Papers with key-value (KV) cache
DELTA: Dynamic Layer-Aware Token Attention for Efficient Long-Context Reasoning (2026.findings-acl)
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| Challenge: | Large reasoning models generate long chains of intermediate steps, but their inference cost is dominated by decoding, where each new token must attend to the entire growing sequence. |
| Approach: | They propose a training-free sparse attention mechanism that reduces inference cost by evicting entries from the key-value cache. |
| Outcome: | The proposed model matches or surpasses full attention on reasoning benchmarks . it reduces the number of attended tokens by up to 4.25 and delivers 1.54 speedup . |
Eigen Attention: Attention in Low-Rank Space for KV Cache Compression (2024.findings-emnlp)
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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. |
| Approach: | They propose an approach which performs the attention operation in a low-rank space and reduces the KV cache memory overhead. |
| Outcome: | The proposed approach reduces the KV cache memory overhead and reduces memory usage with minimal drop in performance over OPT, MPT, and Llama model families. |
Latent-Condensed Transformer for Efficient Long Context Modeling (2026.acl-long)
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| Challenge: | Existing approaches address these bottlenecks separately: Multi-head Latent Attention (MLA) reduces the KV cache by projecting tokens into a low-dimensional latent space, while sparse attention reduces computation. |
| Approach: | They propose a Latent-Condensed Attention mechanism that performs structured context condensation directly within MLA's latent space. |
| Outcome: | The proposed approach reduces KV cache size and attention cost without adding parameters. |
RefreshKV: Updating Small KV Cache During Long-form Generation (2025.acl-long)
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| Challenge: | Existing methods for generating long sequences of tokens are expensive and require memory and computation resources. |
| Approach: | They propose a method that alternates between full context attention and attention over a subset of input tokens during generation. |
| Outcome: | The proposed method achieves comparable speedup to eviction-based methods while improving performance for various long-form generation tasks. |
Towards Efficient Large Language Model Serving: A Survey on System-Aware KV Cache Optimization (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are a powerful tool for high-performance inference serving. |
| Approach: | They focus on system-aware KV infrastructure for serving LLMs . they analyze cross-behavior co-design affinity and behavior-objective links . |
| Outcome: | The proposed key-value (KV) cache is crucial for low-latency, high-throughput LLM inference serving. |