Papers by Huidong Ma

2 papers
Efficient Learned Data Compression via Dual-Stream Feature Decoupling (2026.acl-long)

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Challenge: Learned data compression has achieved superior compression ratios, but balancing precise probability modeling with system efficiency remains challenging.
Approach: They propose a Dual-Stream Multi-Scale Decoupler that disentangles local and global contexts to replace deep serial processing with shallow parallel streams.
Outcome: The proposed method achieves state-of-the-art performance in both compression ratio and throughput while maintaining the lowest latency and memory usage.
AgentGC: Evolutionary Learning-based Lossless Compression for Genomics Data with LLM-driven Multiple Agent (2026.findings-acl)

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Challenge: Lossless compression has made significant advancements in Genomics Data storage, sharing and management.
Approach: They propose a novel agent-based GD Compressor with 3 layers with a multi-agent named Leader and Worker.
Outcome: The proposed method improves on existing methods with low-level modeling and limited adaptability and user-unfriendly interface.

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