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

• URL: http://arxiv.org/abs/2601.13559v1
• Date: Tue, 20 Jan 2026 03:29:45 GMT
• Title: AgentGC: Evolutionary Learning-based Lossless Compression for Genomics Data with LLM-driven Multiple Agent
• Abstract summary: AgentGC is the first evolutionary agent-based Genomics Data (GD) management system.<n>AgentGC integrates user-friendly interface, cognitive layer, and compression layer.<n>Compared with 14 baselines on 9 datasets, the average compression ratios gains are 16.66%, 16.11%, and 16.33%, respectively.
• Score: 25.73008424563264
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• TAFA-GSGC: Group-wise Scalable Point Cloud Geometry Compression with Progressive Residual Refinement [62.57701599278593]
  We propose TAFA-GSGC, a scalable learned point cloud geometry that enables multi-quality decoding from a single bitstream and a single trained model.<n>Compared with the baseline PCGCv2, TAFA-GSGC attains comparable and slightly better RD performance, achieving average BD-Rate savings of -4.99% in D1 and -5.92% in D2.
  arXiv  Detail & Related papers  (2026-04-30T15:57:59Z)
• Arbitrary Ratio Feature Compression via Next Token Prediction [52.10426317889982]
  Arbitrary Ratio Feature Compression (ARFC) framework supports any compression ratio with a single model.<n>ARC is an auto-regressive model that performs compression via next-gressive prediction.<n>MoS module refines the compressed tokens by utilizing multiple compression results.<n>ERGC is integrated into the training process to preserve semantic and structural relationships during compression.
  arXiv  Detail & Related papers  (2026-02-12T02:38:57Z)
• UltraCUA: A Foundation Model for Computer Use Agents with Hybrid Action [77.63125913907771]
  We present UltraCUA, a foundation model that bridges the gap between GUI primitives and high-level programmatic tool calls.<n>Experiments with our 7B and 32B models demonstrate substantial improvements over state-of-the-art agents.
  arXiv  Detail & Related papers  (2025-10-20T17:48:26Z)
• Compressing Many-Shots in In-Context Learning [61.231471139896506]
  We study an approach to improve the memory and computational efficiency of ICL inference by compressing the many-shot prompts.<n>We first show that existing prompt compression methods are ineffective for many-shot compression.<n>We propose MemCom, a layer-wise compression method.
  arXiv  Detail & Related papers  (2025-10-17T16:57:42Z)
• Layer-wise dynamic rank for compressing large language models [2.9416461160070955]
  Large language models (LLMs) have rapidly scaled in size, bringing severe memory and computational challenges.<n>We propose D-Rank, a framework with layer-wise balanced Dynamic Rank allocation for LLMs compression.
  arXiv  Detail & Related papers  (2025-09-30T00:29:50Z)
• PMKLC: Parallel Multi-Knowledge Learning-based Lossless Compression for Large-Scale Genomics Database [9.687284031748792]
  We propose a novel underlineParallel underlineMulti-underlineKnowledge underlineLearning-based underlineCompressor (PMKLC) with four crucial designs.<n>We benchmark PMKLC-S/M and 14 baselines on 15 real-world datasets with different species and data sizes.<n>PMKLC-S/M achieve the average compression ratio improvement up to 73.609% and 73.480%, the average throughput improvement up to 3.036
  arXiv  Detail & Related papers  (2025-07-17T05:46:08Z)
• Breaking the Compression Ceiling: Data-Free Pipeline for Ultra-Efficient Delta Compression [57.71917274869577]
  UltraDelta is a data-free delta compression pipeline that achieves both ultra-high compression and strong performance.<n>UltraDelta is designed to minimize redundancy, maximize information, and stabilize performance across inter-layer, intra-layer, and global dimensions.
  arXiv  Detail & Related papers  (2025-05-19T10:37:22Z)
• UniPCGC: Towards Practical Point Cloud Geometry Compression via an Efficient Unified Approach [4.754973569457509]
  We propose an efficient unified point cloud geometry compression framework, dubbed as UniPCGC.<n>It supports lossy compression, lossless compression, variable rate and variable complexity.<n>Our method achieves a compression ratio (CR) gain of 8.1% on lossless compression, and a Bjontegaard Delta Rate (BD-Rate) gain of 14.02% on lossy compression.
  arXiv  Detail & Related papers  (2025-03-24T10:51:28Z)
• HEMGS: A Hybrid Entropy Model for 3D Gaussian Splatting Data Compression [25.820461699307042]
  We introduce a novel Hybrid Entropy Model for 3D Gaussian Splatting (HEMGS) to achieve hybrid lossy-lossless compression.<n>It consists of three main components: a variable-rate predictor, a hyperprior network, and an autoregressive network.<n>HEMGS achieves about a 40% average reduction in size while maintaining rendering quality over baseline methods.
  arXiv  Detail & Related papers  (2024-11-27T16:08:59Z)
• EoRA: Fine-tuning-free Compensation for Compressed LLM with Eigenspace Low-Rank Approximation [84.70637613266835]
  EoRA is a fine-tuning-free method that augments compressed Large Language Models with low-rank matrices.<n>EoRA consistently outperforms prior training-free low rank methods in recovering the accuracy of compressed LLMs.
  arXiv  Detail & Related papers  (2024-10-28T17:59:03Z)
• FusionLLM: A Decentralized LLM Training System on Geo-distributed GPUs with Adaptive Compression [55.992528247880685]
  Decentralized training faces significant challenges regarding system design and efficiency. We present FusionLLM, a decentralized training system designed and implemented for training large deep neural networks (DNNs) We show that our system and method can achieve 1.45 - 9.39x speedup compared to baseline methods while ensuring convergence.
  arXiv  Detail & Related papers  (2024-10-16T16:13:19Z)
• An Efficient Statistical-based Gradient Compression Technique for Distributed Training Systems [77.88178159830905]
  Sparsity-Inducing Distribution-based Compression (SIDCo) is a threshold-based sparsification scheme that enjoys similar threshold estimation quality to deep gradient compression (DGC) Our evaluation shows SIDCo speeds up training by up to 41:7%, 7:6%, and 1:9% compared to the no-compression baseline, Topk, and DGC compressors, respectively.
  arXiv  Detail & Related papers  (2021-01-26T13:06:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.