Tech has been encroaching on the family domicile for years--but actor, writer, and satirist Jill Kargman is all in on analog. My husband Harry works in tech, and every January he makes his yearly pilgrimage to Consumer Electronics Show (CES) in Las Vegas, where some 4,100 exhibitors are spread across 2.6 million square feet. The dominant concept at this year's edition was that, very soon, anything you put in your house will be compatible with voice-activated AI services like Siri, Alexa, or HomePod. Your newest home automation systems will come equipped with sensors and jazzy master controls on an iPad. The problem for me is that a tiny photoelectric cell you frantically wave to--rather than a switch to flick or press--rarely acknowledges me, because somehow I'm not human temperature.

Length generalization--the ability to solve problems longer than those seen during training--remains a critical challenge for large language models (LLMs). Previous work modifies positional encodings (PEs) and data formats to improve length generalization on specific symbolic tasks such as addition and sorting. However, these approaches are fundamentally limited to special tasks, often degrading general language performance. Furthermore, they are typically evaluated on small transformers trained from scratch on single tasks and can cause performance drop when applied during post-training stage of practical LLMs with general capabilities. Hu et al. [19] proposed Rule-Following Fine-Tuning (RFFT) to improve length generalization in the post-training stage of LLMs.

The whale sustained some injuries during the ordeal, but should recover. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. View of the whale after being freed. Note the red wounds from its most recent entanglement near the tail and the deep, but healing wound, near its head from a prior entanglement. Center for Coastal Studies image, taken under NOAA permit 24359.

Understanding architectural differences in language models is challenging, especially at academic-scale pretraining (e.g., 1.3B parameters, 100B tokens), where results are often dominated by noise and randomness. To overcome this, we introduce controlled synthetic pretraining tasks that isolate and evaluate core model capabilities. Within this framework, we discover Canon layers: lightweight architectural components--named after the musical term "canon"--that promote horizontal information flow across neighboring tokens. Canon layers compute weighted sums of nearby token representations and integrate seamlessly into Transformers, linear attention, state-space models, or any sequence architecture.

Speculative decoding generally dictates having a small, efficient draft model that is either pretrained or distilled offline to a particular target model series, for instance, Llama or Qwen models. However, within online deployment settings, there are two major challenges: 1) usage of a target model that is incompatible with the draft model; 2) expectation of latency improvements over usage and time. In this work, we propose OmniDraft, a unified framework that enables a single draft model to operate with any target model and adapt dynamically to user data. We introduce an online n-gram cache with hybrid distillation fine-tuning to address the cross-vocabulary mismatch across draft and target models; and further improve decoding speed by leveraging adaptive drafting techniques. OmniDraft is particularly suitable for on-device LLM applications where model cost, efficiency and user customization are the major points of contention. This further highlights the need to tackle the above challenges and motivates the "one drafter for all" paradigm.

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If courts want to reduce harm, they must focus on product design choices, measurable safety outcomes, and governance, write Peter Chapman, Ravi Iyer, and Meetali Jain.

TV star mom, 46, who appeared on'quitting everything to change your life' show died in fire at luxury Caribbean beach resort that sent 1,700 tourists running for their lives Furious Trump hits back at Italian Prime Minister Meloni and gives her unusual'nickname' as their photo feud ramps up The'marry me' sex move that'll make even the most commitment-phobic of men beg to see you again... and it worked for THREE of my friends Take the 10-second finger exercise that may reveal your risk of dementia... and even protect against it'It feels like emotional blackmail': As Harry and Meghan announce return to Britain with Archie and Lilibet, insiders reveal fears about decision to bring children and'manipulation' of Royals The four mistakes that led to bungee tragedy on Skeleton Bridge: FRED KELLY saw the scene for himself, now he retraces the prelude to disaster. So was it really an accident? Dua Lipa stuns in a bespoke Chanel bridal gown and parties into the early hours as she shares the first pictures from her £1.5million Little-known penis condition that SHORTENS manhood: Shockingly, 1 in 10 men have it... but most miss the signs until it's too late to reverse with easy cure: DR PETAR BAJIC World Cup commentator denies making racist comment about Ciara live on air during USA's win over Australia Harrowing chain of events behind The Ring star's death at just 35 laid bare by doctors in agonizing detail... and how it could have been prevented Jelly Roll reveals divorce'plot twist' as he posts footage of post-split phone call with Bunnie XO Lindsey Vonn shows off remarkable progress in gym workout just four months after horrific injury: 'Makes me so happy' Taylor Swift's bombshell reconciliation phone call with Blake Lively: Insiders reveal every detail of wedding invite'olive branch' literally no one saw coming... and the actress has a dress picked out! Swedish actress, 81, was in TWO James Bond movies and also worked with Charlton Heston, who is she?

Predicting changes in binding free energy ( G) is essential for understanding protein-protein interactions, which are critical in drug design and protein engineering. However, existing methods often rely on pre-trained knowledge and heuristic features, limiting their ability to accurately model complex mutation effects, particularly higher-order and many-body interactions. To address these challenges, we propose H3-DDG, a Hypergraph-driven Hierarchical network to capture Higherorder many-body interactions across multiple scales.

Building a universal trajectory foundation model is a promising solution to address the limitations of existing trajectory modeling approaches, such as task specificity, regional dependency, and data sensitivity.