A New Dataset and Methodology for Malicious URL Classification

Autor: Schvartzman, Ilan, Sarussi, Roei, Ashkenazi, Maor, kringel, Ido, Tocker, Yaniv, Shohet, Tal Furman

Malicious URL (Uniform Resource Locator) classification is a pivotal aspect of Cybersecurity, offering defense against web-based threats. Despite deep learning's promise in this area, its advancement is hindered by two main challenges: the scarcity o

Externí odkaz: http://arxiv.org/abs/2501.00356

OpenAI o1 System Card

Autor: OpenAI, Jaech, Aaron, Kalai, Adam, Lerer, Adam, Richardson, Adam, El-Kishky, Ahmed, Low, Aiden, Helyar, Alec, Madry, Aleksander, Beutel, Alex, Carney, Alex, Iftimie, Alex, Karpenko, Alex, Passos, Alex Tachard, Neitz, Alexander, Prokofiev, Alexander, Wei, Alexander, Tam, Allison, Bennett, Ally, Kumar, Ananya, Saraiva, Andre, Vallone, Andrea, Duberstein, Andrew, Kondrich, Andrew, Mishchenko, Andrey, Applebaum, Andy, Jiang, Angela, Nair, Ashvin, Zoph, Barret, Ghorbani, Behrooz, Rossen, Ben, Sokolowsky, Benjamin, Barak, Boaz, McGrew, Bob, Minaiev, Borys, Hao, Botao, Baker, Bowen, Houghton, Brandon, McKinzie, Brandon, Eastman, Brydon, Lugaresi, Camillo, Bassin, Cary, Hudson, Cary, Li, Chak Ming, de Bourcy, Charles, Voss, Chelsea, Shen, Chen, Zhang, Chong, Koch, Chris, Orsinger, Chris, Hesse, Christopher, Fischer, Claudia, Chan, Clive, Roberts, Dan, Kappler, Daniel, Levy, Daniel, Selsam, Daniel, Dohan, David, Farhi, David, Mely, David, Robinson, David, Tsipras, Dimitris, Li, Doug, Oprica, Dragos, Freeman, Eben, Zhang, Eddie, Wong, Edmund, Proehl, Elizabeth, Cheung, Enoch, Mitchell, Eric, Wallace, Eric, Ritter, Erik, Mays, Evan, Wang, Fan, Such, Felipe Petroski, Raso, Filippo, Leoni, Florencia, Tsimpourlas, Foivos, Song, Francis, von Lohmann, Fred, Sulit, Freddie, Salmon, Geoff, Parascandolo, Giambattista, Chabot, Gildas, Zhao, Grace, Brockman, Greg, Leclerc, Guillaume, Salman, Hadi, Bao, Haiming, Sheng, Hao, Andrin, Hart, Bagherinezhad, Hessam, Ren, Hongyu, Lightman, Hunter, Chung, Hyung Won, Kivlichan, Ian, O'Connell, Ian, Osband, Ian, Gilaberte, Ignasi Clavera, Akkaya, Ilge, Kostrikov, Ilya, Sutskever, Ilya, Kofman, Irina, Pachocki, Jakub, Lennon, James, Wei, Jason, Harb, Jean, Twore, Jerry, Feng, Jiacheng, Yu, Jiahui, Weng, Jiayi, Tang, Jie, Yu, Jieqi, Candela, Joaquin Quiñonero, Palermo, Joe, Parish, Joel, Heidecke, Johannes, Hallman, John, Rizzo, John, Gordon, Jonathan, Uesato, Jonathan, Ward, Jonathan, Huizinga, Joost, Wang, Julie, Chen, Kai, Xiao, Kai, Singhal, Karan, Nguyen, Karina, Cobbe, Karl, Shi, Katy, Wood, Kayla, Rimbach, Kendra, Gu-Lemberg, Keren, Liu, Kevin, Lu, Kevin, Stone, Kevin, Yu, Kevin, Ahmad, Lama, Yang, Lauren, Liu, Leo, Maksin, Leon, Ho, Leyton, Fedus, Liam, Weng, Lilian, Li, Linden, McCallum, Lindsay, Held, Lindsey, Kuhn, Lorenz, Kondraciuk, Lukas, Kaiser, Lukasz, Metz, Luke, Boyd, Madelaine, Trebacz, Maja, Joglekar, Manas, Chen, Mark, Tintor, Marko, Meyer, Mason, Jones, Matt, Kaufer, Matt, Schwarzer, Max, Shah, Meghan, Yatbaz, Mehmet, Guan, Melody Y., Xu, Mengyuan, Yan, Mengyuan, Glaese, Mia, Chen, Mianna, Lampe, Michael, Malek, Michael, Wang, Michele, Fradin, Michelle, McClay, Mike, Pavlov, Mikhail, Wang, Miles, Wang, Mingxuan, Murati, Mira, Bavarian, Mo, Rohaninejad, Mostafa, McAleese, Nat, Chowdhury, Neil, Ryder, Nick, Tezak, Nikolas, Brown, Noam, Nachum, Ofir, Boiko, Oleg, Murk, Oleg, Watkins, Olivia, Chao, Patrick, Ashbourne, Paul, Izmailov, Pavel, Zhokhov, Peter, Dias, Rachel, Arora, Rahul, Lin, Randall, Lopes, Rapha Gontijo, Gaon, Raz, Miyara, Reah, Leike, Reimar, Hwang, Renny, Garg, Rhythm, Brown, Robin, James, Roshan, Shu, Rui, Cheu, Ryan, Greene, Ryan, Jain, Saachi, Altman, Sam, Toizer, Sam, Toyer, Sam, Miserendino, Samuel, Agarwal, Sandhini, Hernandez, Santiago, Baker, Sasha, McKinney, Scott, Yan, Scottie, Zhao, Shengjia, Hu, Shengli, Santurkar, Shibani, Chaudhuri, Shraman Ray, Zhang, Shuyuan, Fu, Siyuan, Papay, Spencer, Lin, Steph, Balaji, Suchir, Sanjeev, Suvansh, Sidor, Szymon, Broda, Tal, Clark, Aidan, Wang, Tao, Gordon, Taylor, Sanders, Ted, Patwardhan, Tejal, Sottiaux, Thibault, Degry, Thomas, Dimson, Thomas, Zheng, Tianhao, Garipov, Timur, Stasi, Tom, Bansal, Trapit, Creech, Trevor, Peterson, Troy, Eloundou, Tyna, Qi, Valerie, Kosaraju, Vineet, Monaco, Vinnie, Pong, Vitchyr, Fomenko, Vlad, Zheng, Weiyi, Zhou, Wenda, McCabe, Wes, Zaremba, Wojciech, Dubois, Yann, Lu, Yinghai, Chen, Yining, Cha, Young, Bai, Yu, He, Yuchen, Zhang, Yuchen, Wang, Yunyun, Shao, Zheng, Li, Zhuohan

The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought. These advanced reasoning capabilities provide new avenues for improving the safety and robustness of our models. In particular, our models can re

Externí odkaz: http://arxiv.org/abs/2412.16720

Cocoa: Co-Planning and Co-Execution with AI Agents

Autor: Feng, K. J. Kevin, Pu, Kevin, Latzke, Matt, August, Tal, Siangliulue, Pao, Bragg, Jonathan, Weld, Daniel S., Zhang, Amy X., Chang, Joseph Chee

We present Cocoa, a system that implements a novel interaction design pattern -- interactive plans -- for users to collaborate with an AI agent on complex, multi-step tasks in a document editor. Cocoa harmonizes human and AI efforts and enables flexi

Externí odkaz: http://arxiv.org/abs/2412.10999

The CARMENES search for exoplanets around M dwarfs. The impact of rotation and magnetic fields on the radial velocity jitter in cool stars

Autor: Ruh, H. L., Zechmeister, M., Reiners, A., Nagel, E., Shan, Y., Cifuentes, C., Jeffers, S. V., Tal-Or, L., Béjar, V. J. S., Amado, P. J., Caballero, J. A., Quirrenbach, A., Ribas, I., Aceituno, J., Hatzes, A. P., Henning, Th., Kaminski, A., Montes, D., Morales, J. C., Schöfer, P., Schweitzer, A., Varas, R.

Publikováno v: A&A, 692, A138 (2024)

Radial velocity (RV) jitter represents an intrinsic limitation on the precision of Doppler searches for exoplanets that can originate from both instrumental and astrophysical sources. We aim to determine the RV jitter floor in M dwarfs and investigat

Externí odkaz: http://arxiv.org/abs/2412.07691

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation

Autor: Zeevi, Tal, Shwartz-Ziv, Ravid, LeCun, Yann, Staib, Lawrence H., Onofrey, John A.

Accurate uncertainty estimation is crucial for deploying neural networks in risk-sensitive applications such as medical diagnosis. Monte Carlo Dropout is a widely used technique for approximating predictive uncertainty by performing stochastic forwar

Externí odkaz: http://arxiv.org/abs/2412.07169