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 Our reviewer said this projector proves you don't need a normal TV anymore. Hannah Hoolihan is a freelance writer with Mashable. She's written for various entertainment websites since 2017, covering everything from tech to games to film. You'll currently find her work on IGN and Fangoria alongside Mashable, but she also has bylines at Rock Paper Shotgun, Collider, Screen Rant, and more. When she's not writing, she enjoys catching up with the latest films and shows -- horror, in particular -- and has a deep love of FromSoft games, which she continues to happily replay.

Look Up Say More Versus Creator Hub Switch Off Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Trending Now Safety Net In My Bag VidCon with Mashable Back to School Furtastic All Series 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. All products featured here are independently selected by our editors and writers. If you buy something through links on our site, Mashable may earn an affiliate commission. Deal pricing and availability subject to change after time of publication.

Look Up Say More Versus Creator Hub Switch Off Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Trending Now Safety Net In My Bag VidCon with Mashable Back to School Furtastic All Series Spend the rest of summer hosting outdoor movie nights and cozy fall evenings inside with your favorite flicks. Lauren Allain is a freelance journalist covering deals at Mashable. She graduated from Western Washington University with a B.A. in journalism and holds an M.B.A from Webster Leiden. You can find more of her work online from publications including Reader's Digest, U.S. News & World Report, Seattle Refined, and more. When she's not writing, Lauren prefers to be outside hiking, bouldering, swimming, or searching for the perfect location for all three.

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This remarkably vivid home projector has deep inky blacks, without a five-figure price tag. Premium home theater projectors often come at premium prices, usually north of $10,000. The Xgimi Titan Noir Max is different. It's priced at $5,999, well under five figures, but it supports IMAX Enhanced mode and has outstanding picture quality, a 10,000:1 native contrast ratio, 7,000 lumens brightness, and a unique dual intelligent iris system that makes on-the-fly adjustments. Even though Xgimi has been around for more than a decade and is known for affordable home cinema models, the company ran a Kickstarter campaign for the high-end Titan Noir Max home theater projector and raised $19 million.

Projectors are adopting RGB backlighting, the hottest tech from the world of TVs. This large but vivid projector is a great option for summer movie nights. Did not perform as well in a bright room. RGB backlighting is the hottest new tech in the world of televisions, bringing brighter and more colorful images to the world of LED screens. And now, projectors are following suit.

Embodied agents operating in smart homes must understand human behavior through diverse sensory inputs and communicate via natural language. While Vision-Language Models (VLMs) have enabled impressive language-grounded perception, their reliance on visual data limits robustness in real-world scenarios with occlusions, poor lighting, or privacy constraints. In this paper, we introduce HoloLLM, a Multimodal Large Language Model (MLLM) that integrates uncommon but powerful sensing modalities, such as LiDAR, infrared, mmWave radar, and WiFi, to enable seamless human perception and reasoning across heterogeneous environments. We address two key challenges: (1) the scarcity of aligned modalitytext data for rare sensors, and (2) the heterogeneity of their physical signal representations. To overcome these, we design a Universal Modality-Injection Projector (UMIP) that enhances pre-aligned modality embeddings with fine-grained, textaligned features from tailored encoders via coarse-to-fine cross-attention without introducing significant alignment overhead. We further introduce a human-VLM collaborative data curation pipeline to generate paired textual annotations for sensing datasets. Extensive experiments on two newly constructed benchmarks show that HoloLLM significantly outperforms existing MLLMs, improving languagegrounded human sensing accuracy by up to 30%. This work establishes a new foundation for real-world, language-informed multisensory embodied intelligence.

Test-time training (TTT) adapts a pretrained model to each prompt via parameter updates, improving accuracy under pretraining-to-test distribution shifts. Yet, its performance often suffers from instability and sensitivity to hyperparameters such as update steps and subspace. We explain this behavior through a decision-theoretic lens, treating TTT as implicit Bayesian inference in the kernel regime. Under a Gaussian process benchmark, we show that TTT reduces prediction error when updates are spectrally matched to the prompt's signal-to-noise ratio and aligned with query-relevant eigen-directions. This perspective underpins the following results: (1) we show when fixed update steps and subspaces fail under distribution shifts, motivating adaptive strategies; (2) we prove that selecting update steps via prompt evidence admits a PAC-Bayes guarantee against overfitting; and (3) we characterize the Bayes-optimal update subspace under a linear-Gaussian correction model, yielding a scoring rule for selecting Transformer blocks and heads. Our theory helps explain the empirical instability of TTT, taking a step toward principled guidance for when, how far, and which directions to adapt.

Achieving better alignment between vision embeddings and Large Language Models (LLMs) is crucial for enhancing the abilities of Multimodal LLMs (MLLMs), particularly for recent models that rely on powerful pretrained vision encoders and LLMs. A common approach to connect the pretrained vision encoder and LLM is through a projector applied after the vision encoder. However, the projector is often trained to enable the LLM to generate captions, and hence the mechanism by which LLMs understand each vision token remains unclear. In this work, we first investigate the role of the projector in compressing vision embeddings and aligning them with word embeddings. We show that the projector significantly compresses visual information, removing redundant details while preserving essential elements necessary for the LLM to understand visual content.