Haobo Zhang | Search arXiv

arXiv2026

AndroTruth: A Reliable Benchmark Android Malware Dataset Derived from Technical Expert Reports

Reliable family labels are essential for Android malware analysis, yet most widely used benchmarks derive such labels from aggregated VirusTotal engine outputs. Because antivirus vendors differ in detection logic, naming conventions, and signature updates, these labels are often inconsistent across engines and unstable over time, which introduces substantial noise into downstream empirical evaluation. To address this problem, we construct AndroTruth, an Android malware family benchmark whose labels are derived exclusively from traceable expert technical analysis reports rather than AV-consensus voting. AndroTruth spans 2016 to 2025 and contains 8,172 malware samples from 187 families. Our statistical results show that automated labeling tools can exhibit a misleading consensus failure mode in which AVClass2 and ClarAVy agree with each other yet jointly disagree with expert ground truth on 25.38% of samples with explicit labels from both tools. Experimental results show that, under expert-verified supervision, representative classifiers such as Meta-MAMC and AndMFC achieve accuracy above 96%. When trained with real-world AV-derived labels and evaluated against expert ground truth, however, their performance drops to only about 60% accuracy and about 35% macro-F1. ClarAVy confidenceaware filtering can improve family grouping quality, but cannot replace expert-verified labels for exact family naming. Together, these results demonstrate that label reliability is a first-order factor in Android malware family evaluation and highlight the need for expert-verified benchmarks.

cs.CR↗

arXiv2026

Matrix-Game 3.5: Enhancing Real-Time Streaming Interactive World Models with Patch Memory

Interactive world models extend video generation from offline clip synthesis toward persistent simulation of interactive virtual worlds, enabling applications in games, robotics, embodied agents, and XR. Achieving stable long-horizon interactive generation, however, remains challenging, as the model must simultaneously preserve scene geometry, dynamic consistency, and camera control while supporting real-time autoregressive generation. Building upon Matrix-Game 3.0, we present Matrix-Game 3.5, as shown in Figure 1, which advances real-time interactive world generation toward geometry-aware and long-horizon consistent simulation through three key improvements. First, we propose a unified geometry-aware memory framework, whose patch-memory and tiled-PRoPE components introduce no additional learnable parameters, combining explicit 3D patch retrieval with projective camera conditioning to enable geometry-consistent camera control and faithful long-horizon scene recall. Second, we introduce a static-dynamic disentangled world representation that separately models static scene geometry and dynamic subjects, preserving both geometric consistency and subject identity throughout long-horizon generation. Third, we develop a two-stage progressive real-time distillation framework that converts a bidirectional diffusion model into a few-step causal generator through Perceptual Flow Matching and curriculum based Self-Rollout DMD, enabling minute-long real-time interactive generation. Extensive experiments demonstrate that, with a unified training corpus spanning Unreal simulation environments, open-world games, and internet videos, MatrixGame 3.5 achieves strong performance in long-horizon scene recall, precise camera control, subject consistency, prompt-driven world generation, and stable real-time open-world interaction.

cs.CV↗