Tennis player Rafael Jodar accused of pushing French Open ball girl, but did he really? Steve Hilton rips Steyer for trans athlete support, leads'Save Girls Sports' rally at track title meet Umpire Dan Bellino's baffling foul tip call on Seiya Suzuki renews calls for robot review in MLB Dakich: sports media has created an'industry' out of complaining about white athletes like Caitlin Clark Greg Sankey insists SEC is'strongest league' despite Big Ten winning three straight national championships Phillies look to upset Dodgers behind Zack Wheeler as Philadelphia's turnaround continues in LA NJ governor's'protected protest zone' sparks debate amid violent ICE facility clashes Analyzing how Iran's'shadow oil network' evades US sanctions Laura: Celebrities used to be really cool... Greg Gutfeld: Why is it so hard for Dems to admit they screwed up? Sean Hannity: James Talarico is more radical than Beto O'Rourke Mark Levin: We have our foot on the enemy's throat UFO expert illustrates the'tug of war' within the US government over file releases These Democrats'look in the camera' and'lie to you,' Jason Chaffetz says Actor Scott Baio: 'These people are crazy' OutKick-Sports AB Hernandez advances in California state championship as Save Women's Sports activists rally nearby Trans athlete AB Hernandez advances in California girls' track and field championship amid Save Girls' Sports rally Trans athlete AB Hernandez advanced to the final round of the 2026 CIF State Track & Field Championships as activists gathered nearby for a Save Girls' Sports rally. Transgender athlete AB Hernandez advanced in three girls' jumping events, earning the top place in two, during the preliminary round of the California Interscholastic Federation (CIF) state track & field championships on Friday. Hernandez, who has been at the center of a national controversy for competing in girls' competitions dating back to last May, will now contend for the state title in girls' long jump, high jump and triple jump on Saturday evening.

Victor Wembanyama's historic game one performance was personal, Spurs star reveals in postgame interview Dana White says gnats at Trump's White House Rose Garden dinner raised concerns for outdoor UFC events High school athlete slams CIF's shared podium rule as humiliating response that fails female competitors Kuwaiti Muslim jiu-jitsu champion refuses Israeli athlete's handshake: 'We do not respect them at all' Caitlin Clark's fiery Fever teammate tells WNBA haters to relax with perfect three-word response Red Sox legend Jason Varitek's wife appears to take massive swipe at team after ugly ouster Reds vs. Phillies betting preview: Why Cincinnati is the play despite their 4-12 skid over 16 games Bubba Wallace'seeing red' after being wrecked, female driver rage-quits and cries & NASCAR missed the mark Taiwan warns US about China's regional ambitions as Trump weighs arms deal Nate Bargatze takes clean comedy to big screen with'The Breadwinner' Retired vice admiral on Iran standoff: Trump has'time on his hands' Jury dismisses Elon Musk's lawsuit against OpenAI and Sam Altman Strikes must resume if Iran fails to negotiate'in good faith': Brig Gen John Teichert Trace Gallagher: What does liberal America want? 'Rededicate 250' faith event draws thousands to DC OutKick contributor Riley Gaines discusses female high school athletes speaking out after a transgender participant won multiple events at a California track meet on'Fox & Friends.' MOORPARK, Calif. - On the morning of a day that would live in girls' sports infamy, a letter was handed out to coaches who entered a championship track and field meet at Moorpark High School. The letter announced that any girl who finished behind a biological male trans athlete would be bumped up by one spot on the podium, if that girl finished behind the trans athlete. The letter was dated May 16 -- the day of the event. The California Interscholastic Federation (CIF) will implement the pilot entry process introduced last season at the 2025 CIF Track and Field State Championships.

It's time for Wright to retire - Van Gerwen Michael van Gerwen says Peter Wright should retire after his emphatic defeat in the second round of the PDC World Championship. Two-time world champion Wright won just two legs and averaged below 80 as he was surprisingly beaten by debutant Arno Merk on Tuesday. Van Gerwen, who has won the world title three times, said he was not surprised by [Wright's] performance given the Scotsman's inconsistent recent record and it's time for him to retire anyway. The 36-year-old Dutchman beat William O'Connor to reach the third round at Alexandra Palace. Another two-time world champion, Gary Anderson, said players cannot play well all the time and that Wright is having a blip.

The world's top chess player defeated ChatGPT in an online match in only 53 moves. Magnus Carlsen won the game without losing a single piece, while ChatGPT lost all its pawns, screenshots the Norwegian grandmaster shared on X on July 10 showed. "I sometimes get bored while travelling," Carlsen captioned the post. "That was methodical, clean, and sharp. Well played!" ChatGPT said to him, according to the screenshots Carlsen posted.

Jim Harbaugh joins Colin Cowherd to discuss the culture he's created with the Los Angeles Chargers, Justin Herbert's mentality and the'dog-eat-dog' chaos of the AFC West. Los Angeles Chargers head coach Jim Harbaugh was added Friday to a lawsuit against his former employer, the University of Michigan, and a former assistant football coach accused of hacking into computer systems to acquire photos of college athletes. Attorneys claim Harbaugh allowed Matt Weiss to continue working as co-offensive coordinator in a national playoff game after Weiss was seen viewing private information on a computer in December 2022. "The university's delay in taking meaningful protective action until after a high-stakes game sends a clear message: Student welfare was secondary," said Parker Stinar, the lead lawyer in a class-action lawsuit arising from a criminal investigation of Weiss. "Had Harbaugh implemented basic oversight of his staff, plaintiffs and the class would have been protected against predators such as Weiss," the updated lawsuit states.

Temporal tabular question answering presents a significant challenge for Large Language Models (LLMs), requiring robust reasoning over structured data, which is a task where traditional prompting methods often fall short. These methods face challenges such as memorization, sensitivity to table size, and reduced performance on complex queries. To overcome these limitations, we introduce TempTabQA-C, a synthetic dataset designed for systematic and controlled evaluations, alongside a symbolic intermediate representation that transforms tables into database schemas. This structured approach allows LLMs to generate and execute SQL queries, enhancing generalization and mitigating biases. By incorporating adaptive few-shot prompting with contextually tailored examples, our method achieves superior robustness, scalability, and performance. Experimental results consistently highlight improvements across key challenges, setting a new benchmark for robust temporal reasoning with LLMs.

In a real-world corpus, knowledge frequently recurs across documents but often contains inconsistencies due to ambiguous naming, outdated information, or errors, leading to complex interrelationships between contexts. Previous research has shown that language models struggle with these complexities, typically focusing on single factors in isolation. We classify these relationships into four types: distracting, ambiguous, counterfactual, and duplicated. Our analysis reveals that no single approach effectively addresses all these interrelationships simultaneously. Therefore, we introduce Context Organizer (CORG), a framework that organizes multiple contexts into independently processed groups. This design allows the model to efficiently find all relevant answers while ensuring disambiguation. CORG consists of three key components: a graph constructor, a reranker, and an aggregator. Our results demonstrate that CORG balances performance and efficiency effectively, outperforming existing grouping methods and achieving comparable results to more computationally intensive, single-context approaches.

Large language models (LLMs) have achieved remarkable performance on knowledge graph question answering (KGQA) tasks by planning and interacting with knowledge graphs. However, existing methods often confuse tool utilization with knowledge reasoning, harming readability of model outputs and giving rise to hallucinatory tool invocations, which hinder the advancement of KGQA. To address this issue, we propose Memory-augmented Query Reconstruction for LLM-based Knowledge Graph Reasoning (MemQ) to decouple LLM from tool invocation tasks using LLM-built query memory. By establishing a memory module with explicit descriptions of query statements, the proposed MemQ facilitates the KGQA process with natural language reasoning and memory-augmented query reconstruction. Meanwhile, we design an effective and readable reasoning to enhance the LLM's reasoning capability in KGQA. Experimental results that MemQ achieves state-of-the-art performance on widely used benchmarks WebQSP and CWQ.

Statistical topic modeling is widely used in political science to study text. Researchers examine documents of varying lengths, from tweets to speeches. There is ongoing debate on how document length affects the interpretability of topic models. We investigate the effects of aggregating short documents into larger ones based on natural units that partition the corpus. In our study, we analyze one million tweets by U.S. state legislators from April 2016 to September 2020. We find that for documents aggregated at the account level, topics are more associated with individual states than when using individual tweets. This finding is replicated with Wikipedia pages aggregated by birth cities, showing how document definitions can impact topic modeling results.

We present \llinstruct: An 8B instruction-tuned model that is designed to generate content for English Language Proficiency Assessments (ELPA) and related applications. Our work involves creating a new dataset of 70K instructions and explanations in the ELPA domain and using these to fine-tune Llama-3 8B models (SFT) of different sizes (e.g., SFT-17K, SFT-50K and SFT-70K). Human evaluations are conducted over unseen instructions to compare these SFT models against SOTA models (e.g., Dolly-2, Mistral, Llama-3 base version, and GPT-3.5). The findings show although all three SFT models perform comparably, the model trained on largest instruction dataset -- SFT-70K - leads to the most valid outputs ready for assessments. However, although the SFT models perform better than larger model, e.g., GPT 3.5 on the aspect of explanations of outputs, many outputs still need human interventions to make them actual ready for real world assessments.