Daily Papers

Are Capture-the-Flag competitions obsolete? In 2025, Cybersecurity AI (CAI) systematically conquered some of the world's most prestigious hacking competitions, achieving Rank #1 at multiple events and consistently outperforming thousands of human teams. Across five major circuits-HTB's AI vs Humans, Cyber Apocalypse (8,129 teams), Dragos OT CTF, UWSP Pointer Overflow, and the Neurogrid CTF showdown-CAI demonstrated that Jeopardy-style CTFs have become a solved game for well-engineered AI agents. At Neurogrid, CAI captured 41/45 flags to claim the 50,000 top prize; at Dragos OT, it sprinted 37% faster to 10K points than elite human teams; even when deliberately paused mid-competition, it maintained top-tier rankings. Critically, CAI achieved this dominance through our specialized alias1 model architecture, which delivers enterprise-scale AI security operations at unprecedented cost efficiency and with augmented autonomy-reducing 1B token inference costs from 5,940 to just $119, making continuous security agent operation financially viable for the first time. These results force an uncomfortable reckoning: if autonomous agents now dominate competitions designed to identify top security talent at negligible cost, what are CTFs actually measuring? This paper presents comprehensive evidence of AI capability across the 2025 CTF circuit and argues that the security community must urgently transition from Jeopardy-style contests to Attack & Defense formats that genuinely test adaptive reasoning and resilience-capabilities that remain uniquely human, for now.

Any-scale image synthesis offers an efficient and scalable solution to synthesize photo-realistic images at any scale, even going beyond 2K resolution. However, existing GAN-based solutions depend excessively on convolutions and a hierarchical architecture, which introduce inconsistency and the ``texture sticking" issue when scaling the output resolution. From another perspective, INR-based generators are scale-equivariant by design, but their huge memory footprint and slow inference hinder these networks from being adopted in large-scale or real-time systems. In this work, we propose Column-Row Entangled Pixel Synthesis (CREPS), a new generative model that is both efficient and scale-equivariant without using any spatial convolutions or coarse-to-fine design. To save memory footprint and make the system scalable, we employ a novel bi-line representation that decomposes layer-wise feature maps into separate ``thick" column and row encodings. Experiments on various datasets, including FFHQ, LSUN-Church, MetFaces, and Flickr-Scenery, confirm CREPS' ability to synthesize scale-consistent and alias-free images at any arbitrary resolution with proper training and inference speed. Code is available at https://github.com/VinAIResearch/CREPS.

Recent advances in image generation, particularly via diffusion models, have led to impressive improvements in image synthesis quality. Despite this, diffusion models are still challenged by model-induced artifacts and limited stability in image fidelity. In this work, we hypothesize that the primary cause of this issue is the improper resampling operation that introduces aliasing in the diffusion model and a careful alias-free resampling dictated by image processing theory can improve the model's performance in image synthesis. We propose the integration of alias-free resampling layers into the UNet architecture of diffusion models without adding extra trainable parameters, thereby maintaining computational efficiency. We then assess whether these theory-driven modifications enhance image quality and rotational equivariance. Our experimental results on benchmark datasets, including CIFAR-10, MNIST, and MNIST-M, reveal consistent gains in image quality, particularly in terms of FID and KID scores. Furthermore, we propose a modified diffusion process that enables user-controlled rotation of generated images without requiring additional training. Our findings highlight the potential of theory-driven enhancements such as alias-free resampling in generative models to improve image quality while maintaining model efficiency and pioneer future research directions to incorporate them into video-generating diffusion models, enabling deeper exploration of the applications of alias-free resampling in generative modeling.