AgentGC: Evolutionary Learning-based Lossless Compression for Genomics Data with LLM-driven Multiple Agent

TL;DR

AgentGC introduces an evolutionary, multi-agent framework for lossless genomics data compression, leveraging LLMs for adaptability, achieving significant improvements in compression ratio and throughput over existing methods.

Contribution

This paper presents the first evolutionary agent-based genomics data compressor integrating LLMs, enhancing modeling, adaptability, and user interface in lossless compression.

Findings

Average compression ratio gains of ~16% over baselines.

Throughput improvements of up to 9.23x.

Supports diverse scenarios with three operational modes.

Abstract

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.