1 citations · 1 across the 6 of their papers we have counts for
9 papers
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…
LiteStage: Latency-aware Layer Skipping for Multi-stage Reasoning
Beomseok Kang, Jiwon Song, Jae-Joon Kim
Multi-stage reasoning has emerged as an effective strategy for enhancing the reasoning capability of small language models by decomposing complex problems into sequential sub-stage…
Reasoning Path Compression: Compressing Generation Trajectories for Efficient LLM Reasoning
Jiwon Song, Dongwon Jo, Yulhwa Kim +1
Recent reasoning-focused language models achieve high accuracy by generating lengthy intermediate reasoning paths before producing final answers. While this approach is effective i…
FastKV: Decoupling of Context Reduction and KV Cache Compression for Prefill-Decoding Acceleration
Dongwon Jo, Jiwon Song, Yulhwa Kim +1
While large language models (LLMs) excel at handling long-context sequences, they require substantial prefill computation and key-value (KV) cache, which can heavily burden computa…
SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks
Jiwon Song, Kyungseok Oh, Taesu Kim +3
Large language models (LLMs) have proven to be highly effective across various natural language processing tasks. However, their large number of parameters poses significant challe…