Recent advancements in LLMs have contributed to the rise of advanced conversational assistants that can assist with user needs through natural language conversation. This paper presents a ScheduleMe, a multi-agent calendar assistant for users to manage google calendar events in natural language. The system uses a graph-structured coordination mechanism where a central supervisory agent supervises specialized task agents, allowing modularity, conflicts resolution, and context-aware interactions to resolve ambiguities and evaluate user commands. This approach sets an example of how structured reasoning and agent cooperation might convince operators to increase the usability and flexibility of personal calendar assistant tools.

Recent advances have enabled LLM-powered AI agents to autonomously execute complex tasks by combining language model reasoning with tools, memory, and web access. But can these systems be trusted to follow deployment policies in realistic environments, especially under attack? To investigate, we ran the largest public red-teaming competition to date, targeting 22 frontier AI agents across 44 realistic deployment scenarios. Participants submitted 1.8 million prompt-injection attacks, with over 60,000 successfully eliciting policy violations such as unauthorized data access, illicit financial actions, and regulatory noncompliance. We use these results to build the Agent Red Teaming (ART) benchmark - a curated set of high-impact attacks - and evaluate it across 19 state-of-the-art models. Nearly all agents exhibit policy violations for most behaviors within 10-100 queries, with high attack transferability across models and tasks. Importantly, we find limited correlation between agent robustness and model size, capability, or inference-time compute, suggesting that additional defenses are needed against adversarial misuse. Our findings highlight critical and persistent vulnerabilities in today's AI agents. By releasing the ART benchmark and accompanying evaluation framework, we aim to support more rigorous security assessment and drive progress toward safer agent deployment.

Today, at Apple's annual developers conference--where new software products are previewed in slick video presentations--the company finally joined the generative-AI race. The company introduced Apple Intelligence, a suite of AI features that will be rolled out to the tech giant's latest operating systems starting this fall. New generative-AI models will help Apple users write work memos and highly personalized text; create images and emoji; connect and organize photos, calendar events, and emails. The tools supposedly rely on the context of what's happening on your device: They'll be able to identify which contacts you are referencing and pull information from a range of apps. Apple offered a quintessentially Apple example in its marketing video: The senior vice president of software engineering, Craig Federighi, plays a busy dad who uses Apple Intelligence to figure out whether a last-minute meeting will conflict with his daughter's play.

The electronic calendar is a valuable resource nowadays for managing our daily life appointments or schedules, also known as events, ranging from professional to highly personal. Researchers have studied various types of calendar events to predict smartphone user behavior for incoming mobile communications. However, these studies typically do not take into account behavioral variations between individuals. In the real world, smartphone users can differ widely from each other in how they respond to incoming communications during their scheduled events. Moreover, an individual user may respond the incoming communications differently in different contexts subject to what type of event is scheduled in her personal calendar. Thus, a static calendar-based behavioral model for individual smartphone users does not necessarily reflect their behavior to the incoming communications. In this paper, we present a machine learning based context-aware model that is personalized and dynamically identifies individual's dominant behavior for their scheduled events using logged time-series smartphone data, and shortly name as ``CalBehav''. The experimental results based on real datasets from calendar and phone logs, show that this data-driven personalized model is more effective for intelligently managing the incoming mobile communications compared to existing calendar-based approaches.

Google's next voice-activated device could be a smart speaker with a screen. Leaked photos claim to show a smart display, called the'Google Home Hub,' which features a 7-inch display mounted on top of a speaker. The photos give credence to previous rumors that Google may soon launch its own smart display. Google's next voice-activated device could be a smart speaker with a screen. For all of 2017, 35 million smart speakers were shipped worldwide, Canalys data reveals.

With online calendar services gaining popularity worldwide, calendar data has become one of the richest context sources for understanding human behavior. However, event scheduling is still time-consuming even with the development of online calendars. Although machine learning based event scheduling models have automated scheduling processes to some extent, they often fail to understand subtle user preferences and complex calendar contexts with event titles written in natural language. In this paper, we propose Neural Event Scheduling Assistant (NESA) which learns user preferences and understands calendar contexts, directly from raw online calendars for fully automated and highly effective event scheduling. We leverage over 593K calendar events for NESA to learn scheduling personal events, and we further utilize NESA for multi-attendee event scheduling. NESA successfully incorporates deep neural networks such as Bidirectional Long Short-Term Memory, Convolutional Neural Network, and Highway Network for learning the preferences of each user and understanding calendar context based on natural languages. The experimental results show that NESA significantly outperforms previous baseline models in terms of various evaluation metrics on both personal and multi-attendee event scheduling tasks. Our qualitative analysis demonstrates the effectiveness of each layer in NESA and learned user preferences.

AI-powered intelligent assistant Alexa is now able to create reminders, Amazon announced today. Now anyone with an Alexa-enabled device can say "Alexa, remind me to stay woke in 10 minutes" or "Alexa, remind me to tell my dad I love him on June 18." Amazon also announced today that Echo or Echo Dot users can create countdown timers with custom names. All scheduled reminders and timers can be viewed in the Alerts and Alarms section section of Alexa app. The ability to create reminders is a longtime requested feature from Alexa fans, like adding calendar events used to be for Google Assistant on Google Home users until Google finally added that feature in May.

French startup Snips is launching its first product today, an iOS app that is supposed to make your life easier by letting you view and search your data more easily. Snips is a personal assistant that helps you know where you need to be and when. When you first launch the app, Snips wants you to grant access to your location, calendars, contacts, motion sensor and Gmail account. Then you're good to go. The main screen shows a tiny map at the top, a search bar in the middle and a list of events at the bottom.