Derrick Henry's rushing yards prop leads Ravens vs Titans picks as Baltimore favored by 11.5 Fanatics Sportsbook Promo Code FOXNEWS350 Unlocks Bet $20, Get $350 Promo for Braves vs. Dodgers Underdog Promo Code FOXNEWS: Play $5, Get $100 on MLB Division Series Padres vs. Brewers Former OpenAI safety chief warns AI industry's culture is'broken' Chris Hansen slams'Primetime' movie, calls it an insult Sen John Thune: The Democratic Party doesn't want to give the president any victories'Gangs' vs. 'Cliques': Seattle's crime language comes under fire Tomi Lahren says France protests are a'cautionary tale' for the US Rep Mike Lawler: These leaders don't want to hold people accountable for their actions This page may contain affiliate links to legal sports betting partners. If you sign up or place a wager, FOX News may be compensated. This content was created by a team that works independently from the Fox newsroom. Washington Commanders quarterback Marcus Mariota (8) throws during the first half of an NFL football game against the Denver Broncos Sunday, Nov. 30, 2025, in Landover, Maryland. The NFL normally dominates the television screen on Sunday, but its reach is even longer today as the first kickoff starts at 9:30 AM ET, and the final kickoff is 8:20 PM ET.

Trump anticipated Xi's visit for months To play this video you need to enable JavaScript in your browser. Trump anticipated Xi's visit for months Close US President Donald Trump has been talking for months about Chinese President Xi Jinping's visit to Washington DC. He's spruced up the White House, lamented that his new ballroom won't be finished in time and ensured a new marble helipad is ready to show off to the Chinese leader. Trump gave Xi a warm welcome on his arrival at Joint Base Andrews in Maryland, complete with a military flyover and a 100ft (30m) red carpet. The BBC's North America Editor Sarah Smith explains how Trump has long made no secret of his admiration for strongman leaders around the world.

Trump-Xi summit: Here's what's on the agenda, and why it matters Share Trump-Xi summit: Here's what's on the agenda, and why it matters on social media China's leader Xi Jinping is expected to hold highly anticipated talks with United States President Donald Trump in Washington on Thursday, as both sides seek to ease trade tensions and discuss increasingly capable artificial intelligence (AI) systems. On Wednesday evening, Trump and US First Lady Melania Trump greeted Xi and his wife, Peng Liyuan, at Joint Base Andrews in Maryland, with a rare red carpet welcome. No US president had previously welcomed a visiting guest personally at the base in six decades. Earlier in the day, China's state broadcaster CCTV reported that Xi and Peng were being accompanied by the president's closest aide and adviser, Cai Qi, and Foreign Minister Wang Yi. The White House said the talks scheduled for Thursday would mark "a significant milestone in the relationship between the two countries".

Trump offers warm welcome as China's Xi arrives for US visit To play this video you need to enable JavaScript in your browser. US President Donald Trump has greeted his Chinese counterpart Xi Jinping with a military flyover and 100ft (30m) red carpet to kick off their three-day summit in Washington. In a rare move, Trump met China's premier at the airport as the US Air Force band marched on the tarmac and US and Chinese flags flew. Trump and First Lady Melania Trump welcomed Xi and his wife Peng Liyuan at Joint Base Andrews on Wednesday evening in Maryland. The high-stakes visit - Xi's first trip to Washington since 2015 - is expected to include closed-door meetings, a state dinner, and plenty to discuss, from AI, to the Iran war and Taiwan, and an extended trade truce.

As Xi meets Trump, who's winning their trade war? Share As Xi meets Trump, who's winning their trade war? on social media Chinese President Xi Jinping is scheduled to hold talks with United States President Donald Trump at the White House during his state visit - the first by a Chinese leader in more than a decade - as the world's two largest economies are locked in an ongoing tussle over trade and artificial intelligence. Trump is expected to welcome Xi on the tarmac at Joint Base Andrews outside the US capital, Washington, DC, in a rare gesture for a high-stakes three-day visit by the Chinese leader. He ramped up tariffs on Chinese goods after returning to power in 2025, and has since imposed curbs on the sale of AI chips to Beijing as the two nations compete for supremacy in the AI race. The future of their fragile trade truce will be high on the agenda when the two leaders meet on Thursday.

AI, Tariffs, Rare Minerals: What to Expect From Trump's Upcoming Summit With Xi Jinping Washington and Beijing have grown ever more linked in the AI boom, making hardware exports and technological restrictions hefty bargaining chips in negotiations. Of all the events in the United Nations General Assembly taking place this week, the most anticipated by far is the upcoming meeting between Donald Trump and Chinese president Xi Jinping. Though the majority of the diplomatic activities are happening in New York, this face-to-face between the two leaders will take place at the White House. Trump is scheduled to personally welcome Xi at Joint Base Andrews on Wednesday, September 23--an unusual diplomatic gesture, as US presidents do not typically go to the airport to welcome foreign leaders. Ahead of that airport reunion, a US delegation led by Treasury secretary Scott Bessent and trade representative Jamieson Greer is meeting with He Lifeng, China's vice premier of the State Council, to set the agenda.

Figure 4: Impact of the proportion of injected noise for learning Paragraph Embeddings on XSum dataset. PPLint and the PPL of the generation obtained from training PLANNER on the corresponding z at different noise level. We observed when the value of p is within (0, 0.7), there Performing a grid search on each task using diffusion models is an expensive process. However, it has been observed that an increase in the value of p leads to a deviation between the two. This could be attributed to a higher conversion error that occurs when p is excessively large. A.2 Selection of number of latent code k The parameter k determines the number of latent codes used to represent a paragraph and therefore controls the compression level. Latent codes with smaller values of k are easier to model using the diffusion model, but may struggle to accurately preserve all the information in the original text. Additionally, smaller values of k offer computational efficiency as the sequence length for the diffusion model is k. To determine the best set of latent codes, we conducted experiments using three different methods: 1) selecting the first k hidden vectors, 2) selecting the last k hidden vectors, and 3) selecting interleaving hidden vectors, one for every L k hidden vectors. The results of the ablation study are presented in Table 5. Based on our findings, we observed no significant difference among the different choices, so we opted for option 1). Furthermore, we discovered that increasing the value of k does not lead to a dramatic improvement in performance. To balance between efficiency and performance, in most of our study we only use k =16 Setup BLEU_clean BLEU_robust First k (k=16) 79.59 43.17 A.3 Reconstruction, denoising and interpolation examples In Table 6, we present examples that demonstrate the adeptness of the trained Variational Paragraph Embedder in providing clean and denoised reconstructions. Additionally, we showcase interpolation results (Table 7, 8) derived from two random sentences in the hotel review dataset. The interpolated paragraph is usually coherent and incorporates inputs from both sentences, characterizing the distributional smoothness of the latent space. Reconstructed text complaints: after two nights stay, i asked the maid to clean our room (empty the wastebasket & make the bed). Denoising reconstruction (hotel review), noise level 0.3 Original text * * * check out the bathroom picture * * * i was in nyc by myself to watch some friends participate in the us olympic marathon trials. Corrupted text * * [unused697] check exams the bathroom picture * * slams i was in nyc mead myself yankee 2016 some scotch ruin in the outfielder olympicnca trials.

Deep neural networks are powerful machines for visual pattern recognition, but reasoning tasks that are easy for humans may still be difficult for neural models. Humans possess the ability to extrapolate reasoning strategies learned on simple problems to solve harder examples, often by thinking for longer. For example, a person who has learned to solve small mazes can easily extend the very same search techniques to solve much larger mazes by spending more time. In computers, this behavior is often achieved through the use of algorithms, which scale to arbitrarily hard problem instances at the cost of more computation. In contrast, the sequential computing budget of feed-forward neural networks is limited by their depth, and networks trained on simple problems have no way of extending their reasoning to accommodate harder problems. In this work, we show that recurrent networks trained to solve simple problems with few recurrent steps can indeed solve much more complex problems simply by performing additional recurrences during inference. We demonstrate this algorithmic behavior of recurrent networks on prefix sum computation, mazes, and chess. In all three domains, networks trained on simple problem instances are able to extend their reasoning abilities at test time simply by "thinking for longer."

Bayesian Optimal Experimental Design (BOED) provides a rigorous framework for decision-making tasks in which data acquisition is often the critical bottleneck, especially in resource-constrained settings. Traditionally, BOED typically selects designs by maximizing expected information gain (EIG), commonly defined through the Kullback-Leibler (KL) divergence. However, classical evaluation of EIG often involves challenging nested expectations, and even advanced variational methods leave the underlying log-density-ratio objective unchanged. As a result, support mismatch, tail underestimation, and rare-event sensitivity remain intrinsic concerns for KL-based BOED. To address these fundamental bottlenecks, we introduce an IPM-based BOED framework that replaces density-based divergences with integral probability metrics (IPMs), including the Wasserstein distance, Maximum Mean Discrepancy, and Energy Distance, resulting in a highly flexible plug-and-play BOED framework. We establish theoretical guarantees showing that IPM-based utilities provide stronger geometry-aware stability under surrogate-model error and prior misspecification than classical EIG-based utilities. We also validate the proposed framework empirically, demonstrating that IPM-based designs yield highly concentrated credible sets. Furthermore, by extending the same sample-based BOED template in a plug-and-play manner to geometry-aware discrepancies beyond the IPM class, illustrated by a neural optimal transport estimator, we achieve accurate optimal designs in high-dimensional settings where conventional nested Monte Carlo estimators and advanced variational methods fail.