Large language models (LLMs) are increasingly deployed in settings where the available context is incomplete or degraded. We argue that an LLM generating answers under incomplete context can be viewed as an implicit imputer, and evaluated against a criterion from the multiple imputation (MI) literature: uncertainty should scale with the amount of missing information. We assess this criterion on SQuAD, using a controlled framework in which context availability is varied across five levels. We evaluate two answer-level uncertainty measures that can be estimated from repeated sampling: sampling-based confidence (empirical mode frequency) and response entropy. Confidence fails to reflect increasing missingness: it remains high even as accuracy collapses. Entropy, by contrast, increases with context removal, consistent with the MI analogy, and explains substantially more variance in accuracy than confidence across all evidence levels (quadratic $R^2$ gap up to 0.057). We further introduce a black-box diagnostic $ρ_R(α)$ that estimates the proportion of baseline uncertainty resolved by context level $α$, requiring only repeated sampling with and without context. These results suggest that entropy is a more responsive black-box uncertainty measure than confidence under incomplete context.

Flow Language Models (FLMs) are a recently introduced class of language models which adapt continuous flow matching for one-hot encoded token sequences. Their denoisers have a special structure absent from generic continuous diffusion models: each block of the denoising mean is a posterior marginal distribution over the clean token at that position. Standard DDPM-style samplers collapse these marginals to a single conditional-mean endpoint and bridge toward this simplex-valued point, which is generally not a valid one-hot sequence. We argue that the natural sampler for an FLM is instead posterior-predictive. At each reverse step, we sample a clean one-hot endpoint from the factorized posterior defined by the FLM token marginals, and then sample the next continuous state from the analytic Ornstein--Uhlenbeck bridge conditioned on that endpoint. The method is training-free, uses the same model evaluations as standard sampling, and gives a principled interface for token-level decoding controls such as temperature scaling and nucleus truncation. We show that, under exact posterior marginals, the endpoint approximation error is exactly the conditional multi-information among token positions. The induced one-step bridge kernel preserves all token-wise posterior-predictive marginals and loses only the residual cross-position dependence. Finally, we prove a Girsanov path-space comparison showing that the marginal-conditioned bridge has a no-larger denoising-error term than the frozen conditional-mean bridge, with strict improvement whenever intermediate coordinate-wise bridge observations reveal additional information about the clean token. Experiments with FLMs show that the sampler improves the quality--diversity tradeoff. Code is available at: github.com/imbirik/mcb.

Valiant's 1984 paper is widely credited with introducing the PAC learning model, but it, in fact, introduced a different model: unlike PAC learning, the learner receives only positives, may issue membership queries, and must output a hypothesis with no false positives. Prior work characterized variants, including the case without queries. We revisit Valiant's original model and ask: *Which classes are learnable in it?* For every finite domain, including Valiant's Boolean-hypercube setting, we show that a class is learnable if and only if every realizable positive sample can be certified by a poly-size adaptive query-compression scheme. This is a new variant of sample compression where the learner certifies samples via a short interaction with the membership oracle. Our characterization shows that learnability in Valiant's model is strictly sandwiched between learnability in the PAC model and the variant of Valiant's model without membership queries. This is one of the rare cases where introducing membership queries changes the set of learnable classes, and not just the sample or computational complexity. Next, we study the natural extension of the model to arbitrary domains. While we do not obtain an exact characterization, our techniques readily generalize and show that the same strict sandwiching persists. Finally, we show that $d$-dimensional halfspaces, which are not learnable without queries, are learnable with queries: we give a $\mathrm{poly}(d) \tilde{O}(1/ε)$ sample and $\mathrm{poly}(d) \mathrm{polylog}(1/ε)$ query algorithm, and prove that at least $Ω(d)$ samples or queries are necessary. To our knowledge, this is the first algorithm for halfspaces in Valiant's model. Together, these results uncover a surprisingly rich theory behind Valiant's original notion of learnability and introduce ideas that may be of independent interest in learning theory.

Some of the world's biggest and most powerful brands are attempting to be more cute and cuddly. Tech giants Microsoft and Apple are among a wave of businesses who have recently introduced new cartoon character mascots, a tactic experts say is often used to make a brand seem more human and friendly, and to build a stronger connection with customers. Apple's character, a blue and white figure with an outsized head, has become unofficially known as Little Finder Guy. Introduced in March in social media videos to promote a new laptop, it has gained some positive coverage. Microsoft, which years ago shelved its widely-disliked Clippy paperclip virtual assistant, has also unveiled a new cartoon character for its AI assistant Copilot.

The plaintiffs and defense have rested their cases, as well as their rear ends. The final stragglers testified on Wednesday in the trial. The witnesses generated few waves, aside from the revelation that Microsoft has so far spent over $100 billion on its partnership with OpenAI . Rather than focus on that, I wanted to bring you a candid observation that my colleague Maxwell Zeff and I can't stop talking about after spending nearly three weeks watching the trial. The courtroom is littered with butt cushions.

PCWorld reports that Microsoft Edge's new AI-driven'Journeys' feature is replacing traditional browser history with AI summaries that often omit direct website links. This change frustrates users by hindering their ability to find specific previously visited sites, removing the autonomy provided by conventional browsing tools. Microsoft is also discontinuing the useful'Collections' feature in favor of this AI-centric approach, representing a step backward in browser functionality. There's a school of thought that says that "AI brain" is a real thing, where AI quietly removes the traditional need to think through a problem. In this context, Microsoft Edge's AI-brain problem just got a lot worse -- and it's actively blocking your ability to get things done. Microsoft began rolling out substantial updates to the Edge desktop and mobile browser today, and yes, they obviously prioritize Copilot. Some of these feel familiar; didn't Google launch automated quizzes and podcasts months ago? But Copilot isn't just being added to Edge. It's actively taking over portions of Edge that humans used to manage themselves, specifically the nearly infinite list of sites that you've browsed as part of your browser history.

Instagram's Instants app lets you send disappearing photos--and it's probably where your horny friends will post spicy pics. Meta launched a new app on Wednesday, called Instants, that integrates with existing Instagram accounts and allows users to send unedited, disappearing photos. Instants leans into the popularity of Instagram's Stories feature and Close Friends lists, where users can selectively share images with a smaller audience. Instants is available as a stand-alone app on iOS and Android in select countries, and it's accessible through Instagram's direct messaging tab. The core of Instants, from its name to the bare-bones layout, is designed to evoke a sense of ephemerality.

Artificial intelligence has posed a multi-layered problem for Apple in recent years. We're expecting to hear some big news at WWDC this year about how AI will be integrated into the company's gadgets, but there are still other wrinkles still to be ironed out in its broader approach to the use of this influential technology. According to, one of those challenges is the recent interest and development of agentic AI. To date, Apple has not permitted vibe coding tools on the App Store because they would violate its policies. They could also potentially be used to create original apps for people who would have otherwise gotten software from the App Store, which could pose a threat to Apple's revenue as well as creating a loophole for spreading malware or taking other malicious actions. But applying that same block more broadly to any agentic AI services, which can take active control over a device and its programs, could keep Apple out of the loop as those tools are generating a lot of interest among both developers and casual users.

For animals, certain colors scream poison. 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. Although largely safe, turbines still pose a danger to some migratory birds. Breakthroughs, discoveries, and DIY tips sent six days a week. Wind turbines are a net positive for a sustainable society, but that doesn't mean they don't have an environmental impact.

If this hasn't been said before, it should've been -- you can't hide in the bushes at a bachelorette pool party Shakira cranks up the heat with a World Cup song that has people dancing, buy Elvis' rhinestone jock & BBQ UCF graduates clobber commencement speaker with boos after she says AI is the'next Industrial Revolution' Hang gliding Lookout Mountain: What it's really like to be aero-towed 1,700 feet above Georgia Paige Spiranac and her mom stun the internet, Lane Kiffin's incredible shot at Ole Miss & the NFL did it again Maggie Sajak appears at Savannah Bananas game as Jackson Olson's girlfriend, e-bike near death & MEAT! Mike Pompeo: I've never seen anyone colder, more ruthless than Xi Jinping Trump to press Xi to'open up' China as tech CEOs join key summit South Carolina AG on overturned Murdaugh conviction: 'We have time to try him again' Former CDC director says'outside scientists' might have influenced COVID-19 origins findings Dr. Fauci's role in COVID cover-up was'INTENTIONAL,' CIA whistleblower says CIA calls COVID whistleblower hearing'political theater' in new statement Sen. Moreno warns Chinese cars pose data risks, could devastate US auto industry Olivia Dunne and her Baywatch co-stars are gearing up for a big season while Miller Lite continues to raise the bar. Fox News Flash top sports headlines are here. Check out what's clicking on FoxNews.com. We're halfway to June, somehow, and that means ... well, it means very little. It's a pretty slow(ish) time of year, which is fine with me.