Researchers are working on manipulating the digestive systems of wax worms to create a scalable way of disposing of plastic. Plastics that support modern life are inexpensive, strong, and versatile, but are difficult to dispose of and have a serious impact when released into the environment. Polyethylene, in particular, is the most widely produced plastic in the world, with more than 100 million tons distributed annually. Since it can take decades to decompose--and along the way can harm wildlife and degrade into harmful microplastics --its disposal is an urgent issue for mankind. In 2017, European researchers discovered a potential solution.

Onion CEO Ben Collins Hasn't Given Up on Print--or Buying Infowars A year after relaunching The Onion as a newspaper, Collins visits to talk about why "going into something and not ruining it is bravery." Ben Collins made a big bet. A year ago, just a few months after he'd been named CEO of The Onion, he relaunched its print edition. Once a favorite on university campuses, The Onion hadn't published a physical issue since 2013 . Common wisdom said that readership, and advertising dollars, just weren't there for newspapers. But Collins, a fan of the satirical paper since childhood, thought "that's dumb." Readers celebrated The Onion's relaunch and the ability to read all of its bitingly funny headlines on a single broadsheet. Collins wouldn't give exact numbers on how many people are currently subscribed to the print edition but did say they should be enough to keep its writers' room humming (a few weeks after we taped this episode, the Wall Street Journal reported that The Onion now boasts more than 53,000 paying subscribers). On this episode of, I spoke with Collins about his hopes for The Onion, the future of journalism, and his Balatro addiction. KATIE DRUMMOND: Do you have a recent favorite Onion headline? Can I look it up for you? "Ghislaine Maxwell Can't Help but Notice Interview Room Covered in Plastic Sheeting." The staff churns out like 15 a day that are great. I sit there, and I still don't know how they do it. When I say they throw away eight or nine of the best sentences I would ever write every day, I mean that sincerely.

Researchers are closer to equipping a dog-like robot to conduct science on the surface of Mars after five days of experiments this month at White Sands National Park in New Mexico. The national park is serving as a Mars analog environment and the scientists are conducting field test scenarios to inform future Mars operations with astronauts, dog-like robots known as quadruped robots, rovers and scientists at Mission Control on Earth. The work builds on similar experiments by the team with the same robot on the slopes of Mount Hood in Oregon, which simulated the landscape on the Moon. "Our group is very committed to putting quadrupeds on the Moon and on Mars," said Cristina Wilson, a robotics researcher in the College of Engineering at Oregon State University. "It's the next frontier and takes advantage of the unique capabilities of legged robots."

Australia has announced plans to ban apps used for stalking and creating deepfake nudes. Tech platforms will be responsible for preventing access to "nudify" and undetectable online stalking tools under the reforms announced on Tuesday by the Australian government. Minister for Communications Anika Wells said Australia would work with firms to stamp out "abhorrent technologies" while ensuring "legitimate and consent-based" artificial intelligence (AI) and online tracking services were not adversely affected. "Abusive technologies are widely and easily accessible and are causing real and irreparable damage now," Wells said in a statement. "These new, evolving, technologies require a new, proactive, approach to harm prevention – and we'll work closely with industry to achieve this." "While this move won't eliminate the problem of abusive technology in one fell swoop, alongside existing laws and our world-leading online safety reforms, it will make a real difference in protecting Australians," she added.

Gov. Kevin Stitt, R-Okla., spoke with Fox News Digital about becoming chair of the National Governors Association (NGA) and his legacy as Oklahoma governor. As America approaches its 250th anniversary, the bipartisan National Governors Association (NGA) is focused on reigniting the American Dream, NGA Chair Gov. Kevin Stitt, R-Okla., told Fox News Digital in an exclusive interview. "I've lived the American Dream," the Oklahoma governor said, explaining that Democratic and Republican governors "can all agree that we want to teach the next generation that America is exceptional, and that you can accomplish anything you set your mind to." During the NGA's summer meeting in Colorado Springs, Stitt announced his marquee initiative as the incoming chair, focusing on the economy, education and investing in artificial intelligence. "There's no such thing as equal outcomes, but we want equal opportunities to go chase your dreams through hard work, through entrepreneurship and free markets," Stitt explained.

We present QZhou-Embedding, a general-purpose contextual text embedding model with exceptional text representation capabilities. Built upon the Qwen2.5-7B-Instruct foundation model, we designed a unified multi-task framework comprising specialized data transformation and training strategies. The data transformation scheme enables the incorporation of more diverse textual training datasets, while the task-specific training strategies enhance model learning efficiency. We developed a data synthesis pipeline leveraging LLM API, incorporating techniques such as paraphrasing, augmentation, and hard negative example generation to improve the semantic richness and sample difficulty of the training set. Additionally, we employ a two-stage training strategy, comprising initial retrieval-focused pretraining followed by full-task fine-tuning, enabling the embedding model to extend its capabilities based on robust retrieval performance. Our model achieves state-of-the-art results on the MTEB and CMTEB benchmarks, ranking first on both leaderboards (August 27 2025), and simultaneously achieves state-of-the-art performance on tasks including reranking, clustering, etc. Our findings demonstrate that higher-quality, more diverse data is crucial for advancing retrieval model performance, and that leveraging LLMs generative capabilities can further optimize data quality for embedding model breakthroughs. Our model weights are released on HuggingFace under Apache 2.0 license. For reproducibility, we provide evaluation code and instructions on GitHub.

Access to diverse perspectives is essential for understanding real-world events, yet most news retrieval systems prioritize textual relevance, leading to redundant results and limited viewpoint exposure. We propose NEWSCOPE, a two-stage framework for diverse news retrieval that enhances event coverage by explicitly modeling semantic variation at the sentence level. The first stage retrieves topically relevant content using dense retrieval, while the second stage applies sentence-level clustering and diversity-aware re-ranking to surface complementary information. To evaluate retrieval diversity, we introduce three interpretable metrics, namely Average Pairwise Distance, Positive Cluster Coverage, and Information Density Ratio, and construct two paragraph-level benchmarks: LocalNews and DSGlobal. Experiments show that NEWSCOPE consistently outperforms strong baselines, achieving significantly higher diversity without compromising relevance. Our results demonstrate the effectiveness of fine-grained, interpretable modeling in mitigating redundancy and promoting comprehensive event understanding. The data and code are available at https://github.com/tangyixuan/NEWSCOPE.

Artificial intelligence (AI) is increasingly integrated into society, from financial services and traffic management to creative writing. Academic literature on the deployment of AI has mostly focused on the risks and harms that result from the use of AI. We introduce Fabric, a publicly available repository of deployed AI use cases to outline their governance mechanisms. Through semi-structured interviews with practitioners, we collect an initial set of 20 AI use cases. In addition, we co-design diagrams of the AI workflow with the practitioners. We discuss the oversight mechanisms and guardrails used in practice to safeguard AI use. The Fabric repository includes visual diagrams of AI use cases and descriptions of the deployed systems. Using the repository, we surface gaps in governance and find common patterns in human oversight of deployed AI systems. We intend for Fabric to serve as an extendable, evolving tool for researchers to study the effectiveness of AI governance.

Learning whole-body control for locomotion and arm motions in a single policy has challenges, as the two tasks have conflicting goals. For instance, efficient locomotion typically favors a horizontal base orientation, while end-effector tracking may benefit from base tilting to extend reachability. Additionally, current Reinforcement Learning (RL) approaches using a pose-based task specification lack the ability to directly control the end-effector velocity, making smoothly executing trajectories very challenging. To address these limitations, we propose an RL-based framework that allows for dynamic, velocity-aware whole-body end-effector control. Our method introduces a multi-critic actor architecture that decouples the reward signals for locomotion and manipulation, simplifying reward tuning and allowing the policy to resolve task conflicts more effectively. Furthermore, we design a twist-based end-effector task formulation that can track both discrete poses and motion trajectories. We validate our approach through a set of simulation and hardware experiments using a quadruped robot equipped with a robotic arm. The resulting controller can simultaneously walk and move its end-effector and shows emergent whole-body behaviors, where the base assists the arm in extending the workspace, despite a lack of explicit formulations. Videos and supplementary material can be found at multi-critic-locomanipulation.github.io.

Large Language Models (LLMs) increasingly shape political discourse, yet exhibit inconsistent responses when challenged. While prior research categorizes LLMs as left- or right-leaning based on single-prompt responses, a critical question remains: Do these classifications reflect stable ideologies or superficial mimicry? Existing methods cannot distinguish between genuine ideological alignment and performative text generation. To address this, we propose a framework for evaluating ideological depth through (1) argumentative consistency and (2) uncertainty quantification. Testing 12 LLMs on 19 economic policies from the Political Compass Test, we classify responses as stable or performative ideological positioning. Results show 95% of left-leaning models and 89% of right-leaning models demonstrate behavior consistent with our classifications across different experimental conditions. Furthermore, semantic entropy strongly validates our classifications (AUROC=0.78), revealing uncertainty's relationship to ideological consistency. Our findings demonstrate that ideological stability is topic-dependent and challenge the notion of monolithic LLM ideologies, and offer a robust way to distinguish genuine alignment from performative behavior.