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Xiaowei Zhu

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3 papers

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NeurIPS Conference 2025 Conference Paper

Connectome-Based Modelling Reveals Orientation Maps in the Drosophila Optic Lobe

Jia Nuo Liew
Shenghan Lin
Bowen Chen
Xiaowei Zhu
Wei Zhang
Xiaolin Hu

The ability to extract oriented edges from visual input is a core computation across animal vision systems. Orientation maps, long associated with the layered architecture of the mammalian visual cortex, systematically organise neurons by their preferred edge orientation. Despite lacking cortical structures, the Drosophila melanogaster brain contains feature-selective neurons and exhibits complex visual detection capacity, raising the question of whether map-like vision representations can emerge without cortical infrastructure. We integrate a complete fruit fly brain connectome with biologically grounded spiking neuron models to simulate neuroprocessing in the fly visual system. By driving the network with oriented stimuli and analysing downstream responses, we show that coherent orientation maps can emerge from purely connectome-constrained dynamics. These results suggest that species of independent origin could evolve similar visual structures.

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NeurIPS Conference 2025 Conference Paper

DNA-DetectLLM: Unveiling AI-Generated Text via a DNA-Inspired Mutation-Repair Paradigm

Xiaowei Zhu
Yubing Ren
Fang Fang
Qingfeng Tan
Shi Wang
Yanan Cao

The rapid advancement of large language models (LLMs) has blurred the line between AI-generated and human-written text. This progress brings societal risks such as misinformation, authorship ambiguity, and intellectual property concerns, highlighting the urgent need for reliable AI-generated text detection methods. However, recent advances in generative language modeling have resulted in significant overlap between the feature distributions of human-written and AI-generated text, blurring classification boundaries and making accurate detection increasingly challenging. To address the above challenges, we propose a DNA-inspired perspective, leveraging a repair-based process to directly and interpretably capture the intrinsic differences between human-written and AI-generated text. Building on this perspective, we introduce DNA-DetectLLM, a zero-shot detection method for distinguishing AI-generated and human-written text. The method constructs an ideal AI-generated sequence for each input, iteratively repairs non-optimal tokens, and quantifies the cumulative repair effort as an interpretable detection signal. Empirical evaluations demonstrate that our method achieves state-of-the-art detection performance and exhibits strong robustness against various adversarial attacks and input lengths. Specifically, DNA-DetectLLM achieves relative improvements of 5. 55\% in AUROC and 2. 08\% in F1 score across multiple public benchmark datasets. Code and data are available at https: //github. com/Xiaoweizhu57/DNA-DetectLLM.

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AAAI Conference 2024 Conference Paper

Integer Is Enough: When Vertical Federated Learning Meets Rounding

Pengyu Qiu
Yuwen Pu
Yongchao Liu
Wenyan Liu
Yun Yue
Xiaowei Zhu
Lichun Li
Jinbao Li

Vertical Federated Learning (VFL) is a solution increasingly used by companies with the same user group but differing features, enabling them to collaboratively train a machine learning model. VFL ensures that clients exchange intermediate results extracted by their local models, without sharing raw data. However, in practice, VFL encounters several challenges, such as computational and communication overhead, privacy leakage risk, and adversarial attack. Our study reveals that the usage of floating-point (FP) numbers is a common factor causing these issues, as they can be redundant and contain too much information. To address this, we propose a new architecture called rounding layer, which converts intermediate results to integers. Our theoretical analysis and empirical results demonstrate the benefits of the rounding layer in reducing computation and memory overhead, providing privacy protection, preserving model performance, and mitigating adversarial attacks. We hope this paper inspires further research into novel architectures to address practical issues in VFL.

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