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IP-Augmented Multi-Modal Malicious URL Detection Via Token-Contrastive Representation Enhancement and Multi-Granularity Fusion
Ye Tian, Yanqiu Yu, Liangliang Song, Zhiquan Liu, Yanbin Wang, Jianguo Sun
https://arxiv.org/abs/2510.12395

IP-Augmented Multi-Modal Malicious URL Detection Via Token-Contrastive Representation Enhancement and Multi-Granularity Fusion
Malicious URL detection remains a critical cybersecurity challenge as adversaries increasingly employ sophisticated evasion techniques including obfuscation, character-level perturbations, and adversarial attacks. Although pre-trained language models (PLMs) like BERT have shown potential for URL analysis tasks, three limitations persist in current implementations: (1) inability to effectively model the non-natural hierarchical structure of URLs, (2) insufficient sensitivity to character-level o…

Adversarial Robustness in One-Stage Learning-to-Defer
Yannis Montreuil, Letian Yu, Axel Carlier, Lai Xing Ng, Wei Tsang Ooi
https://arxiv.org/abs/2510.10988 https://

Adversarial Robustness in One-Stage Learning-to-Defer
Learning-to-Defer (L2D) enables hybrid decision-making by routing inputs either to a predictor or to external experts. While promising, L2D is highly vulnerable to adversarial perturbations, which can not only flip predictions but also manipulate deferral decisions. Prior robustness analyses focus solely on two-stage settings, leaving open the end-to-end (one-stage) case where predictor and allocation are trained jointly. We introduce the first framework for adversarial robustness in one-stage …

Uber launches Uber Intelligence, an insights platform that lets advertisers tap into Uber's data about customer trips and deliveries (Lara O'Reilly/Business Insider)
https://www.businessinsider.com/uber-ads-launches-intelli…

Uber is turning trip and takeout data into insights for marketers
Uber has said its ad business is on track to generate $1.5 billion in revenue in 2025. It's hoping a new insights platform will encourage more spend.

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Robust and Efficient Collaborative Learning
Abdellah El Mrini, Sadegh Farhadkhan, Rachid Guerraoui
https://arxiv.org/abs/2510.08311 https://arxiv.org/pdf/2…

Robust and Efficient Collaborative Learning
Collaborative machine learning is challenged by training-time adversarial behaviors. Existing approaches to tolerate such behaviors either rely on a central server or induce high communication costs. We propose Robust Pull-based Epidemic Learning (RPEL), a novel, scalable collaborative approach to ensure robust learning despite adversaries. RPEL does not rely on any central server and, unlike traditional methods, where communication costs grow in $\mathcal{O}(n^2)$ with the number of nodes $n$,…

Uber launches Uber Intelligence, an insights platform that lets advertisers tap into Uber's data about customer trips and deliveries (Lara O'Reilly/Business Insider)
https://www.businessinsider.com/uber-ads-launches-intelli…

Uber is turning trip and takeout data into insights for marketers
Uber has said its ad business is on track to generate $1.5 billion in revenue in 2025. It's hoping a new insights platform will encourage more spend.

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization
Th\'eophane Vallaeys, Jakob Verbeek, Matthieu Cord
https://arxiv.org/abs/2510.04961 https://

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization
Tokenizers are a key component of state-of-the-art generative image models, extracting the most important features from the signal while reducing data dimension and redundancy. Most current tokenizers are based on KL-regularized variational autoencoders (KL-VAE), trained with reconstruction, perceptual and adversarial losses. Diffusion decoders have been proposed as a more principled alternative to model the distribution over images conditioned on the latent. However, matching the performance o…

A unified Bayesian framework for adversarial robustness
Pablo G. Arce, Roi Naveiro, David R\'ios Insua
https://arxiv.org/abs/2510.09288 https://arxiv.o…

A unified Bayesian framework for adversarial robustness
The vulnerability of machine learning models to adversarial attacks remains a critical security challenge. Traditional defenses, such as adversarial training, typically robustify models by minimizing a worst-case loss. However, these deterministic approaches do not account for uncertainty in the adversary's attack. While stochastic defenses placing a probability distribution on the adversary exist, they often lack statistical rigor and fail to make explicit their underlying assumptions. To reso…

Chinese short-video company Kuaishou launches Kling Video O1, saying it is the first multimodal AI model to unify video generation, editing, and post-production (Ben Jiang/South China Morning Post)
https://www.scmp.com/tech/tech-trends/a…

Kuaishou debuts multimodal AI video model with ‘Nano Banana’ editing capability
Kuaishou expects the new model’s abilities to drive its adoption by filmmakers, production studios, advertisers and influencers.