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Fangming Liu

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3 papers

2 author rows

AAAI Conference 2026 Conference Paper

FP=XINT: Representing Neural Networks via Low-Bit Series Basis Functions

Boyang Zhang
Daning Cheng
Yunquan Zhang
Jiake Tian
Jing Li
Fangming Liu

Deep neural networks are often over-parameterized, resulting in prohibitive storage and computational costs. A fundamental question is whether a complex network can be re-expressed in terms of a compact set of basis functions without sacrificing accuracy. Motivated by this perspective, we aim to approximate a dense model by decomposing it into a small number of lightweight components that capture the essential functional structure of the network. To this end, we propose a series expansion framework that rewrites a neural network as a linear combination of low-bit basis models. Within the post-training quantization setting, the full-precision model is expanded hierarchically at the tensor, layer, and model levels into a structured set of basis functions. We theoretically prove that this expansion converges exponentially to the original model. Furthermore, we design AbelianAdd and AbelianMul operations between isomorphic basis models, endowing the expansion with an Abelian group structure that naturally supports commutative and parallel computation. Experimental results across diverse architectures show that our series expansion method leverages a set of ultra-low-bit basis functions, not only preserving full-precision performance without the need for calibration data or fine-tuning, but also featuring a parallel-friendly design that enables efficient and scalable deployment.

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AAAI Conference 2025 Conference Paper

Knowledge Editing with Dynamic Knowledge Graphs for Multi-Hop Question Answering

Yifan Lu
Yigeng Zhou
Jing Li
Yequan Wang
Xuebo Liu
Daojing He
Fangming Liu
Min Zhang

Multi-hop question answering (MHQA) poses a significant challenge for large language models (LLMs) due to the extensive knowledge demands involved. Knowledge editing, which aims to precisely modify the LLMs to incorporate specific knowledge without negatively impacting other unrelated knowledge, offers a potential solution for addressing MHQA challenges with LLMs. However, current solutions struggle to effectively resolve issues of knowledge conflicts. Most parameter-preserving editing methods are hindered by inaccurate retrieval and overlook secondary editing issues, which can introduce noise into the reasoning process of LLMs. In this paper, we introduce KEDKG, a novel knowledge editing method that leverages a dynamic knowledge graph for MHQA, designed to ensure the reliability of answers. KEDKG involves two primary steps: dynamic knowledge graph construction and knowledge graph augmented generation. Initially, KEDKG autonomously constructs a dynamic knowledge graph to store revised information while resolving potential knowledge conflicts. Subsequently, it employs a fine-grained retrieval strategy coupled with an entity and relation detector to enhance the accuracy of graph retrieval for LLM generation. Experimental results on benchmarks show that KEDKG surpasses previous state-of-the-art models, delivering more accurate and reliable answers in environments with dynamic information.

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ICLR Conference 2025 Conference Paper

Proactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistance

Yaxi Lu
Shenzhi Yang
Cheng Qian 0008
Guirong Chen
Qinyu Luo
Yesai Wu
Huadong Wang
Xin Cong

Agents powered by large language models have shown remarkable abilities in solving complex tasks. However, most agent systems remain reactive, limiting their effectiveness in scenarios requiring foresight and autonomous decision-making. In this paper, we tackle the challenge of developing proactive agents capable of anticipating and initiating tasks without explicit human instructions. We propose a novel data-driven approach for this problem. Firstly, we collect real-world human activities to generate proactive task predictions. These predictions are then labeled by human annotators as either accepted or rejected. The labeled data is used to train a reward model that simulates human judgment and serves as an automatic evaluator of the proactiveness of LLM agents. Building on this, we develop a comprehensive data generation pipeline to create a diverse dataset, ProactiveBench, containing 6,790 events. Finally, we demonstrate that fine-tuning models with the proposed ProactiveBench can significantly elicit the proactiveness of LLM agents. Experimental results show that our fine-tuned model achieves an F1-Score of 66.47% in proactively offering assistance, outperforming all open-source and close-source models. These results highlight the potential of our method in creating more proactive and effective agent systems, paving the way for future advancements in human-agent collaboration.

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