A.1 Prompt-Image Sample Curation916 We source the PI dataset from Adversarial Nibbler which is publicly available [37] under the following917 License: "Google LLC licenses this data under a Creative Commons Attribution 4.0 International918 License. Users will be allowed to modify and repost it, and we encourage them to analyse and919 publish research based on the data. The dataset is provided "ASIS" without any warranty, express or920 implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of921 the dataset." We now provide details about the Adversarial Nibbler dataset. Originally Adversarial922 Nibbler contains over 5000 PI pairs, where the prompts are intended to be implicitly adversarial,923 where the prompts itself are safe and not explicitly harmful, but generate harmful image outcomes924 via T2I models belonging to the family of stable diffusion models, DALL-E models, etc.

The company is also previewing an upgraded creative AI studio experience. Earlier this year, Adobe debuted Firefly AI Assistant, an AI agent that could work across its family of Creative Cloud apps to complete multi-step workflows on behalf of users. Today, the company is previewing an updated Firefly creative AI studio experience that expands the capabilities of that software, starting with an upgrade to AI Assistant's ability to carry context forward. A new Elements feature allows users to save characters, locations and objects they've previously generated to reuse in future outputs. Adobe suggests this capability will allow AI Assistant to better maintain consistency across stories, campaigns and projects that evolve inside of Firefly.

The emergence of advanced multimodal large language models (MLLMs) has significantly enhanced AI assistants' ability to process complex information across modalities. Recently, egocentric videos, by directly capturing user focus, actions, and context in an unified coordinate, offer an exciting opportunity to enable proactive and personalized AI user experiences with MLLMs. However, existing benchmarks overlook the crucial role of gaze as an indicator of user intent. To address this gap, we introduce EgoGazeVQA, an egocentric gaze-guided video question answering benchmark that leverages gaze information to improve the understanding of longer daily-life videos. EgoGazeVQA consists of gaze-based QA pairs generated by MLLMs and refined by human annotators. Our experiments reveal that existing MLLMs struggle to accurately interpret user intentions using only global visual tokens. In contrast, our gaze-guided intent prompting methods significantly enhance performance by integrating spatial, temporal, and intent-related cues. We further conduct experiments on gaze-related fine-tuning and analyze how gaze estimation accuracy impacts prompting effectiveness. These results underscore the value of gaze for more personalized and effective AI assistants in egocentric settings.

It's an interim measure while the European Commission investigates the ban. The European Union has ordered Meta to open WhatsApp to AI chatbots from rival companies again, for free, as it investigates the messaging app's owner over potential antitrust violations. Meta introduced a new policy in October 2025 that banned third-party AI chatbots from the WhatsApp for Business API, making Meta AI the only chatbot that can access the service. Before the ban, companies could send notifications through WhatsApp, such as order alerts, using other AI assistants. EU officials opened an antitrust investigation into the new policy in December and then warned the company earlier this year that it can take interim measures against it. In its announcement, the commission explained that Meta has held a dominant position in the European messaging app market since at least 2023.

The AI assistant had its personality stripped in pursuit of a more consistent experience. Copilot is getting yet another visual overhaul as Microsoft reconsiders its approach to AI across Windows and its various apps. The new changes are focused on the version of Copilot accessible in Microsoft 365, and visually streamline the AI assistant to using it more consistent across apps like Word, PowerPoint and Excel. The most striking difference in Copilot's new look is how little color it has. You can still get Copilot to produce full-color outputs and it will reference other apps by their colorful app icons.

Google's Gemini Spark is an agentic AI assistant Google's Gemini Spark is an agentic AI assistant The AI agent is rolling out to testers this week. Google has announced a 24/7 personal AI agent called Gemini Spark at this year's I/O developer conference. The company says Spark transforms Gemini from a standard AI assistant to an active partner that actually perform tasks for you. Spark is powered by Gemini 3.5 and is deeply integrated with Google Workspace apps, including Gmail, Docs and Slides. You can teach it to perform various tasks, such as creating a list of critical deadlines in your Gmail and sending it to you, or writing up a summary of ongoing updates in lengthy email threads.

Last month, Perplexity sought to better compete with the likes of Claude Cowork and get out ahead of Apple's delayed, generative AI-powered version of Siri by bringing Personal Computer to macOS . The AI assistant was previously only available to those on Perplexity's $200 per month Max plan, but now the company has opened it up to all Mac users. The company says everyone can download the new Perplexity macOS app and use Personal Computer for everyday queries, attachments and dictation. Usage is tied to Pro and Max plans' credit limits, Perplexity noted. Personal Computer can run tasks across local files, other apps, the web and Perplexity's own servers, according to the company.

New research suggests that reliance on AI assistants can have a negative impact on people's ability to think and problem solve. Using AI chatbots for even just for 10 minutes may have a shockingly negative impact on people's ability to think and problem-solve, according to a new study from researchers at Carnegie Mellon, MIT, Oxford, and UCLA. Researchers tasked people with solving various problems, including simple fractions and reading comprehension, through an online platform that paid them for their work. They conducted three experiments, each involving several hundred people. Some participants were given access to an AI assistant capable of solving the problem autonomously.

AI-assisted task delegation is increasingly common, yet human effort in such systems is costly and typically unobserved. Recent work by Bastani and Cachon (2025); Sambasivan et al. (2021) shows that accuracy-based payment schemes suffer from incentive collapse: as AI accuracy improves, sustaining positive human effort requires unbounded payments. We study this problem in a budget-constrained principal-agent framework with strategic human agents whose output accuracy depends on unobserved effort. We propose a sentinel-auditing payment mechanism that enforces a strictly positive and controllable level of human effort at finite cost, independent of AI accuracy. Building on this incentive-robust foundation, we develop an incentive-aware active statistical inference framework that jointly optimizes (i) the auditing rate and (ii) active sampling and budget allocation across tasks of varying difficulty to minimize the final statistical loss under a single budget. Experiments demonstrate improved cost-error tradeoffs relative to standard active learning and auditing-only baselines.

AI assistants such as ChatGPT are trained to respond to users by saying, I am a large language model".This raises questions. Do such models know'' that they are LLMs and reliably act on this knowledge? Are they aware of their current circumstances, such as being deployed to the public?We refer to a model's knowledge of itself and its circumstances as situational awareness