The autoregressive inference in large language models requires repeated computation across transformer layers. While caching intermediate key-value (KV) pairs eliminates redundancy, it introduces severe memory overhead, particularly in long-context settings. Most existing cache compression methods operate solely on either quantization or eviction, based on importance estimation of cached data. However, they are limited by coarse compression choices and inaccurate importance assessment, leading to suboptimal inference quality. To address this, we propose HqeKV, a hybrid compression framework built on both quantization and eviction, offering finer-grained compression options that adapt smoothly to the varying importance of cached KV pairs. An integrated optimizer automatically selects the best compression action for each cached element, maximizing quality while insulating end-users from tedious low-level tuning details. We further design a joint K–V importance metric to provide more accurate importance assessment results so that the optimizer can make smarter decisions. Additionally, HqeKV supports flexible conversion policies across multiple quantization precision levels, to further reduce quality degradation. Extensive experiments show that HqeKV improves output quality under the same memory constraints, outperforming state-of-the-art alternatives. Code is available at https://github.com/skywclouds/HqeKV.

Dense retrievers encode queries and documents and map them in an embedding space using pre-trained language models. These embeddings need to be high-dimensional to fit training signals and guarantee the retrieval effectiveness of dense retrievers. However, these high-dimensional embeddings lead to larger index storage and higher retrieval latency. To reduce the embedding dimensions of dense retrieval, this paper proposes a Conditional Autoencoder (ConAE) to compress the high-dimensional embeddings to maintain the same embedding distribution and better recover the ranking features. Our experiments show that ConAE is effective in compressing embeddings by achieving comparable ranking performance with its teacher model and making the retrieval system more efficient. Our further analyses show that ConAE can alleviate the redundancy of the embeddings of dense retrieval with only one linear layer. All codes of this work are available at https://github.com/NEUIR/ConAE.