An In-Depth Analysis of Cyber Attacks in Secured Platformscs.AI updates on arXiv.org arXiv:2510.25470v1 Announce Type: cross
Abstract: There is an increase in global malware threats. To address this, an encryption-type ransomware has been introduced on the Android operating system. The challenges associated with malicious threats in phone use have become a pressing issue in mobile communication, disrupting user experiences and posing significant privacy threats. This study surveys commonly used machine learning techniques for detecting malicious threats in phones and examines their performance. The majority of past research focuses on customer feedback and reviews, with concerns that people might create false reviews to promote or devalue products and services for personal gain. Hence, the development of techniques for detecting malicious threats using machine learning has been a key focus. This paper presents a comprehensive comparative study of current research on the issue of malicious threats and methods for tackling these challenges. Nevertheless, a huge amount of information is required by these methods, presenting a challenge for developing robust, specialized automated anti-malware systems. This research describes the Android Applications dataset, and the accuracy of the techniques is measured using the accuracy levels of the metrics employed in this study.
arXiv:2510.25470v1 Announce Type: cross
Abstract: There is an increase in global malware threats. To address this, an encryption-type ransomware has been introduced on the Android operating system. The challenges associated with malicious threats in phone use have become a pressing issue in mobile communication, disrupting user experiences and posing significant privacy threats. This study surveys commonly used machine learning techniques for detecting malicious threats in phones and examines their performance. The majority of past research focuses on customer feedback and reviews, with concerns that people might create false reviews to promote or devalue products and services for personal gain. Hence, the development of techniques for detecting malicious threats using machine learning has been a key focus. This paper presents a comprehensive comparative study of current research on the issue of malicious threats and methods for tackling these challenges. Nevertheless, a huge amount of information is required by these methods, presenting a challenge for developing robust, specialized automated anti-malware systems. This research describes the Android Applications dataset, and the accuracy of the techniques is measured using the accuracy levels of the metrics employed in this study. Read More
Microsoft Releases Agent Lightning: A New AI Framework that Enables Reinforcement Learning (RL)-based Training of LLMs for Any AI AgentMarkTechPost How do you convert real agent traces into reinforcement learning RL transitions to improve policy LLMs without changing your existing agent stack? Microsoft AI team releases Agent Lightning to help optimize multi-agent systems. Agent Lightning is a open-sourced framework that makes reinforcement learning work for any AI agent without rewrites. It separates training from execution,
The post Microsoft Releases Agent Lightning: A New AI Framework that Enables Reinforcement Learning (RL)-based Training of LLMs for Any AI Agent appeared first on MarkTechPost.
How do you convert real agent traces into reinforcement learning RL transitions to improve policy LLMs without changing your existing agent stack? Microsoft AI team releases Agent Lightning to help optimize multi-agent systems. Agent Lightning is a open-sourced framework that makes reinforcement learning work for any AI agent without rewrites. It separates training from execution,
The post Microsoft Releases Agent Lightning: A New AI Framework that Enables Reinforcement Learning (RL)-based Training of LLMs for Any AI Agent appeared first on MarkTechPost. Read More
4 Techniques to Optimize Your LLM Prompts for Cost, Latency and PerformanceTowards Data Science Learn how to greatly improve the performance of your LLM application
The post 4 Techniques to Optimize Your LLM Prompts for Cost, Latency and Performance appeared first on Towards Data Science.
Learn how to greatly improve the performance of your LLM application
The post 4 Techniques to Optimize Your LLM Prompts for Cost, Latency and Performance appeared first on Towards Data Science. Read More
Bringing Vision-Language Intelligence to RAG with ColPaliTowards Data Science Unlocking the value of non-textual contents in your knowledge base
The post Bringing Vision-Language Intelligence to RAG with ColPali appeared first on Towards Data Science.
Unlocking the value of non-textual contents in your knowledge base
The post Bringing Vision-Language Intelligence to RAG with ColPali appeared first on Towards Data Science. Read More
Cursor 2.0 pivots to multi-agent AI coding, debuts Composer modelAI News Cursor has released its latest AI software development platform with a new multi-agent interface and the debut of its coding model, Composer. The new Composer model is described as a “frontier model”. Cursor claims it is four times faster than other models of similar intelligence. The company built it specifically for “low-latency agentic coding” within
The post Cursor 2.0 pivots to multi-agent AI coding, debuts Composer model appeared first on AI News.
Cursor has released its latest AI software development platform with a new multi-agent interface and the debut of its coding model, Composer. The new Composer model is described as a “frontier model”. Cursor claims it is four times faster than other models of similar intelligence. The company built it specifically for “low-latency agentic coding” within
The post Cursor 2.0 pivots to multi-agent AI coding, debuts Composer model appeared first on AI News. Read More
Generative AI Hype Check: Can It Really Transform SDLC?KDnuggets Gen AI is reshaping the software development lifecycle (SDLC). Faster coding, texting, and documentation. But the fundamental transformation happens when it’s combined with human expertise.
Gen AI is reshaping the software development lifecycle (SDLC). Faster coding, texting, and documentation. But the fundamental transformation happens when it’s combined with human expertise. Read More
Collecting Real-Time Data with APIs: A Hands-On Guide Using PythonKDnuggets In this article, we’ll break down the essentials of using APIs for data collection — why they matter, how they work, and how to get started with them in Python.
In this article, we’ll break down the essentials of using APIs for data collection — why they matter, how they work, and how to get started with them in Python. Read More
Accelerating discovery with the AI for Math InitiativeGoogle DeepMind Blog The initiative brings together some of the world’s most prestigious research institutions to pioneer the use of AI in mathematical research.
The initiative brings together some of the world’s most prestigious research institutions to pioneer the use of AI in mathematical research. Read More
The Cost of Robustness: Tighter Bounds on Parameter Complexity for Robust Memorization in ReLU Netscs.AI updates on arXiv.org arXiv:2510.24643v1 Announce Type: cross
Abstract: We study the parameter complexity of robust memorization for $mathrm{ReLU}$ networks: the number of parameters required to interpolate any given dataset with $epsilon$-separation between differently labeled points, while ensuring predictions remain consistent within a $mu$-ball around each training sample. We establish upper and lower bounds on the parameter count as a function of the robustness ratio $rho = mu / epsilon$. Unlike prior work, we provide a fine-grained analysis across the entire range $rho in (0,1)$ and obtain tighter upper and lower bounds that improve upon existing results. Our findings reveal that the parameter complexity of robust memorization matches that of non-robust memorization when $rho$ is small, but grows with increasing $rho$.
arXiv:2510.24643v1 Announce Type: cross
Abstract: We study the parameter complexity of robust memorization for $mathrm{ReLU}$ networks: the number of parameters required to interpolate any given dataset with $epsilon$-separation between differently labeled points, while ensuring predictions remain consistent within a $mu$-ball around each training sample. We establish upper and lower bounds on the parameter count as a function of the robustness ratio $rho = mu / epsilon$. Unlike prior work, we provide a fine-grained analysis across the entire range $rho in (0,1)$ and obtain tighter upper and lower bounds that improve upon existing results. Our findings reveal that the parameter complexity of robust memorization matches that of non-robust memorization when $rho$ is small, but grows with increasing $rho$. Read More
Charting the European LLM Benchmarking Landscape: A New Taxonomy and a Set of Best Practicescs.AI updates on arXiv.org arXiv:2510.24450v1 Announce Type: cross
Abstract: While new benchmarks for large language models (LLMs) are being developed continuously to catch up with the growing capabilities of new models and AI in general, using and evaluating LLMs in non-English languages remains a little-charted landscape. We give a concise overview of recent developments in LLM benchmarking, and then propose a new taxonomy for the categorization of benchmarks that is tailored to multilingual or non-English use scenarios. We further propose a set of best practices and quality standards that could lead to a more coordinated development of benchmarks for European languages. Among other recommendations, we advocate for a higher language and culture sensitivity of evaluation methods.
arXiv:2510.24450v1 Announce Type: cross
Abstract: While new benchmarks for large language models (LLMs) are being developed continuously to catch up with the growing capabilities of new models and AI in general, using and evaluating LLMs in non-English languages remains a little-charted landscape. We give a concise overview of recent developments in LLM benchmarking, and then propose a new taxonomy for the categorization of benchmarks that is tailored to multilingual or non-English use scenarios. We further propose a set of best practices and quality standards that could lead to a more coordinated development of benchmarks for European languages. Among other recommendations, we advocate for a higher language and culture sensitivity of evaluation methods. Read More