Regression
- Weighted Ensemble Sampling for Rare-Event Estimation in Language Models
- NEPSEE: A Double-Annotated Benchmark for Nepali Sentiment Analysis
Markdown (.md) · JSON-LD schema (.json) · Machine-readable for AI & GEO