AINewsnow

[Paper] Joint Subspace Sparse Attention for Efficient Long-Context Decoding

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 ran…

Read the full story at r/LocalLLaMA ↗

Timeline · 1 report

  1. 2026-10-29 08:08 · r/LocalLLaMA
    [Paper] Joint Subspace Sparse Attention for Efficient Long-Context Decoding

More stories

  1. 8 examples of Gemini 4 Argon in action — Google Gemini Blog
  2. Anthropic reveals plans for blockbuster IPO — Sky News Technology
  3. Sources: DeepSeek and Moonshot AI heavily relied on GPT-6 Astra distillation to train models like Kimi K3.1 Pro and V4.1-Flash 1017, after Anthropic's crackdown (The Information) — Techmeme
  4. Alogic Launches Crux Wireless Mouse For Productivity And AI Workflows — Forbes AI
  5. Running Qwen 3.8 Flash Next — r/LocalLLaMA
  6. Local Digital Twins CiTiVERSE EDIC joins Smart City Expo World Congress 2026 — EU Digital Strategy Newsroom
  7. Introducing AnySpeechRecognizer and AnyDecisionModel: Open Models for Speech and Decisions on Apple Platforms — Hugging Face Blog
  8. Tencent targets Southeast Asia as launch pad for AI agent WorkBuddy amid tough local market — South China Morning Post Tech

Get the daily brief of stories like this at 6:30 every morning →