Baker Mayfield loses bet to coach Todd Bowles, has'rough couple of days' including at press conference Falcons star Kyle Pitts Sr predicts CFB players will unionize, says pro players returning to school is'crazy' New trailer for'Other Mommy' with Jessica Chastain reveals a sinister entity terrorizing a young girl'God has been so good to me': Ella Langley reacts to incredible nine CMA Awards nominations'How can you fight during a love song?' Riley Green defuses crowd fight at New Mexico show Netflix drops full trailer for Ben Affleck's'Animals' and expectations are already soaring Dan Lanning spars with reporter after Dante Moore misses media following Oregon's loss to Oklahoma State Best hikes to try around Lubbock ahead of Texas Tech's week one matchup with Abilene Christian Buccaneers Legend Randy Grimes is Excited for Baker Mayfield to Be Back in Tampa | Don't @ Me w/ Dan Dakich NFL Legend Randy Grimes Gives His Tells On If An Offensive Line is Dominating | Don't @ Me w/ Dan Dakich Eva Shockey Doesn't Like the Taste of Bear but Has Eaten it | Tomi Lahren is Fearless Eva Shockey Shares the Ridiculously Lengthy Process of Obtaining U.S. Citizenship | Tomi Lahren is Fearless Influencer Eva Shockey on her time in Russia: "It was a cool experience, mostly to see how lucky we are in America | Tomi Lahren is Fearless OutKick Attorney reveals Lane Kiffin used ChatGPT for legal advice during LSU's failed bid to add pro players Attorney Tom Mars said the LSU head coach ignored his advice and relied on AI that was'almost always wrong' Lane Kiffin says a lot but he is right about this. He's reportedly a big fan of artificial intelligence, and it nearly sparked a nightmare scenario unfolding for the Tigers. LSU was embroiled in controversy before a single snap of the season was played when Kiffin attempted to add former Ole Miss players Dae'Quan Wright and Zxavian Harris to the roster. This page may contain affiliate links to legal sports betting partners. If you sign up or place a wager, FOX News may be compensated.

Reporter for The Athletic fumes over Arch Manning, silent on Josh Jacobs' alleged violence against woman Jordon Hudson dusts herself off and resumes her WAG duties, Belichick vs Sydney Sweeney & 'Gainesville Ripper' Mets are'trash' because they're not listening to former NJ Gov Chris Christie, Stephen A Smith says Orioles pitcher Shane Baz drilled with 105.1 mph line drive in scary scene NFL's battle with drinking and driving continues as Keenan Allen formally charged in Indianapolis Anthony Molina and Matthew Liberatore's struggles make over 8 runs the play for Giants-Cardinals Shota Imanaga's home run struggles and Wrigley wind make over 7.5 the play for Braves-Cubs Jaxson Dart's girlfriend Marissa Ayers shines in Week 1 win, a Giants-Cowboys fan fight & Ole Miss tension MLB's most electric slugger just had one of the slowest, most disrespectful walk-off home runs of all time James Franklin makes Virginia Tech press conference extremely awkward with bizarre'Hey Michael' moment Texans' Azeez Al-Shaair to be fined once again by NFL for wearing Pro-Palestinian personal message on eyeblack Stephen A. Smith calls out NBA star Kawhi Leonard for letting Clippers take the fall for salary cap scandal Sheriff decries Democrat's'disgusting display' at House hearing on sanctuary laws Sheriff decries Democrat's'disgusting display' at House hearing on sanctuary laws Sheriff calls Democrat's questioning at House hearing a'disgusting display' It's urgent for Congress to step in and protect college sports: Sen Ted Cruz Mike Pence warns this Trump move would send a'deafening' message to Putin'COMPLETE WASTE OF MONEY': Real estate celebrity calls out $200,000 housing mistake Rand Paul slams Congress over AI regulation: 'I wouldn't put Congress in charge of a McDonald's' Rand Paul slams Congress over AI regulation: 'I wouldn't put Congress in charge of a McDonald's' Ed Sheeran responds to Macklemore backlash amid'Free Palestine' controversy Landry said Ole Miss AD Keith Carter'could have saved some money and waited until Saturday' to find Umanmielen I think they're going to put them on their roster that has to be submitted by Friday afternoon. And then all eyes are going to be on, do they run out of the tunnel Saturday afternoon in Columbia, South Carolina, or in Baton Rouge, Louisiana? And if that's the case, Dan, the SEC is going to have a decision to make. On Tuesday afternoon, however, the rivalry received another jolt outside LSU's football facility. In a lawsuit filed this past July, the Ole Miss athletic department is currently suing former players, and current LSU Tigers, Devin Harper and Princewill Umanmielen, on charges of breach of contract tied to their exit from Oxford following the departure of head coach Lane Kiffin.

An escaped tiger believed to be owned by Germany's Tiger Queen has been shot dead by police after attacking one of its keepers, according to local media reports. Police say a 73-year-old man was seriously injured after being attacked on Sunday while he was inside the animal's enclosure, located in a privately-owned facility on the outskirts of the German city of Leipzig. The tiger escaped the enclosure and was found shortly after by armed police, who shot and killed the animal. The site of the enclosure is believed to be owned by controversial trainer and private owner Carmen Zander, who describes herself as Germany's Tiger Queen. The animal was one of eight big cats kept at the industrial site near the German town of Schkeuditz, according to local media.

Fossil evidence shows a case of mistaken big cat identity. Breakthroughs, discoveries, and DIY tips sent six days a week. Present-day Japan may see its fair share of bears, but the islands' big cat populations are long gone. Between 129,000 and 11,700 years ago, temporary land bridges allowed the ancient predators to migrate between mainland Asia and the islands. Paleobiologists have long believed tigers were the primary cats to make this trek, but recently analyzed evidence published in the suggests a different timeline.

In web environments, user preferences are often refined progressively as users move from browsing broad categories to exploring specific items. However, existing generative recommenders overlook this natural refinement process. Generative recommendation formulates next-item prediction as autoregressive generation over tokenized user histories, where each item is represented as a sequence of discrete tokens. Prior models typically fuse heterogeneous attributes such as ID, category, title, and description into a single embedding before quantization, which flattens the inherent semantic hierarchy of items and fails to capture the gradual evolution of user intent during web interactions. To address this limitation, we propose CoFiRec, a novel generative recommendation framework that explicitly incorporates the Coarse-to-Fine nature of item semantics into the tokenization process. Instead of compressing all attributes into a single latent space, CoFiRec decomposes item information into multiple semantic levels, ranging from high-level categories to detailed descriptions and collaborative filtering signals. Based on this design, we introduce the CoFiRec Tokenizer, which tokenizes each level independently while preserving structural order. During autoregressive decoding, the language model is instructed to generate item tokens from coarse to fine, progressively modeling user intent from general interests to specific item-level interests. Experiments across multiple public benchmarks and backbones demonstrate that CoFiRec outperforms existing methods, offering a new perspective for generative recommendation. Theoretically, we prove that structured tokenization leads to lower dissimilarity between generated and ground truth items, supporting its effectiveness in generative recommendation. Our code is available at https://github.com/YennNing/CoFiRec.

We introduce a new tokenizer for language models that minimizes the average tokens per character, thereby reducing the number of tokens needed to represent text during training and to generate text during inference. Our method, which we refer to as the Length-MAX tokenizer, obtains its vocabulary by casting a length-weighted objective maximization as a graph partitioning problem and developing a greedy approximation algorithm. On FineWeb and diverse domains, it yields 14--18\% fewer tokens than Byte Pair Encoding (BPE) across vocabulary sizes from 10K to 50K, and the reduction is 13.0\% when the size is 64K. Training GPT-2 models at 124M, 355M, and 1.3B parameters from scratch with five runs each shows 18.5\%, 17.2\%, and 18.5\% fewer steps, respectively, to reach a fixed validation loss, and 13.7\%, 12.7\%, and 13.7\% lower inference latency, together with a 16\% throughput gain at 124M, while consistently improving on downstream tasks including reducing LAMBADA perplexity by 11.7\% and enhancing HellaSwag accuracy by 4.3\%. Moreover, the Length-MAX tokenizer achieves 99.62\% vocabulary coverage and the out-of-vocabulary rate remains low at 0.12\% on test sets. These results demonstrate that optimizing for average token length, rather than frequency alone, offers an effective approach to more efficient language modeling without sacrificing -- and often improving -- downstream performance. The tokenizer is compatible with production systems and reduces embedding and KV-cache memory by 18\% at inference.

In this paper, we propose capturing and utilizing \textit{Temporal Information through Graph-based Embeddings and Representations} or \textbf{TIGER} to enhance multi-agent reinforcement learning (MARL). We explicitly model how inter-agent coordination structures evolve over time. While most MARL approaches rely on static or per-step relational graphs, they overlook the temporal evolution of interactions that naturally arise as agents adapt, move, or reorganize cooperation strategies. Capturing such evolving dependencies is key to achieving robust and adaptive coordination. To this end, TIGER constructs dynamic temporal graphs of MARL agents, connecting their current and historical interactions. It then employs a temporal attention-based encoder to aggregate information across these structural and temporal neighborhoods, yielding time-aware agent embeddings that guide cooperative policy learning. Through extensive experiments on two coordination-intensive benchmarks, we show that TIGER consistently outperforms diverse value-decomposition and graph-based MARL baselines in task performance and sample efficiency. Furthermore, we conduct comprehensive ablation studies to isolate the impact of key design parameters in TIGER, revealing how structural and temporal factors can jointly shape effective policy learning in MARL. All codes can be found here: https://github.com/Nikunj-Gupta/tiger-marl.