Papers

Deep, long-form breakdowns of recent research in AI, machine learning, security & programming languages.

All ai machine learning cybersecurity scraping programming languages technology Important
machine learning Sep 17 rMuscle: Robotic Muscle Memory for Efficient Vision-Language-Action Model Inference by Kaijun Zhou, Zhiyang Li, Le Chen, Jinyu Gu ai Sep 17 Monitoring and Discovering Reward Hacking with Internal Representations during LLM Evaluations by Leon Bergen, Usha Bhalla, Andrew Lee, Barak Widawsky, Linas Nasvytis, Connor Watts, Siddharth Boppana, Sidharth Baskaran, Dron Hazra, Michael Byun, Atticus Geiger, Owen Lewis, Matthew Kowal, Vasudev Shyam, Thomas Fel, Thomas McGrath, Ekdeep Singh Lubana, Jack Merullo cybersecurity Sep 17 Characterizing Network Centralization and Observability in the Remote MCP Ecosystem by Muhammad Abdullah Sohail ai Sep 17 Evidence-Grounded Agentic Formulation Development in an Autonomous Laboratory by Michael M. Craig, Riley J. Hickman, Yingshan Ma, Rémi Piché-Taillefer, Christine Allen, Pauric Bannigan ai Sep 17 Prepared Or Unprepared? Evaluating Healthcare Workforce Readiness for Clinical Adoption of Artificial Intelligence in Nigeria by Abbas M. Rabiu, Abdulrazaq A. Zubair, Um-mulkhairi Ibrahim, Tolulope Olusuyi, Shaheeda Farouq, Safwan M. Dafi, Adaobi C. Emegoakor, Yewande Gbadamosi, Maruf Adewole programming languages Sep 17 Reporting Practice Matters: The Impact of Reference Choice on Chest X-ray Report Evaluation by Daniel P. Jeong, Charles Q. Li, Hossein Hosseiny, Nitya M. Bhalla, Fatma Uyar Morency, Pradeep Ravikumar, Zachary C. Lipton, Michael Oberst cybersecurity Sep 17 When Agents Look Like Beacons: NIDS Evasion by Model Context Protocol Traffic by Muhammad Abdullah Sohail ai Sep 17 MUSE: Benchmarking Large Vision-Language Models on Multi-Modal Understanding in Situated Education by Luyao Zhu, Xun Wei Yee, Wei Li, Mun Thye Mak, Wee Siong Ng machine learning Sep 17 Probabilistic Linear Explanations by Frederic Koriche, Jean-Marie Lagniez, Chi Tran machine learning Sep 17 Double descent is the principle of least action by Congzhou M Sha