Bills cruise past Lions to christen new home behind Josh Allen's five-touchdown performance Patriots' TreVeyon Henderson explains siding with family over DCF in custody dispute involving gender identity Colin Kaepernick accuses NFL of'colluding' against Macklemore amid'Free Palestine' controversy Virginia Governor Abigail Spanberger quietly rolls back Youngkin's plans to protect girls' sports Human vs. ChatGPT NFL Week 2 betting picks: AI looks to rebound after disastrous 0-5 debut NBA star Kyrie Irving jumps into NFL drama, offers to pay player's fine for Gaza-themed eye black NFL's highest-paid TE George Kittle reveals favorite purchase: A 50-foot flagpole for a massive American flag Bill Belichick would like you to know that his 25-year-old girlfriend isn't running things at UNC Bijan Robinson believes Texas' comeback over Ohio State can change entire season trajectory Barstool chief Dave Portnoy blasts Macklemore's shady past, strongly supports Patriots owner Robert Kraft Lane Kiffin's return to Ole Miss comes with betrayal, lawsuits and memories of him trying to burn it all down Paige Spiranac and her'cans' go to bat for Sydney Sweeney, Ole Miss-LSU gets nasty & UF alumni speak out Iran's AI-powered cyber threat sparks alarm over US infrastructure Raman would'catastrophically' push LA over the edge if elected: Former Trump campaign official'Gutfeld!': Tirade at 30K feet left him taped to a seat We have'no idea' about long-term effects of Chinese pesticides at Cali pot grows: Ex-DEA chief Gutfeld: If you're concerned about AI, fear the humans - not the device OutKick Sports Steve Sarkisian says he should have shown'more patience' after running from Holly Rowe interview The Texas Longhorns coach addressed the viral post-game moment ahead of the team's next contest against UTSA Craig breaks down why Arch Manning shouldn't have to apologize for laughimg at the Holly Rowe/Steve Sarkisian AI video Texas Longhorns head coach Steve Sarkisian went viral following his team's miraculous 24-23 comeback against Ohio State this past Saturday after running off from a postgame interview with ESPN's Holly Rowe. Sarkisian screamed, That's called Texas fight! Hook'em! before running off back to the locker room to celebrate the victory with his team . At the time, Rowe hadn't even asked her first question to him postgame, and she seemed quite upset as she darted out of camera view after Sarkisian ran away.

We study how two information feeds, a closed-form Markov estimator of residual sojourn and an online trained actor-critic, affect reneging and jockeying in a dual M/M/1 system. Analytically, for unequal service rates and total-time patience, we show that total wait grows linearly so abandonment is inevitable and the probability of a successful jockey vanishes as the backlog approaches towards infinity. Furthermore, under a mild sub-linear error condition both information models yield the same asymptotic limits (robustness). We empirically validate these limits and quantify finite backlog differences. Our findings show that learned and analytic feeds produce different delays, reneging rates and transient jockeying behavior at practical sizes, but converge to the same asymptotic outcome implied by our theory. The results characterize when value-of-information matters (finite regimes) and when it does not (asymptotics), informing lightweight telemetry and decision-logic design for low-cost, jockeying-aware systems.

Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? How much of Europe's oil still comes from Russia? Ukraine's Zelenskyy to meet European leaders in London over military aid Ukrainian President Volodymyr Zelenskyy is due to meet European leaders in the United Kingdom for talks on military aid to stave off future Russian aggression if a ceasefire stops the war now in its fourth year. Zelenskyy and British Prime Minister Keir Starmer are expected to be joined at the Foreign Office in London on Friday by NATO Secretary-General Mark Rutte, Danish Prime Minister Mette Frederiksen and Dutch Prime Minister Dick Schoof.

O1/R1 style large reasoning models (LRMs) signal a substantial leap forward over conventional instruction-following LLMs. By applying test-time scaling to generate extended reasoning paths, they establish many SOTAs across a wide range of complex reasoning tasks. However, recent studies show that LRMs are prone to suffer from overthinking -- the tendency to overcomplicate simple problems, leading to excessive strategy switching and long, convoluted reasoning traces that hinder their interpretability. To mitigate this issue, we conduct a systematic investigation into the reasoning efficiency of a broad set of LRMs and uncover a common dilemma: the difficulty in balancing multiple generation objectives such as correctness and brevity. Based on this discovery, we propose a test-time scaling method, EDIT (Efficient Dynamic Inference Trimming), which efficiently guides LRMs to identify the shortest correct reasoning paths at test time. EDIT employs constraint-guided generation while jointly tracking length and answer distributions under varying constraints, allowing it to select responses that strike an optimal balance between conciseness and correctness. Extensive experiments across diverse models and datasets show that EDIT substantially enhance the reasoning efficiency, producing compact yet informative outputs that improve readability and user experience.

Large language models (LLMs) have grown in their usage to provide support for question answering across numerous disciplines. The models on their own have already shown promise for answering basic questions, however fail quickly where expert domain knowledge is required or the question is nuanced. Scientific research often involves searching for relevant literature, distilling pertinent information from that literature and analysing how the findings support or contradict one another. The information is often encapsulated in the full text body of research articles, rather than just in the abstracts. Statements within these articles frequently require the wider article context to be fully understood. We have built an LLM-based system that performs such search and distillation of information encapsulated in scientific literature, and we evaluate our keyword based search and information distillation system against a set of biology related questions from previously released literature benchmarks. We demonstrate sparse retrieval methods exhibit results close to state of the art without the need for dense retrieval, with its associated infrastructure and complexity overhead. We also show how to increase the coverage of relevant documents for literature review generation.

Multi-trait automated essay scoring (AES) systems provide a fine-grained evaluation of an essay's diverse aspects. While they excel in scoring, prior systems fail to explain why specific trait scores are assigned. This lack of transparency leaves instructors and learners unconvinced of the AES outputs, hindering their practical use. To address this, we propose a self-explainable Rationale-Driven Multi-trait automated Essay scoring (RaDME) framework. RaDME leverages the reasoning capabilities of large language models (LLMs) by distilling them into a smaller yet effective scorer. This more manageable student model is optimized to sequentially generate a trait score followed by the corresponding rationale, thereby inherently learning to select a more justifiable score by considering the subsequent rationale during training. Our findings indicate that while LLMs underperform in direct AES tasks, they excel in rationale generation when provided with precise numerical scores. Thus, RaDME integrates the superior reasoning capacities of LLMs into the robust scoring accuracy of an optimized smaller model. Extensive experiments demonstrate that RaDME achieves both accurate and adequate reasoning while supporting high-quality multi-trait scoring, significantly enhancing the transparency of AES.