用多智能体和大模型实现可进化基因组数据无损压缩，效果显著优于现有方法。
AgentGC: Evolutionary Learning-based Lossless Compression for Genomics Data with LLM-driven Multiple Agent
- 设计多智能体架构，由领导与工作智能体协作完成压缩任务。
- 在9个数据集上平均压缩率提升16.3%，吞吐量最高提升9.23倍。
- 支持三种模式，适配不同场景需求，界面友好且自适应性强。
无损压缩在基因组数据（GD）存储、共享与管理中取得显著进展。现有基于学习的方法存在不可进化、建模层次低、适应性差及用户界面不友好等问题。为此，我们提出AgentGC，首个基于进化智能体的基因组数据压缩器，包含三层架构：1）用户层通过领导者结合大语言模型提供友好的交互界面；2）认知层由领导者驱动，融合大语言模型，实现算法-数据-系统联合优化，解决低层次建模与适应性不足问题；3）压缩层由工作智能体主导，基于自动化多知识学习框架完成压缩与解压。基于AgentGC，设计三种模式以支持多样化场景：CP（优先压缩比）、TP（优先吞吐量）和BM（平衡模式）。在9个数据集上对比14种基线方法，平均压缩率分别提升16.66%、16.11%和16.33%，吞吐量分别提升4.73倍、9.23倍和9.15倍。
原文摘要 · Abstract (English)
Lossless compression has made significant advancements in Genomics Data (GD) storage, sharing and management. Current learning-based methods are non-evolvable with problems of low-level compression modeling, limited adaptability, and user-unfriendly interface. To this end, we propose AgentGC, the first evolutionary Agent-based GD Compressor, consisting of 3 layers with multi-agent named Leader and Worker. Specifically, the 1) User layer provides a user-friendly interface via Leader combined with LLM; 2) Cognitive layer, driven by the Leader, integrates LLM to consider joint optimization of algorithm-dataset-system, addressing the issues of low-level modeling and limited adaptability; and 3) Compression layer, headed by Worker, performs compression & decompression via a automated multi-knowledge learning-based compression framework. On top of AgentGC, we design 3 modes to support diverse scenarios: CP for compression-ratio priority, TP for throughput priority, and BM for balanced mode. Compared with 14 baselines on 9 datasets, the average compression ratios gains are 16.66%, 16.11%, and 16.33%, the throughput gains are 4.73x, 9.23x, and 9.15x, respectively.
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