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This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG . Survivor's Christian Hubicki marvels at China's robotics games records: 'Willing to break the robots to do it' Chinese humanoid robot breaks Usain Bolt's 100m dash record at 9.39 seconds'CHUMP CHANGE': How a $17B settlement is a landmark for social, still good for Meta Kurt'CyberGuy' Knutsson recalls being stuck in New Orleans during Hurricane Katrina Cybercrime a global problem, thieves go for'targets of opportunity,' expert says Steve Doocy explores the US Air Force Academy's elite military training Supporting data centers is'perilous' politically: Brian Kilmeade Fox News Flash top headlines are here. Check out what's clicking on FoxNews.com.

Trending Now Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Say More Mashable Selects Mashable Voices Safety Net Creator Hub Versus Gift Ideas For Everyone On Your List Switch Off In My Bag All Series Haley Henschel is a Chicago-based Senior Shopping Reporter at Mashable who reviews and finds deals on popular tech, from laptops to gaming consoles and VPNs. She has years of experience covering shopping holidays and can tell you what's actually worth buying on Black Friday and Amazon Prime Day. Her work has also explored the driving forces behind digital trends within the shopping sphere, from dupes to 12-foot skeletons . Christina Buff is a Nashville-based freelance writer for who covers shopping with a splash of entertainment. If you're ever wondering what streaming service you need to watch something (and the cheapest way to sign up for it), she's your girl.

I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen Opinion: Comcast's new motion detection technology could benefit your home, but it also raises privacy concerns. The details are in the fine print. Comcast has introduced a new opt-in motion-detection feature in its routers. Xfinity Gateway devices detect movement and alert users via a mobile app. A footnote advisory indicates law enforcement could access your motion data.

Versus Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Say More Creator Hub Gift Ideas For Everyone On Your List Mashable Selects Switch Off Trending Now Safety Net In My Bag VidCon with Mashable All Series Expect more banned products in the future, experts warn. Chase joined Mashable's Social Good team in 2020, covering online stories about digital activism, climate justice, accessibility, and media representation. Will you still be able to buy robots in the U.S.? Kind of. Do you know where the robots running your home -- your Google Nest devices, AI-powered smart assistants, and, especially, your robovacuums -- come from? Well, the American government certainly wants you to.

I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen Wi-Fi 7 adoption in the US quadrupled in a year - is it time to upgrade? Wi Fi 7 is rapidly moving out of the lab and into American living rooms, with data from Ookla showing US usage is growing fast. Wi-Fi 7 is finally becoming popular. With that trend, overall internet speeds are picking up. You can now buy Wi-Fi 7 routers for reasonable prices.

Large language model (LLM) routers improve the efficiency of multi-model systems by directing each query to the most appropriate model while leveraging the diverse strengths of heterogeneous LLMs. Most existing approaches frame routing as a classification problem based solely on the input query. While this reduces overhead by avoiding inference across all models, it overlooks valuable information that could be gleaned from potential outputs and fails to capture implicit intent or contextual nuances that often emerge only during response generation. These limitations can result in suboptimal routing decisions, particularly for complex or ambiguous queries that require deeper semantic understanding. To address this challenge, we propose Lookahead, a routing framework that "foresees" potential model outputs by predicting their latent representations and uses these predictions to guide model selection, thus enabling more informed routing without full inference. Within this framework, we implement two approaches based on causal and masked language models. Empirical evaluations across seven public benchmarks--spanning instruction following, mathematical reasoning, and code generation--show that Lookahead consistently outperforms existing routing baselines, achieving an average performance gain of 7.7% over the state-of-the-art.

Mixture of Experts (MoE) pretraining is more scalable than dense Transformer pretraining, because MoEs learn to route inputs to a sparse set of their feedforward parameters. However, this means that MoEs only receive a sparse backward update, leading to training instability and suboptimal performance. We present a lightweight approximation method that gives the MoE router a dense gradient update while continuing to sparsely activate its parameters. Our method, which we refer to as Default MoE, substitutes missing expert activations with default outputs consisting of an exponential moving average of expert outputs previously seen over the course of training. This allows the router to receive signals from every expert for each token, leading to significant improvements in training performance. Our Default MoE outperforms standard TopK routing in a variety of settings without requiring significant computational overhead.

We introduce FLEXOLMO, a new class of language models (LMs) that supports (1) distributed training without data sharing, where different model parameters are independently trained on closed datasets, and (2) data-flexible inference, where these parameters along with their associated data can be flexibly included or excluded from model inferences with no further training. FLEXOLMO employs a mixture-of-experts (MoE) architecture where each expert is trained independently on closed datasets and later integrated through a new domain-informed routing without any joint training. FLEXOLMO is trained on FLEXMIX, a corpus we curate comprising publicly available datasets alongside seven domain-specific sets, representing realistic approximations of closed sets. We evaluate models with up to 37 billion parameters (20 billion active) on 31 diverse downstream tasks. We show that a general expert trained on public data can be effectively combined with independently trained experts from other data owners, leading to an average 41% relative improvement while allowing users to opt out of certain data based on data licensing or permission requirements. Our approach also outperforms prior model merging methods by 10.1% on average and surpasses the standard MoE trained without data restrictions using the same training FLOPs. Altogether, this research presents a solution for both data owners and researchers in regulated industries with sensitive or protected data. FLEXOLMO enables benefiting from closed data while respecting data owners' preferences by keeping their data local and supporting fine-grained control of data access during inference.

We study cost-aware routing for large language models across diverse and dynamic pools of models. Existing approaches often overlook prompt-specific context, rely on expensive model profiling, assume a fixed set of experts, or use inefficient trialand-error strategies. We introduce Cost-Spectrum Contrastive Routing (CSCR), a lightweight framework that maps both prompts and models into a shared embedding space to enable fast, cost-sensitive selection. CSCR uses compact, fast-to-compute logit footprints for open-source models and perplexity fingerprints for black-box APIs. A contrastive encoder is trained to favor the cheapest accurate expert within adaptive cost bands. At inference time, routing reduces to a single k-NN lookup via a FAISS index, requiring no retraining when the expert pool changes and enabling microsecond latency. Across multiple benchmarks, CSCR consistently outperforms baselines, improving the accuracy-cost tradeoff by up to 25%, while generalizing robustly to unseen LLMs and out-of-distribution prompts.