Kascade: A Practical Sparse Attention Method for Long-Context LLM Inference

arXiv:2512.16391 · cs.LG, cs.AI, cs.DC · Submitted 2025-12-18 · Read on arXiv

cs.LG, cs.AI, cs.DC

Submitted: 2025-12-18

Updated: 2026-08-31

Code: https://github.com/microsoft/kascade

Terminology

• Decode — 41× in this paper · explained in 2 episodes
• Accuracy — 36× in this paper · explained in 1 episode
• Sparse Attention — 30× in this paper · explained in 5 episodes
• Sparsity — 16× in this paper · explained in 4 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Transformer — 7× in this paper · explained in 2 episodes
• RAG — 4× in this paper · explained in 3 episodes
• F1 Score — 3× in this paper · explained in 3 episodes
• KV cache — 3× in this paper · explained in 3 episodes
• Attention Mechanism — 2× in this paper · explained in 3 episodes
• Grouped Query Attention — 2× in this paper
• Adaptation — 1× in this paper · explained in 1 episode
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• gpt-oss-120b & gpt-oss-20b Model Card
• GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
• Longformer: The Long-Document Transformer
• Leveraging redundancy in attention with Reuse Transformers
• SeerAttention: Learning Intrinsic Sparse Attention in Your LLMs
• SeerAttention-R: Sparse Attention Adaptation for Long Reasoning
• The Llama 3 Herd of Models
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• What Matters in Transformers? Not All Attention is Needed
• TurboRAG: Accelerating Retrieval-Augmented Generation with Precomputed KV Caches for Chunked Text
• Qwen3 Technical Report
• Less Is More: Fast and Accurate Reasoning with Cross-Head Unified Sparse Attention
• LServe: Efficient Long-sequence LLM Serving with Unified Sparse Attention
• Quest: Query-Aware Sparsity for Efficient Long-Context LLM Inference
• LazyFormer: Self Attention with Lazy Update
• Gemma: Open Models Based on Gemini Research and Technology
• TileLang: A Composable Tiled Programming Model for AI Systems
• Efficient Streaming Language Models with Attention Sinks
• Sharing Attention Weights for Fast Transformer

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