Papers by Fangxin Wang
FlexQuant: A Flexible and Efficient Dynamic Precision Switching Framework for LLM Quantization (2025.findings-emnlp)
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Fangxin Liu, Zongwu Wang, Jinhong Xia, Junping Zhao, Shouren Zhao, Jinjin Li, Jian Liu, Li Jiang, Haibing Guan
| Challenge: | Existing methods for quantization of large language models struggle to adapt to dynamic workloads. |
| Approach: | a new framework optimizes the trade-off between inference speed and accuracy . FlexQuant enables fine-grained, layer-wise mixed-precision quantization . |
| Outcome: | a new framework optimizes the trade-off between inference speed and accuracy . it achieves a 1.3 speedup across diverse language tasks with negligible accuracy loss . |
Calibrated Speculative Decoding: Frequency-Guided Candidate Selection for Efficient Inference (2026.acl-long)
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| Challenge: | Speculative decoding (SD) is a powerful and efficient way to accelerate autoregressive generation. |
| Approach: | They propose a training-free framework that recovers valid tokens discarded by standard verification . they use online correction memory and Semantic Consistency Gating to analyze rejections . |
| Outcome: | The proposed framework outperforms existing methods and achieves peak throughput speedup of 2.33x. |
LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey (2026.findings-acl)
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Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu, Jizhou Guo, Yankai Chen, Chunyu Miao, Hoang H Nguyen, Yue Zhou, Weizhi Zhang, Liancheng Fang, Hanrong Zhang, Fangxin Wang, Pengfei Zhang, Langzhou He, Yangning Li, Dongyuan Li, Renhe Jiang, Philip S. Yu
| Challenge: | Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents. |
| Approach: | They propose to integrate human-provided information, feedback, or control into the agent system to enhance system performance, reliability, and safety. |
| Outcome: | The proposed systems improve system performance, reliability, and safety by integrating human-provided information, feedback, or control into the agent system. |
Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback (2024.findings-acl)
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Zhangqian Bi, Yao Wan, Zheng Wang, Hongyu Zhang, Batu Guan, Fangxin Lu, Zili Zhang, Yulei Sui, Hai Jin, Xuanhua Shi
| Challenge: | Large Language Models (LLMs) generate code for given contexts, such as incomplete code, class, data structure, or project-specific information. |
| Approach: | They propose a compiler feedback-based code generation approach that leverages static analysis to identify mismatches between the generated code and the project's context. |
| Outcome: | The proposed model outperforms retrieval-based code generation baselines and significantly outperfies the existing large language models. |