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Joint Subspace Sparse Attention for Efficient Long-Context Decoding
Jiwon Song, Yoongon Kim, Jae-Joon Kim
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 fi…
Rotation-Aligned Key Channel Pruning for Efficient Vision-Language Model Inference
Beomseok Kang, Dongwon Jo, Jiwon Song +2
Vision-Language Models suffer severe KV cache pressure at inference, as a single image often encodes into thousands of tokens. Most existing methods exploit token sparsity through…
CompactAttention: Accelerating Chunked Prefill with Block-Union KV Selection
Jiwon Song, Dongwon Jo, Beomseok Kang +1
Chunked prefill has become a widely adopted serving strategy for long-context large language models, but efficient attention computation in this regime remains challenging. Existin…
RelayGen: Intra-Generation Model Switching for Efficient Reasoning
Jiwon Song, Yoongon Kim, Jae-Joon Kim
Large reasoning models (LRMs) achieve strong performance on complex reasoning tasks by generating long, multi-step reasoning trajectories, but inference-time scaling incurs substan…
Token Sparse Attention: Efficient Long-Context Inference with Interleaved Token Selection
Dongwon Jo, Beomseok Kang, Jiwon Song +1
The quadratic complexity of attention remains the central bottleneck in long-context inference for large language models. Prior acceleration methods either sparsify the attention m…