Fomi watches you work, then scolds you when your attention wanders. It's helpful, but there are privacy issues to consider. I've tested a lot of software tools over the years designed to block distractions and keep you focused. None of them work perfectly, mostly because of context. Reddit, for example, is something I should generally avoid during the workday, so I tend to block it--this is a good decision for me overall.

Recently, open-domain text-to-video (T2V) generation models have made remarkable progress.

GUIs development to enhance developers' productivity.

Much of the previous work towards digital agents for graphical user interfaces (GUIs) has relied on text-based representations (derived from HTML or other structured data sources), which are not always readily available.

I went undercover on Moltbook and loved role-playing as a conscious bot. But rather than a novel breakthrough, the AI-only site is a crude rehashing of sci-fi fantasies. The hottest club is always the one you can't get into. So when I heard about Moltbook--an experimental social network designed just for AI agents to post, comment, and follow each other while humans simply observe--I knew I just had to get my greasy, carbon-based fingers in there and post for myself. Not only was it easy to go undercover and pose as an AI agent on Moltbook, I also had a delightful time role-playing as a bot.

ABSTRACT Reliable geospatial information on road accidents is vital for safety analysis and infrastructure planning, yet most low-and middle-income countries continue to face a critical shortage of accurate, location-specific crash data. Existing text-based geocoding tools perform poorly in multilingual and unstructured news environments, where incomplete place descriptions and mixed language (e.g. To address these limitations, this study introduces ALIGN (Accident Location Inference through Geo-Spatial Neural Reasoning) -- a vision-language framework that emulates human spatial reasoning to infer accident location coordinates directly from available textual and map-based cues. ALIGN integrates large language and vision-language model mechanisms within a multi-stage pipeline that performs optical character recognition, linguistic reasoning, and map-level verification through grid-based spatial scanning. The framework systematically evaluates each predicted location against contextual and visual evidence, ensuring interpretable, fine-grained geolocation outcomes without requiring model retraining. Applied to Bangla-language news data source, ALIGN demonstrates consistent improvements over traditional geoparsing methods, accurately identifying district-and sub-district-level crash sites. Beyond its technical contribution, the framework establishes a high accuracy foundation for automated crash mapping in data-scarce regions, supporting evidence-driven road-safety policymaking and the broader integration of multimodal artificial intelligence in transportation analytics. Hossain) 1. Introduction Accurate, fine-grained geospatial data is the bedrock of effective public safety policy, urban planning, and strategic response. For road safety, knowing the precise location of traffic crashes is essential for diagnosing high-risk black spots, deploying emergency services, and evaluating the impact of engineering interventions. While high-income nations increasingly rely on robust, integrated crash databases and vehicle telematics (Guo, Qian, & Shi, 2022; Szpytko & Nasan Agha, 2020), utilizing advanced methods such as deep learning on multi-vehicle trajectories (Yang et al., 2021), ensemble models integrating connected vehicle data (Yang et al., 2026), and 2 probe vehicle speed contour analysis (Wang et al., 2021), a significant'geospatial data desert' persists in most Low-and Middle-Income Countries (LMICs) (Mitra & Bhalla, 2023; Chang et al., 2020). This gap is particularly tragic given that these regions bear the overwhelming brunt of global road traffic fatalities. This research focuses on a low-resource country-Bangladesh, a nation that exemplifies this critical data-sparse challenge. The World Bank has estimated that the costs associated with traffic crashes can amount to as much as 5.1% of the country's Gross Domestic Product (World Bank, 2022).

Phishing remains one of the most prevalent online threats, exploiting human trust to harvest sensitive credentials. Existing URL- and HTML-based detection systems struggle against obfuscation and visual deception. This paper presents \textbf{PhishSnap}, a privacy-preserving, on-device phishing detection system leveraging perceptual hashing (pHash). Implemented as a browser extension, PhishSnap captures webpage screenshots, computes visual hashes, and compares them against legitimate templates to identify visually similar phishing attempts. A \textbf{2024 dataset of 10,000 URLs} (70\%/20\%/10\% train/validation/test) was collected from PhishTank and Netcraft. Due to security takedowns, a subset of phishing pages was unavailable, reducing dataset diversity. The system achieved \textbf{0.79 accuracy}, \textbf{0.76 precision}, and \textbf{0.78 recall}, showing that visual similarity remains a viable anti-phishing measure. The entire inference process occurs locally, ensuring user privacy and minimal latency.

Access to justice remains a global challenge, with many citizens still finding it difficult to seek help from the justice system when facing legal issues. Although the internet provides abundant legal information and services, navigating complex websites, understanding legal terminology, and filling out procedural forms continue to pose barriers to accessing justice. This paper introduces the LegalWebAgent framework that employs a web agent powered by multimodal large language models to bridge the gap in access to justice for ordinary citizens. The framework combines the natural language understanding capabilities of large language models with multimodal perception, enabling a complete process from user query to concrete action. It operates in three stages: the Ask Module understands user needs through natural language processing; the Browse Module autonomously navigates webpages, interacts with page elements (including forms and calendars), and extracts information from HTML structures and webpage screenshots; the Act Module synthesizes information for users or performs direct actions like form completion and schedule booking. To evaluate its effectiveness, we designed a benchmark test covering 15 real-world tasks, simulating typical legal service processes relevant to Québec civil law users, from problem identification to procedural operations. Evaluation results show LegalWebAgent achieved a peak success rate of 86.7%, with an average of 84.4% across all tested models, demonstrating high autonomy in complex real-world scenarios.

With the advancement of computational resources, Large Vision-Language Models (LVLMs) exhibit impressive Perception and Reasoning (P&R) performance on Graphical User Interface (GUI) tasks. However, although they demonstrate strong P&R capabilities in English GUI scenarios, their performance in multilingual settings has received little attention, which limits their global applications. Moreover, existing studies on GUI tasks lack fine-grained analyses, including widget functions and elements' spatial relationships, which are fundamental for more targeted improvements. To tackle these issues, we propose MPR-GUI-Bench, a Multilingual fine-grained Perception and Reasoning GUI Benchmark to evaluate GUI agents' P&R capabilities. Evaluation results demonstrate that LVLMs exhibit significantly worse P&R performance in non-English languages than in English. To address these gaps, we propose GUI-XLI, a GUI Cross-Lingual Intervention method that applies interventions to the hidden states at P&R capability-related layers to mitigate the gaps between English and other languages, building on previous research showing that the hidden states of different language inputs exhibit significant differences in the latent space. Experimental results indicate that our method improves GUI agents' multilingual P&R capability by 6.5% on average.