The securit y of cloud environments, such as Amazon Web Services (AWS), is complex and dynamic. St atic security policies have be come inadequate as threats evolve and cloud resources exhibit elasticity [1]. This paper addresses the limitations of static policies by proposing a security policy management framework that uses reinforcement learning (RL) to adapt dynamically. Specifically, we employ deep reinforcement learni ng algorithms, including deep Q Networks and proximal polic y op timization, enabling the learning and continuous adjustment of controls such as firewall rules and Identity an d Access Management (IAM) poli cies. The proposed RL based solution leverages cloud telemetry data (AWS Cloud Trail logs, network traffic data, threat intelligence feeds) to continuously refine security policies, maximizing threat mitigation, and compliance while minimizing resource impact. Experimental results d emonstrate that our adaptive RL bas ed framework significantly out performs static policies, achieving higher intrusion detection rates (92 % compared to 82% for static policies) and substantially reducing incident detection and response times by 58%. In a ddition, it maintains high con formity with security requirements and efficient resource usage. I. INTRODUCTION Cloud security is a critical concern as more orga nizations rely on cloud infras tructure. AWS an d other cloud platforms provide security configurations such as firewall rules and IAM policies, which are typically managed through static policies set by administrators. However, static policies cannot adapt to the dynamic nature of cloud environments, where workloads, users, and attack patterns change rapidly [1]. This rigidity exposes cloud deployments to new threats or misconfigurations that are not covered by static rules. For instance, static firewall rules may fail to detect novel attack patterns, and fixed IAM roles may become over privileged as resources scale, increasing risk . Problem Statement: Traditional cloud security policy management cannot keep pace with evolving threats and agile DevOps practices. M anual policy updates are error prone and slow.

Recent advances in generative AI, such as ChatGPT, have transformed access to information in education, knowledge-seeking, and everyday decision-making. However, in many developing regions, access remains a challenge due to the persistent digital divide. To help bridge this gap, we developed WaLLM - a custom AI chatbot over WhatsApp, a widely used communication platform in developing regions. Beyond answering queries, WaLLM offers several features to enhance user engagement: a daily top question, suggested follow-up questions, trending and recent queries, and a leaderboard-based reward system. Our service has been operational for over 6 months, amassing over 14.7K queries from approximately 100 users. In this paper, we present WaLLM's design and a systematic analysis of logs to understand user interactions. Our results show that 55% of user queries seek factual information. "Health and well-being" was the most popular topic (28%), including queries about nutrition and disease, suggesting users view WaLLM as a reliable source. Two-thirds of users' activity occurred within 24 hours of the daily top question. Users who accessed the "Leaderboard" interacted with WaLLM 3x as those who did not. We conclude by discussing implications for culture-based customization, user interface design, and appropriate calibration of users' trust in AI systems for developing regions.

Recent advancements in large language models (LLMs) have revolutionized their ability to handle single-turn tasks, yet real-world applications demand sophisticated multi-turn interactions. This survey provides a comprehensive review of recent advancements in evaluating and enhancing multi-turn interactions in LLMs. Focusing on task-specific scenarios, from instruction following in diverse domains such as math and coding to complex conversational engagements in roleplay, healthcare, education, and even adversarial jailbreak settings, we systematically examine the challenges of maintaining context, coherence, fairness, and responsiveness over prolonged dialogues. The paper organizes current benchmarks and datasets into coherent categories that reflect the evolving landscape of multi-turn dialogue evaluation. In addition, we review a range of enhancement methodologies under multi-turn settings, including model-centric strategies (contextual learning, supervised fine-tuning, reinforcement learning, and new architectures), external integration approaches (memory-augmented, retrieval-based methods, and knowledge graph), and agent-based techniques for collaborative interactions. Finally, we discuss open challenges and propose future directions for research to further advance the robustness and effectiveness of multi-turn interactions in LLMs. Related resources and papers are available at https://github.com/yubol-cmu/Awesome-Multi-Turn-LLMs.

Mamba extends earlier state space models (SSMs) by introducing input-dependent dynamics, and has demonstrated strong empirical performance across a range of domains, including language modeling, computer vision, and foundation models. However, a surprising weakness remains: despite being built on architectures designed for long-range dependencies, Mamba performs poorly on long-range sequential tasks. Understanding and addressing this gap is important for improving Mamba's universality and versatility. In this work, we analyze Mamba's limitations through three perspectives: expressiveness, inductive bias, and training stability. Our theoretical results show how Mamba falls short in each of these aspects compared to earlier SSMs such as S4D. To address these issues, we propose $\text{B}_2\text{S}_6$, a simple extension of Mamba's S6 unit that combines block-wise selective dynamics with a channel-specific bias. We prove that these changes equip the model with a better-suited inductive bias and improve its expressiveness and stability. Empirically, $\text{B}_2\text{S}_6$ outperforms S4 and S4D on Long-Range Arena (LRA) tasks while maintaining Mamba's performance on language modeling benchmarks.

We study online learning of feedforward neural networks with the sign activation function that implement functions from the unit ball in $\mathbb{R}^d$ to a finite label set $\{1, \ldots, Y\}$. First, we characterize a margin condition that is sufficient and in some cases necessary for online learnability of a neural network: Every neuron in the first hidden layer classifies all instances with some margin $γ$ bounded away from zero. Quantitatively, we prove that for any net, the optimal mistake bound is at most approximately $\mathtt{TS}(d,γ)$, which is the $(d,γ)$-totally-separable-packing number, a more restricted variation of the standard $(d,γ)$-packing number. We complement this result by constructing a net on which any learner makes $\mathtt{TS}(d,γ)$ many mistakes. We also give a quantitative lower bound of approximately $\mathtt{TS}(d,γ) \geq \max\{1/(γ\sqrt{d})^d, d\}$ when $γ\geq 1/2$, implying that for some nets and input sequences every learner will err for $\exp(d)$ many times, and that a dimension-free mistake bound is almost always impossible. To remedy this inevitable dependence on $d$, it is natural to seek additional natural restrictions to be placed on the network, so that the dependence on $d$ is removed. We study two such restrictions. The first is the multi-index model, in which the function computed by the net depends only on $k \ll d$ orthonormal directions. We prove a mistake bound of approximately $(1.5/γ)^{k + 2}$ in this model. The second is the extended margin assumption. In this setting, we assume that all neurons (in all layers) in the network classify every ingoing input from previous layer with margin $γ$ bounded away from zero. In this model, we prove a mistake bound of approximately $(\log Y)/ γ^{O(L)}$, where L is the depth of the network.

The RSF is trying to show that they don't need to reach Port Sudan by land in order to be able to have an impact there,

A chatbot developed by Elon Musk's multibillion-dollar artificial intelligence startup xAI appeared to be suffering from a glitch Wednesday when it repeatedly brought up white genocide in South Africa in response to user queries about unrelated topics on X. Grok, which competes with other chatbots like OpenAI's ChatGPT, is directly integrated into the social media platform that Musk also owns. Numerous examples of the phenomenon could be found by searching the official Grok profile for posts containing the term "boer," a word used to refer to people from South Africa of "Dutch, German, or Huguenot descent." It is sometimes used by Black South Africans as a pejorative against white Afrikaners, or people associated with the apartheid regime. In response to topics ranging from streaming platform HBO Max's name change to Medicaid cuts proposed by US lawmakers, the chatbot often seemed to initially stay on topic before veering back to white genocide in South Africa, completely unprompted. When asked to confirm the salary of Toronto Blue Jays player Max Scherzer, for example, the generative artificial intelligence chatbot launched into an explanation of white genocide and a controversial South African anti-apartheid song.

Three far-right extremists who amassed hundreds of weapons and planned to carry out attacks on targets including a mosque have been convicted of terrorism offences. Brogan Stewart, 25, from West Yorkshire, Christopher Ringrose, 34, from Staffordshire, and Marco Pitzettu, 25, from Derbyshire, were part of an online group who "idolised the Nazi regime". Sheffield Crown Court was told how Stewart had detailed torturing a Muslim leader using an "information extraction kit". All three were found guilty of terrorism offences at the same court on Wednesday and are due to be sentenced on 17 July.Counter Terrorism Policing North EastThe trio had amassed a cache of weapons as part of their planning During the nine-week trial, the court heard more than 200 weapons including machetes, hunting knives, swords and crossbows were found at their homes. Ringrose had also begun to build a 3D-printed semi-automatic firearm, which counter-terror police said would have been a "lethal weapon".

Early this year the Chinese company Deepseek revealed that it had developed a very powerful model mostly using Nvidia chips obtained before the Biden administration closed an export loophole in 2023, heightening the intensity of the race. Last week, the Trump administration ripped up those rules, with a spokesperson calling them "overly complex, bureaucratic" and saying they "would stymie American innovation." They then switched to a new tack: linking countries' access to AI chips with larger trade negotiations. Transitioning to a negotiation-based approach, the administration argued, could allow for more flexibility from country-to-country and allow Trump to secure key business concessions from Middle Eastern partners. Business and governments in the Middle East have massive ambitions for AI, aiming to position themselves at the forefront of this emerging technology.

The administration of U.S. President Donald Trump is clearing a path for two key Persian Gulf allies to pursue their artificial intelligence ambitions -- and some of the biggest U.S. tech companies are seizing on that opening with plans to spend billions of dollars in the region. Under agreements with the U.S. expected to be unveiled in coming days, Saudi Arabia and the United Arab Emirates are poised to win wider access to advanced AI chips from Nvidia and Advanced Micro Devices that are considered the gold standard for running AI models. The deals are taking shape while President Donald Trump visits the Middle East seeking to forge deeper business ties that put U.S. technology initiatives at center stage. Even before any formal announcement of accords between the U.S. and its partners, news began to emerge of American companies readying expanded projects in the region.