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 Sophie Cunningham says she's talked with NBA Commissioner Adam Silver amid search for WNBA's next commissioner Ryan Day hints at day of reckoning, vows to make'hard decisions' after Ohio State's late-game Texas breakdown Elon Musk proposes AI competitors'grade' each other's safety models Trump shooter Thomas Crooks' FBI files reveal bizarre details Charlie Kirk's family files claim against Utah Valley University over security failures The six-year deal raises Buck's annual salary to $18M, tying him with Troy Aikman behind Stephen A Smith and Pat McAfee Pate wants to capture the vibe of college campus before a big game. ESPN and Joe Buck have agreed to a six-year contract extension worth around $108 million. The deal will raise Buck's annual salary from $15 million to $18 million, as The Athletic first reported Wednesday. Buck signed with ESPN in 2022 alongside his longtime broadcast partner, Troy Aikman. In terms of scale, signing Buck and Aikman marked one of ESPN's most important talent acquisitions in its 40-year history.

Florida State fans pining for Jimbo Fisher's return are living in the past, not in reality Roman Reigns survives Penta's World Heavyweight Championship challenge, LA Knight makes a statement of his own Introducing OutKick's first original animated series, 'Sports Talk with Cal McCallister' WWE star Roxanne Perez reminds fans to respect wrestlers' boundaries while on the road: 'We are just humans' Ryan Serhant predicts the next US real estate EPICENTER is'dead center' in America #shorts Democrats believe they can use AI dispute for'more political power,' expert says AG Todd Blanche defends Trump's criticism of Supreme Court mail ballot ruling AG Todd Blanche defends Trump's criticism of Supreme Court mail ballot ruling Sen Rand Paul accuses Dems of bribing voters, calls out'terrible' welfare system There's a lot of good in the Protect College Sports Act, says Baseball Hall of Famer AI companies have been saying this'very consistently,' Axios co-founder GOP lawmaker slams Dems' push to rein in Trump on Iran: 'Political stage' OutKick CC Sabathia says Yankees could do a better job helping young players handle the pinstripes: 'Different beast' The Hall of Famer pointed to Anthony Volpe, Gary Sanchez and more who got'labels' right away from the fan base Are the Yankees under the most pressure to win a World Series? Opening Day is tonight, with the New York Yankees facing off with the San Francisco Giants. Colin Cowherd asks if the Yankees are under the most pressure entering this season to win a World Series. It's been said many times over a century: earning your pinstripes with the New York Yankees is one of the hardest things to do in baseball. Just take Anthony Volpe's rollercoaster career with the ballclub, which has once again become a storyline as they head toward the postseason.

Large language models excel at many tasks but often incur high inference costs during deployment. To mitigate hallucination, many systems use a knowledge graph to enhance retrieval-augmented generation (KG-RAG). However, the large amount of retrieved knowledge contexts increase these inference costs further. A promising solution to balance performance and cost is LLM routing, which directs simple queries to smaller LLMs and complex ones to larger LLMs. However, no dedicated routing methods currently exist for RAG, and existing training-based routers face challenges scaling to this domain due to the need for extensive training data. We observe that the score distributions produced by the retrieval scorer strongly correlate with query difficulty. Based on this, we propose an extremely simple yet effective routing framework, the first specifically designed for KG-RAG that efficiently balances performance and cost in a plug-and-play manner. It delivers over 3x higher routing effectiveness while reducing runtime to less than 0.001x compared to existing methods. Our code is available at https://github.com/hrwang00/SkewRoute.

Although Large Language Models achieve strong success in many tasks, they still suffer from hallucinations and knowledge deficiencies in real-world applications. Many knowledge graph-based retrieval-augmented generation (KG-RAG) methods enhance the quality and credibility of LLMs by leveraging structure and semantic information in KGs as external knowledge bases. However, these methods struggle to effectively incorporate structure information, either incurring high computational costs or underutilizing available knowledge. Inspired by smoothing operations in graph representation learning, we propose path pooling, a simple, train-free strategy that introduces structure information through a novel path-centric pooling operation. It seamlessly integrates into existing KG-RAG methods in a plug-and-play manner, enabling richer structure information utilization. Extensive experiments demonstrate that incorporating the path pooling into the state-of-the-art KG-RAG method consistently improves performance across various settings while introducing negligible additional cost. Code is coming soon at https://github.com/hrwang00/path-pooling.

Large Language Models (LLMs) demonstrate strong reasoning abilities but face limitations such as hallucinations and outdated knowledge. Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) addresses these issues by grounding LLM outputs in structured external knowledge from KGs. However, current KG-based RAG frameworks still struggle to optimize the trade-off between retrieval effectiveness and efficiency in identifying a suitable amount of relevant graph information for the LLM to digest. We introduce SubgraphRAG, extending the KG-based RAG framework that retrieves subgraphs and leverages LLMs for reasoning and answer prediction. Our approach innovatively integrates a lightweight multilayer perceptron with a parallel triple-scoring mechanism for efficient and flexible subgraph retrieval while encoding directional structural distances to enhance retrieval effectiveness. The size of retrieved subgraphs can be flexibly adjusted to match the query's need and the downstream LLM's capabilities. This design strikes a balance between model complexity and reasoning power, enabling scalable and generalizable retrieval processes. Notably, based on our retrieved subgraphs, smaller LLMs like Llama3.1-8B-Instruct deliver competitive results with explainable reasoning, while larger models like GPT-4o achieve state-of-the-art accuracy compared with previous baselines -- all without fine-tuning. Extensive evaluations on the WebQSP and CWQ benchmarks highlight SubgraphRAG's strengths in efficiency, accuracy, and reliability by reducing hallucinations and improving response grounding.

LAS VEGAS – Most baseball fans won't forget the controversial call in Game 6 of the 2019 World Series between the Houston Astros and the Washington Nationals when runner Trea Turner was ruled out because of interference. Umpire accuracy is a frustration for fans and players in nearly every game. This season, MLB has launched so-called "robot umpires" in 11 Pacific Coast League Triple-A teams, putting it one step away from reaching the major leagues, to improve accuracy and reduce delays. The automated balls and strikes system (ABS) debuted in a Las Vegas Aviators' game earlier this month. As cool and bizarre as it would be to see "Jetsons"-style robots on the field, most fans won't notice the actual device -- eight surveillance-looking cameras at the top of the bleachers.

We all have our own tried and true ways to boost productivity and focus. I grew up in a house filled with 60s music, video games, and Cubs baseball broadcasts. Perhaps it's not surprising, in a career of hopping around between computational physics, application development, data science and engineering but always coding, that I have come to love working with the game on in the background -- especially during those Friday afternoon games at Wrigley. To me, these things just go together. The same goes for quantum computing and machine learning (ML).

Snell, second from left, comes out of the game against the Dodgers in the 6th inning in Game 6 of the World Series at Globe Life Field on October 27, 2020 in Arlington, Texas. I'm a lifelong Dodgers fan and I waited for 32 years for the team to win another World Series. But during this period of time, the sport has certainly seen much change. With the availability of huge amounts of data, sophisticated computers and advanced analytics, the strategies have become increasingly based on the numbers. It seems that AI (Artificial Intelligence) has dominated the decision making process.

ARLINGTON, TEXAS - OCTOBER 27:Rays pitcher Blake Snell, second from left, comes out of the game ... [ ] against the Dodgers in the 6th inning in Game 6 of the World Series at Globe Life Field on October 27, 2020 in Arlington, Texas. I'm a lifelong Dodgers fan and I waited for 32 years for the team to win another World Series. But during this period of time, the sport has certainly seen much change. With the availability of huge amounts of data, sophisticated computers and advanced analytics, the strategies have become increasingly based on the numbers. It seems that AI (Artificial Intelligence) has dominated the decision making process.

One thing I get asked about a lot is weighting. What do I need to worry about? By popular demand, I recently put together a lunch-and-learn at my company to help address all these questions. The goal was to be applicable to a large audience, (e.g., with a gentle introduction), but also some good technical advice/details to help practitioners. This blog was adapted from that presentation. Before we talk about weighting, we should all get on the same page about what a model is, what they are used for, and some of the common issues that modelers run into.