Tootfinder

ATLANTIS: AI-driven Threat Localization, Analysis, and Triage Intelligence System
Taesoo Kim, HyungSeok Han, Soyeon Park, Dae R. Jeong, Dohyeok Kim, Dongkwan Kim, Eunsoo Kim, Jiho Kim, Joshua Wang, Kangsu Kim, Sangwoo Ji, Woosun Song, Hanqing Zhao, Andrew Chin, Gyejin Lee, Kevin Stevens, Mansour Alharthi, Yizhuo Zhai, Cen Zhang, Joonun Jang, Yeongjin Jang, Ammar Askar, Dongju Kim, Fabian Fleischer, Jeongin Cho, Junsik Kim, Kyungjoon Ko, Insu Yun, Sangdon Park, Dowoo Baik, Haein Lee, Hy…

ATLANTIS: AI-driven Threat Localization, Analysis, and Triage Intelligence System
We present ATLANTIS, the cyber reasoning system developed by Team Atlanta that won 1st place in the Final Competition of DARPA's AI Cyber Challenge (AIxCC) at DEF CON 33 (August 2025). AIxCC (2023-2025) challenged teams to build autonomous cyber reasoning systems capable of discovering and patching vulnerabilities at the speed and scale of modern software. ATLANTIS integrates large language models (LLMs) with program analysis -- combining symbolic execution, directed fuzzing, and static analysi…

SpatialTrackerV2: 3D Point Tracking Made Easy
Yuxi Xiao, Jianyuan Wang, Nan Xue, Nikita Karaev, Yuri Makarov, Bingyi Kang, Xing Zhu, Hujun Bao, Yujun Shen, Xiaowei Zhou
https://arxiv.org/abs/2507.12462

SpatialTrackerV2: 3D Point Tracking Made Easy
We present SpatialTrackerV2, a feed-forward 3D point tracking method for monocular videos. Going beyond modular pipelines built on off-the-shelf components for 3D tracking, our approach unifies the intrinsic connections between point tracking, monocular depth, and camera pose estimation into a high-performing and feedforward 3D point tracker. It decomposes world-space 3D motion into scene geometry, camera ego-motion, and pixel-wise object motion, with a fully differentiable and end-to-end archi…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[4/8]:
- DataSentinel: A Game-Theoretic Detection of Prompt Injection Attacks
Yupei Liu, Yuqi Jia, Jinyuan Jia, Dawn Song, Neil Zhenqiang Gong

Replaced article(s) found for physics.comp-ph. https://arxiv.org/list/physics.comp-ph/new
[1/1]:
- Learning phase transitions by siamese neural network
Jianmin Shen, Shiyang Chen, Feiyi Liu, Wei Li, Youju Liu

The stability of long-range order in disordered systems: A generalized Ding-Zhuang argument
Yejia Chen, Jianwen Zhou, Ruifeng Liu, Hai-Jun Zhou
https://arxiv.org/abs/2507.11445

The stability of long-range order in disordered systems: A generalized Ding-Zhuang argument
The stability of long-range order against quenched disorder is a central problem in statistical mechanics. This paper develops a generalized framework extending the Ding-Zhuang method and integrated with the Pirogov-Sinai framework, establishing a systematic scheme for studying phase transitions of long-range order in disordered systems. We axiomatize the Ding-Zhuang approach into a theoretical framework consisting of the Peierls condition and a local symmetry condition. For systems in dimensio…

Time-resolved certification of frequency-bin entanglement over multi-mode channels
St\'ephane Vinet, Marco Clementi, Marcello Bacchi, Yujie Zhang, Massimo Giacomin, Luke Neal, Paolo Villoresi, Matteo Galli, Daniele Bajoni, Thomas Jennewein
https://arxiv.org/abs/2508.10200

Time-resolved certification of frequency-bin entanglement over multi-mode channels
Frequency-bin entangled photons can be efficiently produced on-chip which offers a scalable, robust and low-footprint platform for quantum communication, particularly well-suited for resource-constrained settings such as mobile or satellite-based systems. However, analyzing such entangled states typically requires active and lossy components, limiting scalability and multi-mode compatibility. We demonstrate a novel technique for processing frequency-encoded photons using linear interferometry a…

CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome Reward
Shudong Liu, Hongwei Liu, Junnan Liu, Linchen Xiao, Songyang Gao, Chengqi Lyu, Yuzhe Gu, Wenwei Zhang, Derek F. Wong, Songyang Zhang, Kai Chen
https://arxiv.org/abs/2508.03686

CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome Reward
Answer verification is crucial not only for evaluating large language models (LLMs) by matching their unstructured outputs against standard answers, but also serves as the reward model to guide LLM optimization. Most evaluation frameworks rely on regularized matching or employ general LLMs for answer verification, which demands extensive, repetitive customization for regex rules or evaluation prompts. Two fundamental limitations persist in current methodologies: 1) the absence of comprehensive …

Joint Lossless Compression and Steganography for Medical Images via Large Language Models
Pengcheng Zheng, Xiaorong Pu, Kecheng Chen, Jiaxin Huang, Meng Yang, Bai Feng, Yazhou Ren, Jianan Jiang
https://arxiv.org/abs/2508.01782

Joint Lossless Compression and Steganography for Medical Images via Large Language Models
Recently, large language models (LLMs) have driven promis ing progress in lossless image compression. However, di rectly adopting existing paradigms for medical images suf fers from an unsatisfactory trade-off between compression performance and efficiency. Moreover, existing LLM-based compressors often overlook the security of the compres sion process, which is critical in modern medical scenarios. To this end, we propose a novel joint lossless compression and steganography framework. …

Unnoticeable Community Deception via Multi-objective Optimization
Junyuan Fang, Huimin Liu, Yueqi Peng, Jiajing Wu, Zibin Zheng, Chi K. Tse
https://arxiv.org/abs/2509.01438 http…

Unnoticeable Community Deception via Multi-objective Optimization
Community detection in graphs is crucial for understanding the organization of nodes into densely connected clusters. While numerous strategies have been developed to identify these clusters, the success of community detection can lead to privacy and information security concerns, as individuals may not want their personal information exposed. To address this, community deception methods have been proposed to reduce the effectiveness of detection algorithms. Nevertheless, several limitations, s…

While it is bad #grok is bad again, it is very good how obvious the chains are.
#ai