Dreame's robot vacuum with an arm is back at CES 2026 and it can do more than pick up shoes The Cyber 10 Ultra has its own attachments for reaching hard-to-get places. Dreame's Cyber10 Ultra has an arm that can grab objects and clean hard to reach areas. Last year at CES, Dreame showed off a robot vacuum prototype with a mechanical arm . But while we were able to see the arm extend and retract, we didn't see the device, which was described as a prototype at the time, actually grab anything, which was a bit disappointing. This year, though, the company has made its arm-enabled vacuum a reality with the Cyber 10 Ultra.

From robot vacuums to Dyson stick vacs, don't miss these Cyber Monday vacuum deals happening on our favorite vacuums in every category. There's a lot of shopping you can do this weekend, but these Cyber Monday vacuum deals are ones you shouldn't skip if you need any kind of cleaning upgrade. Changing to a stick vacuum hugely improved my life after moving to a three-story home, but all kinds of vacuum types can turn your life around. Never have time to vacuum or mop? You'll want a handheld vacuum .

A key ingredient to the recent breakthroughs in language modeling has been the availability of large-scale datasets.

Knowledge graph-based dialogue generation (KG-DG) is a challenging task requiring models to effectively incorporate external knowledge into conversational responses. While large language models (LLMs) have achieved impressive results across various NLP tasks, their ability to utilize external knowledge in KG-DG remains under-explored. We observe that LLMs often rely on internal knowledge, leading to detachment from provided knowledge graphs, even when they are given a flawlessly retrieved knowledge graph. First, we introduce LLM-KAT, an evaluation procedure for measuring knowledge attachment in generated responses. Second, we propose a simple yet effective entity anonymization technique to encourage LLMs to better leverage external knowledge. Experiments on the OpenDialKG dataset demonstrate that our approach improves LLMs' attachment on external knowledge.

We thank the reviewers for their thoughtful comments. Below we try to provide as many of them as we could accomplish within the tight time frame. R1, R3, R4: Where does the performance gain come from--the transition system or the GNN? R4: Does the choice of GNNs matter? The 3 most frequent categories are "PP Attachment", Compared to Mrini et al., our method has more "PP Attachment" (342 vs. 320) R1, R3: Compare with existing parsers in terms of efficiency. About 50% of the time is spent on the XLNet encoder, which is the same computation for all three methods.

Phishing emails continue to pose a significant threat to cybersecurity by exploiting human vulnerabilities through deceptive content and malicious payloads. While Machine Learning (ML) models are effective at detecting phishing threats, their performance largely relies on the quality and diversity of the training data. This paper presents MeAJOR (Merged email Assets from Joint Open-source Repositories) Corpus, a novel, multi-source phishing email dataset designed to overcome critical limitations in existing resources. It integrates 135894 samples representing a broad number of phishing tactics and legitimate emails, with a wide spectrum of engineered features. We evaluated the dataset's utility for phishing detection research through systematic experiments with four classification models (RF, XGB, MLP, and CNN) across multiple feature configurations. Results highlight the dataset's effectiveness, achieving 98.34% F1 with XGB. By integrating broad features from multiple categories, our dataset provides a reusable and consistent resource, while addressing common challenges like class imbalance, generalisability and reproducibility.

"What does it really mean to upload your consciousness into intangible space?" In, the characters face an impossible choice: upload your mind into a virtual utopia, or crumble away in the abandoned physical world. Mind-uploading is familiar to us as a science fiction trope, often anchoring relationship dramas and philosophical inquiry. But what does it really mean to upload your consciousness into intangible space? Can the mechanics be extrapolated from our present-day science?

Looking for all our top recommended vacuums? Here are our favorites in every style we've tested, from stick vacs to robot vacuums. 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. We've tried every kind of vacuum here at WIRED, and we've put together this list noting the best vacuum cleaner for every category we've tried.

For years, Paul Kingsnorth was one of the most visible members of the green movement. Then he walked away from it. Now he wants us to walk away from everything else. For Kingsnorth, the Industrial Revolution marked the point of no return, the moment when we decided to play gods and turn our backs on the Earth. In 2014, Paul Kingsnorth was sunk in doubt. He was forty-one and had been on the green movement's front lines since the nineteen-nineties--working for Greenpeace and EarthAction, chaining himself to a bridge, getting tear-gassed outside a G-8 summit.

We analyze the syntactic sensitivity of Text-to-Speech (TTS) systems using methods inspired by psycholinguistic research. Specifically, we focus on the generation of intonational phrase boundaries, which can often be predicted by identifying syntactic boundaries within a sentence. We find that TTS systems struggle to accurately generate intonational phrase boundaries in sentences where syntactic boundaries are ambiguous (e.g., garden path sentences or sentences with attachment ambiguity). In these cases, systems need superficial cues such as commas to place boundaries at the correct positions. In contrast, for sentences with simpler syntactic structures, we find that systems do incorporate syntactic cues beyond surface markers. Finally, we finetune models on sentences without commas at the syntactic boundary positions, encouraging them to focus on more subtle linguistic cues. Our findings indicate that this leads to more distinct intonation patterns that better reflect the underlying structure.