Online shopping giant Temu has agreed to work with the greeting card industry to remove copied designs from its site more quickly. Designers told the BBC the process for getting the plagiarised listings removed has been like the fairground game'whack-a-mole' with copied products re-appearing within days. Temu said protecting intellectual property was a top priority and that it was encouraging sellers to join the trial of a new takedown process specifically for the greetings card industry. Amanda Mountain, the co-founder of York-based Lola Design, discovered the catalogue of designs she had built up over a decade had nearly all been copied. She found the images she had created had been lifted and were being advertised by other sellers on cards and other products like t-shirts.

What Should Be in Your Emergency Kit Before Disaster Strikes? We consulted preparedness experts and WIRED's team of testers on the essential bug-out gear to keep your family safe during an unplanned exit. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. You never know when you're going to have to bug out on short notice.

Text embedding models play a cornerstone role in AI applications, such as retrieval-augmented generation (RAG). While general-purpose text embedding models demonstrate strong performance on generic retrieval benchmarks, their effectiveness diminishes when applied to private datasets (e.g., company-specific proprietary data), which often contain specialized terminology and lingo. In this work, we introduce BMEmbed, a novel method for adapting general-purpose text embedding models to private datasets. By leveraging the well-established keyword-based retrieval technique (BM25), we construct supervisory signals from the ranking of keyword-based retrieval results to facilitate model adaptation. We evaluate BMEmbed across a range of domains, datasets, and models, showing consistent improvements in retrieval performance. Moreover, we provide empirical insights into how BM25-based signals contribute to improving embeddings by fostering alignment and uniformity, highlighting the value of this approach in adapting models to domain-specific data. We release the source code available at https://github.com/BaileyWei/BMEmbed for the research community.

The £5.30 orange juice that tells the story of why supermarket prices are sky high There has been more than a bitter twang in the glasses at British breakfast tables. Only five years ago, a typical supermarket own-label carton of orange juice could be bought for 76p for 1 litre. One colleague was outraged to be sent a bill for £9 for a glass of hangover-busting orange juice and lemonade at an unassuming little restaurant in Kent. Asked why so much, she was told that the orange juice - albeit freshly squeezed - accounted for £5.30 of the price. Yet as costs have surged, the taste is changing too, with certain manufacturers substituting oranges for mandarins to cut costs.

This holiday season, more shoppers are expected to use chatbots to figure out what to buy. This holiday season, rather than searching on Google, more Americans will likely be turning to large language models to find gifts, deals, and sales. Retailers could see up to a 520 percent increase in traffic from chatbots and AI search engines this year compared to 2024, according to a recent shopping report from Adobe . OpenAI is already moving to capitalize on the trend: Last week, the ChatGPT maker announced a major partnership with Walmart that will allow users to buy goods directly within the chat window. As people start relying on chatbots to discover new products, retailers are having to rethink their approach to online marketing.

Simulating step-wise human behavior with Large Language Models (LLMs) has become an emerging research direction, enabling applications in various practical domains. While prior methods, including prompting, supervised fine-tuning (SFT), and reinforcement learning (RL), have shown promise in modeling step-wise behavior, they primarily learn a population-level policy without conditioning on a user's persona, yielding generic rather than personalized simulations. In this work, we pose a critical question: how can LLM agents better simulate personalized user behavior? We introduce Customer-R1, an RL-based method for personalized, step-wise user behavior simulation in online shopping environments. Our policy is conditioned on an explicit persona, and we optimize next-step rationale and action generation via action correctness reward signals. Experiments on the OPeRA dataset emonstrate that Customer-R1 not only significantly outperforms prompting and SFT-based baselines in next-action prediction tasks, but also better matches users' action distribution, indicating higher fidelity in personalized behavior simulation.

In just seven minutes, the thieves took off with crown jewels containing with thousands of diamonds along with other precious gems. Police stand outside the Louvre after a brazen theft. Could the French TV series have been prophetic? The show envisioned a heist at the Louvre, an event that became reality on the morning of October 19, when a group of professional thieves managed to break into the world-famous Paris museum . In just seven minutes, they stole a host of priceless French crown jewels.

Large language models (LLMs) are moving beyond static uses and are now powering agents that learn continually during their interaction with external environments. For example, agents can learn reusable skills while navigating web pages or toggling new tools. However, existing methods for skill learning often create skills that are over-specialized to a single website and fail to generalize. We introduce PolySkill, a new framework that enables agents to learn generalizable and compositional skills. The core idea, inspired by polymorphism in software engineering, is to decouple a skill's abstract goal (what it accomplishes) and its concrete implementation (how it is executed). Experiments show that our method (1) improves skill reuse by 1.7x on seen websites and (2) boosts success rates by up to 9.4% on Mind2Web and 13.9% on unseen websites, while reducing steps by over 20%. (3) In self-exploration settings without specified tasks, our framework improves the quality of proposed tasks and enables agents to learn generalizable skills that work across different sites. By enabling the agent to identify and refine its own goals, the PolySkill enhances the agent's ability to learn a better curriculum, leading to the acquisition of more generalizable skills compared to baseline methods. This work provides a practical path toward building agents capable of continual learning in adaptive environments. Our findings show that separating a skill's goal from its execution is a crucial step toward developing autonomous agents that can learn and generalize across the open web continuously.

All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. Fall is for nesting--and for feathering your nest with whatever will keep you sane during the winter. Which is why a number of retailers, including The Home Depot, drop prices on home goods with big fall deals. The Home Depot fall savings event for 2025 is unusually broad, because The Home Depot itself is unusually broad--the store that first brought the home improvement superstore nationwide.

Gear Home This 297-piece Kobalt Mechanics Tool Kit is just $99 at Lowe's with an included tool box This kit is typically $150, but it's just $99 at Lowe's, which makes it a fantastic gift for just about anyone. We may earn revenue from the products available on this page and participate in affiliate programs. I truly believe that a big tool kit with a dedicated carrying case is one of the best gifts you can give. It looks really impressive, it's useful for every type of person, and it's easy to wrap because it's usually rectangular (though, I recommend ditching wrapping paper this year). Right now, Lowe's has this 297-piece Mechanics Tool Set for just $99, which is a total sweet spot for gift buying.