Papers by Pengfei Fang
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. |
Do LLMs Know Tool Irrelevance? Demystifying Structural Alignment Bias in Tool Invocations (2026.acl-long)
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| Challenge: | Large language models (LLMs) have impressive capabilities in utilizing external tools, but in practice, they are often exposed to tools that are irrelevant to the user’s query, in which case the desired behavior is to refrain from invocations. |
| Approach: | They propose a new dataset that decouples structural alignment from semantic relevance and propose rebalancing strategies that effectively mitigates structural alignment bias. |
| Outcome: | The proposed approach effectively mitigates structural alignment bias without degrading general tool-use capabilities. |
Human-in-the-loop Robotic Grasping Using BERT Scene Representation (2022.coling-1)
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| Challenge: | Existing approaches for robotic grasping in cluttered scenes are expensive and lack structure information. |
| Approach: | They propose a human-in-the-loop framework for robotic grasping in cluttered scenes . they substitute scene-graph representation with a text representation of the scene using BERT . |
| Outcome: | The proposed framework outperforms object-agnostic and scene-graph based methods on robots and physical robots. |
AscendKernelGen: LLM-Driven Kernel Generation for NPUs (2026.findings-acl)
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Xinzi Cao, Jianyang Zhai, Pengfei Li, Zhiheng Hu, Cen Yan, null Mubingxu, Guanghuan Fang, Bin She, Jiayu Li, Yihan Su, Dongyang Tao, Feidiao Yang, Chang-Dong Wang, Yutong Lu, Weicheng Xue, Bin Zhou, Yonghong Tian
| Challenge: | Neural Processing Units (NPUs) are critical for AI infrastructure, but their development remains a bottleneck due to vendor-specific Domain-Specific Languages (DSLs). |
| Approach: | They propose a framework for NPU kernel development that bridges the gap in hardware-specific coding . compiler success on complex Level-2 kernels improves from 0% to 95.5%, they say . |
| Outcome: | The proposed framework bridges the gap in hardware-specific coding, showing a near-zero success rate on complex kernels. |
Uni-Encoder: A Fast and Accurate Response Selection Paradigm for Generation-Based Dialogue Systems (2023.findings-acl)
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| Challenge: | Existing approaches to decode a given context-candidate pair are expensive and time-consuming. |
| Approach: | They propose a new paradigm that keeps full attention over each pair while only encoding the context once. |
| Outcome: | The proposed paradigm achieves new state-of-the-art results on four benchmark datasets with high computational efficiency. |