Tootfinder

SafeMT: Multi-turn Safety for Multimodal Language Models
Han Zhu, Juntao Dai, Jiaming Ji, Haoran Li, Chengkun Cai, Pengcheng Wen, Chi-Min Chan, Boyuan Chen, Yaodong Yang, Sirui Han, Yike Guo
https://arxiv.org/abs/2510.12133

SafeMT: Multi-turn Safety for Multimodal Language Models
With the widespread use of multi-modal Large Language models (MLLMs), safety issues have become a growing concern. Multi-turn dialogues, which are more common in everyday interactions, pose a greater risk than single prompts; however, existing benchmarks do not adequately consider this situation. To encourage the community to focus on the safety issues of these models in multi-turn dialogues, we introduce SafeMT, a benchmark that features dialogues of varying lengths generated from harmful quer…

Elevating Medical Image Security: A Cryptographic Framework Integrating Hyperchaotic Map and GRU
Weixuan Li, Guang Yu, Quanjun Li, Junhua Zhou, Jiajun Chen, Yihang Dong, Mengqian Wang, Zimeng Li, Changwei Gong, Lin Tang, Xuhang Chen
https://arxiv.org/abs/2510.12084

Elevating Medical Image Security: A Cryptographic Framework Integrating Hyperchaotic Map and GRU
Chaotic systems play a key role in modern image encryption due to their sensitivity to initial conditions, ergodicity, and complex dynamics. However, many existing chaos-based encryption methods suffer from vulnerabilities, such as inadequate permutation and diffusion, and suboptimal pseudorandom properties. This paper presents Kun-IE, a novel encryption framework designed to address these issues. The framework features two key contributions: the development of the 2D Sin-Cos Pi Hyperchaotic Ma…

Repulsive Mixture Model with Projection Determinantal Point Process
Ziyi Song, Federico Camerlenghi, Weining Shen, Michele Guindani, Mario Beraha
https://arxiv.org/abs/2510.08838

Repulsive Mixture Model with Projection Determinantal Point Process
In many scientific domains, clustering aims to reveal interpretable latent structure that reflects relevant subpopulations or processes. Widely used Bayesian mixture models for model-based clustering often produce overlapping or redundant components because priors on cluster locations are specified independently, hindering interpretability. To mitigate this, repulsive priors have been proposed to encourage well-separated components, yet existing approaches face both computational and theoretica…

Integrable Model of a Superconductor with non-Fermi liquid and Mott Phases
Santhosh M, Jorge Dukelsky, Gerardo Ortiz
https://arxiv.org/abs/2510.10684 https://

Integrable Model of a Superconductor with non-Fermi liquid and Mott Phases
We present and analyze an exactly solvable interacting fermionic pairing model, which features interactions that entangle states at momenta $\mathbf{k}$ and $-\mathbf{k}$. These interactions give rise to novel correlated ground states, leading to a rich phase diagram that includes superconducting, multiple metallic, and Mott-insulating phases. At finite interaction strengths, we observe the emergence of multiple many-body Fermi surfaces, which violate Luttinger's theorem and challenge the conve…