CoWindow Attention: Full Causal Coverage Is a Collective Property

arXiv:2609.32704 · cs.AI · Submitted 2026-09-26 · Read on arXiv

cs.AI

Submitted: 2026-09-26

Updated: 2026-09-26

Code: https://github.com/huggingface/open-r1

Terminology

• Benchmark — 27× in this paper · explained in 2 episodes
• Sparse Attention — 13× in this paper · explained in 5 episodes
• Perplexity — 12× in this paper · explained in 3 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Scaling Laws — 8× in this paper · explained in 4 episodes
• Transformer — 8× in this paper · explained in 2 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Attention Mechanism — 5× in this paper · explained in 3 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Sparsity — 3× in this paper · explained in 4 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• KV cache — 2× in this paper · explained in 3 episodes
• Quantization — 2× in this paper · explained in 12 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes
• Verifier — 1× in this paper · explained in 2 episodes

Sources

• GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
• Why do LLMs attend to the first token?
• Longformer: The Long-Document Transformer
• Generating Long Sequences with Sparse Transformers
• Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
• Training Verifiers to Solve Math Word Problems
• DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models
• HashAttention: Semantic Sparsity for Faster Inference
• Flex Attention: A Programming Model for Generating Optimized Attention Kernels
• Sliding Window Attention Training for Efficient Large Language Models
• Attention Sink Forges Native MoE in Attention Layers: Sink-Aware Training to Address Head Collapse
• SeerAttention: Learning Intrinsic Sparse Attention in Your LLMs
• When Attention Sink Emerges in Language Models: An Empirical View
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• Active-Dormant Attention Heads: Mechanistically Demystifying Extreme-Token Phenomena in LLMs
• Measuring Mathematical Problem Solving With the MATH Dataset
• RULER: What's the Real Context Size of Your Long-Context Language Models?
• NOSA: Native and Offloadable Sparse Attention
• Scaling Laws for Neural Language Models
• FlexPrefill: A Context-Aware Sparse Attention Mechanism for Efficient Long-Sequence Inference

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