Trump declares that Keir Starmer'failed badly' as UK's Prime Minister as he CRIES while resigning Boston's Scotland-loving residents claim England fans are'ruining the vibe' compared to the Tartan Army'Al Roker is an absolute ****': KENNEDY's Today show insider gives brutal behind-the-scenes verdict on beloved weatherman and names other two-faced NBC hosts Call me cynical, but the real reason Gruesome Twosome Harry and Meghan are returning to the UK is just so obvious... and highly humiliating: MAUREEN CALLAHAN Putin'prepares mass call-up' for the Ukraine meat-grinder - as video shows Russian veteran with no legs threatening recruiter with a knife in sign of growing resistance facing desperate Kremlin Secret life of John Travolta's daughter Ella Bleu: New details about'unusual' relationship with her dad revealed by insiders amid fears that aspiring actress is'stuck' Johnny Depp's ex Amber Heard gives rare glimpse of daughter Oonagh, five, after finishing 10k race in Spain Rock band playing at Madison Square Garden when fan, 51, plunged to his death break silence... as investigators probe his final moments Colorado siblings VANISH from home in middle of the night... and police still can't find them a week later Revealed: Emotional handwritten letter Iran's'oppressed' World Cup stars left in SoFi Stadium locker room No one can see the real reason Jelly Roll divorced Bunnie XO. New footage shows tourists fleeing with their bags as fire destroys Dominican Republic resort - and it's revealed Italian celebrity died from carbon monoxide fumes Unseen trove of intimate letters from Nicole Brown Simpson's passionate affair: Her lover reveals jealous OJ's peeping tom compulsion and relentless stalking Map reveals every state's favorite fast food cheeseburger Family-man facade of award-winning children's swim coach is shattered by disturbing teen babysitter claims: Read all the vile texts Fidel Castro's'secret' daughter speaks out to reveal truth about those Justin Trudeau'sibling' rumors: 'Sensitive subject' Kyle Busch's widow posts heartbreaking Father's Day tribute a month on from his shock death at 41: 'Cards were already made' US marks'encouraging progress' in peace talks with Iran after summit was nearly derailed by Trump's fiery threat I'm 52, and after ending my sexless marriage I slept with men in every decade from 30s to 60s. Here's EXACTLY what happened with each of them and the surprising truth about who was best - and worst! For years, it's been believed that the sun will start to expand in around five billion years, swallowing our planet in the process. However, a new study suggests that this might not be the case after all.

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However, a prevalent issue is "superficial self-reflection", where models fail to robustly verify their own outputs. We introduce RISE (Reinforcing Reasoning with Self-Verification), a novel online RL framework designed to tackle this. RISE explicitly and simultaneously trains an LLM to improve both its problemsolving and self-verification abilities within a single, integrated RL process. The core mechanism involves leveraging verifiable rewards from an outcome verifier to provide on-the-fly feedback for both solution generation and self-verification tasks. In each iteration, the model generates solutions, then critiques its own onpolicy generated solutions, with both trajectories contributing to the policy update. Extensive experiments on diverse mathematical reasoning benchmarks show that RISE consistently improves model's problem-solving accuracy while concurrently fostering strong self-verification skills. Our analyses highlight the advantages of online verification and the benefits of increased verification compute.

In real world, the observed label distribution of a dataset often mismatches its true distribution due to noisy labels. In this situation, noisy labels learning (NLL) methods directly integrated with long-tailed learning (LTL) methods tend to fail due to a dilemma: NLL methods normally rely on unbiased model predictions to recover true distribution by selecting and correcting noisy labels; while LTL methods like logit adjustment depends on true distributions to adjust biased predictions, leading to a deadlock of mutual dependency defined in this paper. To address this, we propose Unlocker, a bilevel optimization framework that integrates NLL methods and LTL methods to iteratively disentangle this deadlock. The inner optimization leverages NLL to train the model, incorporating LTL methods to fairly select and correct noisy labels. The outer optimization adaptively determines an adjustment strength, mitigating model bias from over-or under-adjustment. We also theoretically prove that this bilevel optimization problem is convergent by transferring the outer optimization target to an equivalent problem with a closed-form solution. Extensive experiments on synthetic and real-world datasets demonstrate the effectiveness of our method in alleviating model bias and handling long-tailed noisy label data. Code is available at https://github.com/ChenShu248/Unlocker.

Language model outputs are not always reliable, thus prompting research into how to adapt model responses based on uncertainty. Common approaches include: abstention, where models refrain from generating responses when uncertain; and linguistic calibration, where models hedge their statements using uncertainty quantifiers. However, abstention can withhold valuable information, while linguistically calibrated responses are often challenging to leverage in downstream tasks. We propose a unified view, Conformal Linguistic Calibration (CLC), which reinterprets linguistic calibration as answer set prediction. First we present a framework connecting abstention and linguistic calibration through the lens of linguistic pragmatics. We then describe an implementation of CLC that allows for controlling the level of imprecision in model responses. Results demonstrate our method produces calibrated outputs with conformal guarantees on factual accuracy. Further, our approach enables fine-tuning models to perform uncertainty-aware adaptive claim rewriting, offering a controllable balance between factuality and specificity.1

Deep learning techniques have driven significant progress in various analytical tasks within 3D genomics in computational biology. However, a holistic understanding of 3D genomics knowledge remains underexplored. Here, we propose MIX-HIC, the first multimodal foundation model of 3D genome that integrates both Hi-C contact maps and epigenomic tracks, which obtains unified and comprehensive semantics.

Cinematography, the fundamental visual language of film, is essential for conveying narrative, emotion, and aesthetic quality. While recent Vision-Language Models (VLMs) demonstrate strong general visual understanding, their proficiency in comprehending the nuanced cinematic grammar embedded within individual shots remains largely unexplored and lacks robust evaluation.

Most counterfactual inference frameworks traditionally assume acyclic structural causal models (SCMs), i.e. directed acyclic graphs (DAGs).

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We study the problem of minimizing the sum of a smooth function and a nonsmooth convex regularizer over a compact Riemannian submanifold embedded in Euclidean space. By introducing an auxiliary splitting variable, we propose an adaptive Riemannian alternating direction method of multipliers (ARADMM), which, for the first time, achieves convergence without requiring smoothing of the nonsmooth term. Our approach involves only one Riemannian gradient evaluation and one proximal update per iteration. Through careful and adaptive coordination of the stepsizes and penalty parameters, we establish an optimal iteration complexity of order O(ϵ 3) for finding an ϵ-approximate KKT point, matching the complexity of existing smoothing technique-based Riemannian ADMM methods. Extensive numerical experiments on sparse PCA and robust subspace recovery demonstrate that our ARADMM consistently outperforms state-of-the-art Riemannian ADMM variants in convergence speed and solution quality.