Papers by Huidong Liu
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. |
Generate First, Then Sample: Enhancing Fake News Detection with LLM-Augmented Reinforced Sampling (2025.acl-long)
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| Challenge: | Existing models have a performance gap of 20% between classifying fake news and real news, making them less suitable for practical deployment. |
| Approach: | They propose to adopt an LLM to generate fake news in three different styles, which are later incorporated into the training set to augment the representation of fake news. |
| Outcome: | The proposed model achieves state-of-the-art performance on two benchmark datasets and improves detection accuracy by 24.02% and 11.06% respectively. |
AgentGC: Evolutionary Learning-based Lossless Compression for Genomics Data with LLM-driven Multiple Agent (2026.findings-acl)
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Sun Hui, Ding Yanfeng, Huidong Ma, Chang Xu, Keyan Jin, Lizheng Zu, Cheng Zhong, Xiaoguang Liu, Gang Wang, Wentong Cai
| 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. |
KG-FLIP: Knowledge-guided Fashion-domain Language-Image Pre-training for E-commerce (2023.acl-industry)
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| Challenge: | Various visionlanguage pre-training (VLP) models learn cross-modal alignment from large-scale well-aligned image-text datasets without leveraging external knowledge. |
| Approach: | They propose a knowledge-guided fashion-domain language-image pre-training framework that learns fine-grained representations in e-commerce domain and utilizes external knowledge to improve the pre-train efficiency. |
| Outcome: | The proposed framework outperforms state-of-the-art models on Amazon and Fashion-Gen datasets by large margins. |