Yuxuan Zhu, David H. Yang, Mohammad Mohammadi Amiri, Keerthiram Murugesan, Tejaswini Pedapati, Pin-Yu Chen
| Challenge: | Existing methods for inference use static, offline-learned subspaces that perform poorly under distribution shifts. |
| Approach: | They propose a framework that integrates a storage policy with an online subspace adaptation to preserve key-value tokens in full rank as high-fidelity anchors. |
| Outcome: | Experiments show that OjaKV maintains or improves zero-shot accuracy at high compression ratios, achieving the strongest gains on long-context benchmarks requiring complex reasoning. |
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| Challenge: | autoregressive inference requires repeated computation across transformer layers. |
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TailorKV: A Hybrid Framework for Long-Context Inference via Tailored KV Cache Optimization (2025.findings-acl)
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| Challenge: | Existing work mitigates memory overhead by offloading or compressing the Key-Value cache. |
| Approach: | They propose a method that integrates quantization and offloading into a generative large language model by using a hybrid compression method. |
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StructKV: Preserving the Structural Skeleton for Scalable Long-Context Inference (2026.findings-acl)
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| Challenge: | Existing compression approaches prioritize tokens based on local saliency metrics to decouple prefill computation from decoding memory. |
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Question Tells You Where the Answer Is: Intention-aware Long-Context KV Cache Compression (2026.acl-long)
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Liang Zhao, Xiaocheng Feng, Weihong Zhong, Lei Huang, Kun Zhu, Baoxin Wang, Dayong Wu, Guoping Hu, Ting Liu, Bing Qin
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| Challenge: | Large language models (LLMs) have been increasing context lengths to enhance their performance, but at long context length, the KV cache becomes the new bottleneck in memory usage during inference. |
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HybridKV: Hybrid KV Cache Compression for Efficient Multimodal Large Language Model Inference (2026.acl-long)
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| Challenge: | Multimodal Large Language Models (MLLMs) are hindered by the rapid growth of key–value (KV) caches. |
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ZigZagKV: Dynamic KV Cache Compression for Long-context Modeling based on Layer Uncertainty (2025.coling-main)
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| Challenge: | Existing methods to accelerate inference of Large Language models (LLMs) are limited in their ability to retain key tokens as input length increases. |
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| Challenge: | Existing static compression methods suffer from coarse-grained caching and high I/O overhead. |
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A2ATS: Retrieval-Based KV Cache Reduction via Windowed Rotary Position Embedding and Query-Aware Vector Quantization (2025.findings-acl)
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Junhui He, Junna Xing, Nan Wang, Rui Xu, Shangyu Wu, Peng Zhou, Qiang Liu, Chun Jason Xue, Qingan Li
| Challenge: | Long context large language models (LLMs) pose significant challenges for efficient serving due to the large memory footprint and high access overhead of KV cache. |
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MiniKV: Pushing the Limits of 2-Bit KV Cache via Compression and System Co-Design for Efficient Long Context Inference (2025.findings-acl)
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| Challenge: | State-of-the-art 2-bit KV cache quantization methods achieve excellent results in accelerating LLM inference while retaining accuracy on long context tasks. |
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