PCWorld reports that Microsoft used its Bing Wallpaper app to display full-screen ads promoting Harry Potter and Fantastic Beasts movie collections on Windows 11 desktops. The ads appear randomly based on user region, exploiting small print that allows Microsoft to show advertisements within wallpapers meant for scenic daily backgrounds. Reddit users expressed strong disapproval, especially given that Microsoft previously acknowledged showing too many ads to its users. Microsoft recently caused a stir among some users with an ad that fills the entire Windows 11 desktop background. It's a wallpaper that ends up on the computer via the Bing Wallpaper app.

Claude Cowork and Claude Code can now perform background tasks (clicking, typing, and opening apps) while you do your own stuff.

Will ran game benchmarks without OBS running, then with OBS on different setups to see what kind of effect it had on your game's framerates. Long story short: Across a variety of hardware setups with the hugely powerful RTX 5090, from mid-range to high-powered CPUs, you're going to see a predictable drop of about 10-15 percent while running an OBS stream, even without other tools.

Welcome to our latest roundup of what's going on in the indie game space. Gamescom is fast approaching, but there are still lots of new games you can check out before the tidal wave of announcements from that event next week. We'll take a peek at some games that are a little further out as well. Before we dive in, let's take a look at some news from the last few days. Ternox Games says PlayStation is blocking a PS5 version of its stock market game, STONKS-9800, which debuted on Steam in early access in 2023.

Android has had the advantage when it comes to changing your message wallpaper - but there is an easy way to do it in iOS 26, too, as long as you don't mind one thing.

What if the Universe Isn't as Uniform as Scientists Think? A study based on 47 million galaxies found that the cosmic web retains patterns on enormous scales, which could force a reevaluation of a pillar of cosmology. One of the fundamental pillars of modern cosmology may be beginning to wobble. A study published in Nature has found evidence that the universe may not behave the same way in every direction on the largest observable scales. "What we found is a network of enormous filaments and walls of galaxies that remain aligned and interconnected across billions of light-years," says Francesco Sylos Labini, research director of physics at the Enrico Fermi Research Center in Italy and the study's lead author. What Should the Universe Look Like?

Adversarial input attacks can cause a significant shift of CLIP embeddings. This can affect the downstream robustness of models incorporating CLIP in the pipeline, such as text-to-image generative models or large vision language models. While some efforts have been done towards making the CLIP image encoders robust, the robustness of text encoders remains unexplored. In this work, we cover this gap in the literature. We propose LEAF: an efficient adversarial finetuning method for the text domain, with the ability to scale to large CLIP models. Our models significantly improve the zero-shot adversarial accuracy in the text domain, while maintaining the vision performance provided by robust image encoders. When combined with text-to-image diffusion models, we can improve the generation quality under adversarial noise. In multimodal retrieval tasks, LEAF improves the recall under adversarial noise over standard CLIP models. Finally, we show that robust text encoders facilitate better reconstruction of input text from its embedding via direct optimization.

This paper investigates the occlusion problems in virtual try-on (VTON) tasks. According to how they affect the try-on results, the occlusion issues of existing VTON methods can be grouped into two categories: (1) Inherent Occlusions, which are the ghosts of the clothing from reference input images that exist in the try-on results.

Multimodal large language models have unlocked new possibilities for various multimodal tasks. However, their potential in image manipulation detection remains unexplored. When directly applied to the IMD task, M-LLMs often produce reasoning texts that suffer from hallucinations and overthinking. To address this, we propose ForgerySleuth, which leverages M-LLMs to perform comprehensive clue fusion and generate segmentation outputs indicating specific regions that are tampered with. Moreover, we construct the ForgeryAnalysis dataset through the Chain-of-Clues prompt, which includes analysis and reasoning text to upgrade the image manipulation detection task. A data engine is also introduced to build a largerscale dataset for the pre-training phase. Our extensive experiments demonstrate the effectiveness of ForgeryAnalysis and show that ForgerySleuth significantly outperforms existing methods in generalization, robustness, and explainability.

Object-context shortcuts remain a persistent challenge in vision-language models, undermining zero-shot reliability when test-time scenes differ from familiar training co-occurrences. We recast this issue as a causal inference problem and ask: Would the prediction remain if the object appeared in a different environment? To answer this at inference time, we estimate object and background expectations within CLIP's representation space, and synthesize counterfactual embeddings by recombining object features with diverse alternative contexts sampled from external datasets, batch neighbors, or text-derived descriptions. By estimating the Total Direct Effect and simulating intervention, we further subtract background-only activation, preserving beneficial object-context interactions while mitigating hallucinated scores. Without retraining or prompt design, our method substantially improves both worst-group and average accuracy on context-sensitive benchmarks, establishing a new zero-shot state of the art. Beyond performance, our framework provides a lightweight representation-level counterfactual approach, offering a practical causal avenue for debiased and reliable multimodal reasoning. The implementation is available at https://github.com/peipeng98.