February 2026

arXiv:2602.10160v1 Announce Type: new Abstract: End-to-end autonomous driving systems have achieved significant progress, yet their adversarial robustness remains largely underexplored. In this work, we conduct a closed-loop evaluation of state-of-the-art autonomous driving agents under black-box adversarial threat models in CARLA. Specifically, we consider three representative attack vectors on the visual perception pipeline: (i) a physics-based blur attack induced by acoustic waves, (ii) an electromagnetic interference attack that distorts captured images, and (iii) a digital attack that adds ghost […]

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arXiv:2602.10159v1 Announce Type: new Abstract: Traditional video retrieval benchmarks focus on matching precise descriptions to closed video pools, failing to reflect real-world searches characterized by fuzzy, multi-dimensional memories on the open web. We present textbf{RVMS-Bench}, a comprehensive system for evaluating real-world video memory search. It consists of textbf{1,440 samples} spanning textbf{20 diverse categories} and textbf{four duration groups}, sourced from textbf{real-world open-web videos}. RVMS-Bench utilizes a hierarchical description framework encompassing textbf{Global Impression, Key Moment, Temporal Context, and Auditory Memory} […]

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arXiv:2602.10157v1 Announce Type: new Abstract: Encryption has been commonly used in network traffic to secure transmission, but it also brings challenges for malicious traffic detection, due to the invisibility of the packet payload. Graph-based methods are emerging as promising solutions by leveraging multi-host interactions to promote detection accuracy. But most of them face a critical problem: Graph Drift, where the flow statistics or topological information of a graph change over time. To overcome these drawbacks, we propose a […]

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arXiv:2602.10154v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) enhance collaboration in Extended Reality (XR) environments by enabling flexible object and animation creation through the combination of natural language and visual inputs. However, visual data captured by XR headsets includes real-world backgrounds that may contain irrelevant or sensitive user information, such as credit cards left on the table or facial identities of other users. Uploading those frames to cloud-based MLLMs poses serious privacy risks, particularly when such […]

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arXiv:2602.10153v1 Announce Type: new Abstract: The AI Control research agenda aims to develop control protocols: safety techniques that prevent untrusted AI systems from taking harmful actions during deployment. Because human oversight is expensive, one approach is trusted monitoring, where weaker, trusted models oversee stronger, untrusted models$unicode{x2013}$but this often fails when the untrusted model’s actions exceed the monitor’s comprehension. We introduce legibility protocols, which encourage the untrusted model to take actions that are easier for a monitor to evaluate. […]

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arXiv:2602.10149v1 Announce Type: new Abstract: Third-Party Risk Assessment (TPRA) is a core cybersecurity practice for evaluating suppliers against standards such as ISO/IEC 27001 and NIST. TPRA questionnaires are typically drawn from large repositories of security and compliance questions, yet tailoring assessments to organizational needs remains a largely manual process. Existing retrieval approaches rely on keyword or surface-level similarity, which often fails to capture implicit assessment scope and control semantics. This paper explores strategies for organizing and retrieving TPRA […]

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arXiv:2602.10148v1 Announce Type: new Abstract: Vision-Language Models (VLMs) with multimodal reasoning capabilities are high-value attack targets, given their potential for handling complex multimodal harmful tasks. Mainstream black-box jailbreak attacks on VLMs work by distributing malicious clues across modalities to disperse model attention and bypass safety alignment mechanisms. However, these adversarial attacks rely on simple and fixed image-text combinations that lack attack complexity scalability, limiting their effectiveness for red-teaming VLMs’ continuously evolving reasoning capabilities. We propose textbf{CrossTALK} (textbf{underline{Cross}}-modal entextbf{underline{TA}}ngtextbf{underline{L}}ement […]

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arXiv:2602.10147v1 Announce Type: new Abstract: The use of Large Language Models (LLMs) has drawn growing interest within the scientific community. LLMs can handle large volumes of textual data and support methods for evidence synthesis. Although recent studies highlight the potential of LLMs to accelerate screening and data extraction steps in systematic reviews, detailed reports of their practical application throughout the entire process remain scarce. This paper presents an experience report on the conduction of a systematic mapping study […]

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arXiv:2602.10146v1 Announce Type: new Abstract: While Vision-Language Models (VLMs) have shown promise in textual understanding, they face significant challenges when handling long context and complex reasoning tasks. In this paper, we dissect the internal mechanisms governing long-context processing in VLMs to understand their performance bottlenecks. Through the lens of attention analysis, we identify specific Visual Evidence Retrieval (VER) Heads – a sparse, dynamic set of attention heads critical for locating visual cues during reasoning, distinct from static OCR […]

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arXiv:2602.10143v1 Announce Type: new Abstract: Recently, few-shot learning (FSL) has become a popular task that aims to recognize new classes from only a few labeled examples and has been widely applied in fields such as natural science, remote sensing, and medical images. However, most existing methods focus only on the visual modality and compute prototypes directly from raw support images, which lack comprehensive and rich multimodal information. To address these limitations, we propose a novel Multimodal Prototype Augmentation […]

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