Posts

All the articles I've posted.

• A Large-Scale Empirical Analysis of Custom GPTs' Vulnerabilities in the OpenAI Ecosystem

  Published: 16 May, 2025 |  at  11:13 AM

  

  94.41 🤔

  This paper conducts a large-scale empirical analysis of 14,904 custom GPTs in the OpenAI store, revealing over 95% lack adequate security against attacks like roleplay (96.51%) and phishing (91.22%), introduces a multi-metric popularity ranking system, and highlights the need for enhanced security in both custom and base models.

• VideoUFO: A Million-Scale User-Focused Dataset for Text-to-Video Generation

  Published: 16 May, 2025 |  at  11:10 AM

  

  94.40 🤔

  This paper introduces VideoUFO, a million-scale dataset of 1.09 million video clips across 1,291 user-focused topics for text-to-video generation, curated from YouTube with minimal overlap with existing datasets, demonstrating improved performance on worst-performing topics when training a simple model like MVDiT.

• Contaminated Multivariate Time-Series Anomaly Detection with Spatio-Temporal Graph Conditional Diffusion Models

  Published: 16 May, 2025 |  at  11:29 AM

  

  94.27 🤔

  TSAD-C introduces a pioneering unsupervised framework for multivariate time-series anomaly detection on contaminated data, using a Decontaminator with S4-based diffusion, long-range dependency modeling via a time-then-graph approach, and anomaly scoring, achieving state-of-the-art performance across diverse datasets.

• CB-cPIR: Code-Based Computational Private Information Retrieval

  Published: 8 May, 2025 |  at  11:08 AM

  

  93.98 🤔

  CB-cPIR introduces a code-based single-server computational private information retrieval scheme that enhances security against subquery attacks by using high-weight secret vectors and dual queries, achieving lower communication and computational costs compared to lattice-based schemes like XPIR and SimplePIR.

• Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

  Published: 20 May, 2025 |  at  11:11 AM

  

  93.91 🤔

  本文通过 pass@k 指标系统评估 RLVR 在大型语言模型推理能力边界上的效果，发现 RLVR 仅提高采样效率而未引入新推理模式，其能力受限于基础模型，强调需改进 RL 范式以激发真正的新推理能力。