arXiv AI
MLIPS: Multi-Layer Indexer Pool Sharing
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arXiv Machine Learning
Jun 4
SparDA: Sparse Decoupled Attention for Efficient Long-Context LLM Inference
arXiv:2606. 04511v1 Announce Type: cross Abstract: Sparse attention reduces compute and memory bandwidth for long-context LLM inference.
arXiv Machine Learning
Jun 5
You Only Index Once: Cross-Layer Sparse Attention with Shared Routing
arXiv:2606. 06467v1 Announce Type: cross Abstract: Long-context inference in modern LLMs is increasingly constrained by decoding efficiency, especially in reasoning-heavy settings where models generate long intermediate chains of thought.
arXiv AI
Jun 12
MiniMax Sparse Attention
arXiv:2606. 13392v1 Announce Type: new Abstract: Ultra-long-context capability is becoming indispensable for frontier LLMs: agentic workflows, repository-scale code reasoning, and persistent memory all require the model to jointly attend over hundreds of thousands to millions of tokens, yet the quadratic cost of softmax attention makes this untenable at deployment scale.
arXiv AI
Jun 9
FlashMemory-DeepSeek-V4: Lightning Index Ultra-Long Context via Lookahead Sparse Attention
arXiv:2606. 09079v1 Announce Type: cross Abstract: Conventional LLMs keep the full KV cache loaded during decoding, causing a severe GPU memory bottleneck for ultra-long context serving.
arXiv Machine Learning
Oct 9
QUILT: Rethinking Sparse-Attention Prefill through Shared Query Execution
The paper introduces QUILT, a new sparse‑attention execution mechanism that jointly processes neighboring queries to reuse shared key‑value (KV) entries, thereby reducing redundant memory traffic and computation. QUILT employs Shift‑and‑Compare Set Decomposition (SCSD) to convert irregular set operations into regular data‑parallel primitives and pipelines this with attention computation to hide overhead. The approach includes cascaded sharing across multiple granularities and a tile‑aware strategy that balances sharing with hardware utilization, achieving up to 55.1% lower kernel latency and 36.8% faster time‑to‑first‑token on LongBench benchmarks with negligible accuracy loss.
arXiv Machine Learning
Oct 9
PageWeaver: KV-Guided Query Unions for Sparse Attention
PageWeaver introduces a KV‑guided query union strategy for sparse attention, assembling query groups based on selected‑page affinity while preserving each query’s support and output ownership. The design employs a bounded GPU search to generate query IDs and an ID‑aware two‑CTA kernel that consumes them without materializing reordered Q tensors or cross‑page partial outputs. Experiments on the H200 Union8 with FP8 KV show a 1.70× speedup over FlashInfer, with additional latency reductions of 3.26–7.66% on selected captures and modest throughput gains for whole‑model prefill.