Published transfer-BO comparisons often estimate an average treatment effect of acquisition choice over hidden regime variables, while practitioners need the conditional effect for their specific prior quality, budget ratio, and metric. An audit of 40 transfer-BO papers from NeurIPS, ICML, ICLR, AISTATS, UAI, TMLR, JMLR, and AutoML-Conf (2022-2025) finds that 98% never vary B/|A| as a controlled axis. On the same GDSC2 benchmark, changing only the budget reverses the ranking: at B=50, Greedy outperforms UCB by 0.050 Hit@1, while at B=100, UCB outperforms Greedy by 0.035. We capture this transition with the Portable Regime Score PRS=(B/|A|)(1-rho), where rho is the prior rank correlation and can be estimated from pilot contexts before the main comparison. Across 79 conditions spanning chemistry, drug-response biology, and HPO, a hierarchical model gives beta=0.50 (p=1.1e-9), and 19% of conditions fall in an equivalence zone where |advantage|<0.01 Hit@1. In five published reversal cases, PRS predicts the winner from pre-comparison observables. A No-Free-Leaderboard proposition explains why unconditional rankings are unstable: when CATE changes sign across regimes, the reported ATE becomes a function of benchmark mixture. RegimePlanner, which estimates rho online and switches acquisition accordingly, wins all 16 HPO-B search spaces at B=100 and exceeds the matched {Greedy,UCB} per-context oracle on GDSC2 by 18%. Pre-registered predictions achieve 27/40=67.5% overall accuracy and above 90% within EMA prior families. The practical protocol is simple: report B/|A|, rho, K, and metric alongside any claimed acquisition advantage.

The coin minted between 1584 and 1586 celebrates England's naval superiority. Breakthroughs, discoveries, and DIY tips sent every weekday. A coin minted during the reign of one of Great Britain's most famous monarchs recently fetched a record price at auction. The officially designated Elizabeth I (1558-1603) gold "Ship" Ryal of 15 Shillings ND (1584-1586) MS63 NGC sold for $372,000 by Heritage Auction in November . The sale set a world record for an Elizabeth Ship Ryal sold at auction.

On Inauguration Day, fans of the All-In Podcast gathered in a billiards room in Washington DC to watch Donald Trump's swearing-in – and a few miles away, the podcast co-host and PayPal Mafia alum David Sacks prepared to ascend to his role as Trump's AI and crypto czar. Very popular in Silicon Valley, All-In is fiercely pro-capitalism and enthusiastic about the world of tech start-ups and investments. Last summer, its co-hosts, Sacks and Jason Calacanis in particular, became vocal in their support for Trump and attempted to rally other tech leaders, including their listeners, behind the candidate. Now, Sacks has a seat at the table in the White House, as do many others in tech, including a former Uber executive, a senior adviser at Palantir, and a PayPal co-founder, who was picked to be ambassador to Denmark (Greenland, a territory Trump wants to seize, is part of Denmark). It's a watershed moment for relationships between Silicon Valley and Washington and, more broadly, what's often described as the tech right.

Models for conversational question answering (ConvQA) over knowledge graphs (KGs) are usually trained and tested on benchmarks of gold QA pairs. This implies that training is limited to surface forms seen in the respective datasets, and evaluation is on a small set of held-out questions. Through our proposed framework REIGN, we take several steps to remedy this restricted learning setup. First, we systematically generate reformulations of training questions to increase robustness of models to surface form variations. This is a particularly challenging problem, given the incomplete nature of such questions. Second, we guide ConvQA models towards higher performance by feeding it only those reformulations that help improve their answering quality, using deep reinforcement learning. Third, we demonstrate the viability of training major model components on one benchmark and applying them zero-shot to another. Finally, for a rigorous evaluation of robustness for trained models, we use and release large numbers of diverse reformulations generated by prompting GPT for benchmark test sets (resulting in 20x increase in sizes). Our findings show that ConvQA models with robust training via reformulations, significantly outperform those with standard training from gold QA pairs only.

AI is defining the future, even as many US senators struggle to understand it in the present. "It would have been better if it had been held in a room where the acoustics were better," Senator Chuck Grassley, an Iowa Republican, says of a much-anticipated--if overdue--All-Senators AI briefing orchestrated by Senate Majority Leader Chuck Schumer earlier this month. The shoddy acoustics of the first of three closed-door meetings--kept private to insulate senators from electoral pressure to perform before cameras--were far from Grassley's biggest complaint. "I would say that the next [one] will be more valuable, because this was a very general overview," he says. As AI expands its foothold across industries, households, and legislative bodies--including amongst some at the Capitol itself--Congress is under pressure to act quickly, even though many lawmakers still don't know what they're being asked to regulate.

A quarter of a century ago, a new application quietly emerged, seemingly out of nowhere, and changed the way we find information forever. Today, the word "Google" has become a synonym for "search," and its creator – now known as Alphabet – has gone on to become one of the largest and most powerful corporations on the planet. Sure, there were search engines before it. But Google was the first to popularize knowledge-based search. And although competitors have emerged over the years, mostly they've just been variations on a theme. Today, when we want to find something, no one says, "I'll Bing it" or "I'll Yahoo it."