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Tao Ni

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AAAI Conference 2026 Conference Paper

FIXME: Towards End-to-End Benchmarking of LLM-Aided Design Verification

  • Gwok-Waa Wan
  • SamZaak Wong
  • Shengchu Su
  • Chenxu Niu
  • Ning Wang
  • Xinlai Wan
  • Qixiang Chen
  • Mengnv Xing

We introduce FIXME, the first end-to-end and large-scale benchmark for evaluating Large Language Models (LLMs) in hardware design functional verification (FV). Comprising 747 tasks derived from real-world hardware designs, FIXME spans five core FV sub-sets: specification comprehension, reference model generation, testbench generation, assertion design, and RTL debugging. To ensure high data quality, we developed an AI-human collaborative framework for agile data curation and annotation. This process resulted in 25,000 lines of verified RTL, 35,000 lines of enhanced testbenches, and over 1,200 SystemVerilog Assertions. Furthermore, through expert-guided optimization within the multi-agent aided flow, we achieved a remarkable 45.57% improvement in average functional coverage, underscoring the benchmark's robustness. Through evaluation of state-of-the-art LLMs like GPT-4.1, FIXME identifies key limitations and provides actionable insights, advancing the potential of LLM-driven automation in hardware design functional verification.

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NeurIPS Conference 2025 Conference Paper

The Fluorescent Veil: A Stealthy and Effective Physical Adversarial Patch Against Traffic Sign Recognition

  • Shuai Yuan
  • Xingshuo Han
  • Hongwei Li
  • Guowen Xu
  • Wenbo Jiang
  • Tao Ni
  • Qingchuan Zhao
  • Yuguang Fang

Recently, traffic sign recognition (TSR) systems have become a prominent target for physical adversarial attacks. These attacks typically rely on conspicuous stickers and projections, or using invisible light and acoustic signals that can be easily blocked. In this paper, we introduce a novel attack medium, i. e. , fluorescent ink, to design a stealthy and effective physical adversarial patch, namely FIPatch, to advance the state-of-the-art. Specifically, we first model the fluorescence effect in the digital domain to identify the optimal attack settings, which guide the real-world fluorescence parameters. By applying a carefully designed fluorescence perturbation to the target sign, the attacker can later trigger a fluorescent effect using invisible ultraviolet light, causing the TSR system to misclassify the sign and potentially leading to traffic accidents. We conducted a comprehensive evaluation to investigate the effectiveness of FIPatch, which shows a success rate of 98. 31% in low-light conditions. Furthermore, our attack successfully bypasses five popular defenses and achieves a success rate of 96. 72%.

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