Self-play fine-tuning has demonstrated promising abilities in adapting large language models (LLMs) to downstream tasks with limited real-world data. The basic principle is to iteratively refine the model with real samples and synthetic ones generated from itself. However, the existing methods primarily focus on the relative gaps between the rewards for two types of data, neglecting their absolute values. Through theoretical analysis, we identify that the gap-based methods suffer from unstable evolution, due to the potentially degenerated objectives. To address this limitation, we introduce a novel self-play fine-tuning method, namely SelfPlAy via Noise Contrastive Estimation (SPACE), which leverages noise contrastive estimation to capture the real-world data distribution. Specifically, SPACE treats synthetic samples as auxiliary components, and discriminates them from the real ones in a binary classification manner.

Multimodal large language models (MLLMs) have achieved remarkable progress in video understanding. However, hallucination, where the model generates plausible yet incorrect outputs, persists as a significant and under-addressed challenge in the video domain. Among existing solutions, activation engineering has proven successful in mitigating hallucinations in LLMs and ImageLLMs, yet its applicability to VideoLLMs remains largely unexplored. In this work, we are the first to systematically investigate the effectiveness and underlying mechanisms of activation engineering for mitigating hallucinations in VideoLLMs. We initially conduct an investigation of the key factors affecting the performance of activation engineering and find that a model's sensitivity to hallucination depends on temporal variation rather than task type. Moreover, selecting appropriate internal modules and dataset for activation engineering is critical for reducing hallucination. Guided by these findings, we propose a temporal-aware activation engineering framework for VideoLLMs, which adaptively identifies and manipulates hallucination-sensitive modules based on the temporal variation characteristic, substantially mitigating hallucinations without additional LLM fine-tuning. Experiments across multiple models and benchmarks demonstrate that our method markedly reduces hallucination in VideoLLMs, thereby validating the robustness of our findings2.

For Iran's Athletes, There Is No Separating Sports From Politics From defections and protests to moments of national pride, the 2026 World Cup arrives amid decades of tension between identity and the state. Iran's national soccer team has made its 2026 World Cup debut amid a tumultuous backdrop: an abrupt and tentative ceasefire after months of war, an extraordinary set-up in Mexico after the US prevented the team from residing in-country between matches, and political uncertainty that has now expanded to the international stage. But for many Iranians, professional sports have always sat at an intersection between athleticism, identity, and politics. From sporting defections and political activism to moments of immense national sporting pride, the trajectory of Iranian sports underscores what's at stake this World Cup. The Iranian team, on Tuesday morning, drew 2-2 in their debut against New Zealand and will next face Belgium and Egypt, traveling to and from Mexico in between.

Recent advances of Reinforcement Learning (RL) have highlighted its potential in complex reasoning tasks, yet effective training often relies on external supervision, which limits the broader applicability. In this work, we propose a novel self-rewarding reinforcement learning framework to enhance Large Language Model (LLM) reasoning by leveraging the consistency of intermediate reasoning states across different reasoning trajectories. Our key insight is that correct responses often exhibit consistent trajectory patterns in terms of model likelihood: their intermediate reasoning states tend to converge toward their own final answers (high consistency) with minimal deviation toward other candidates (low volatility). Inspired by this observation, we introduce CoVo, an intrinsic reward mechanism that integrates Consistency and Volatility via a robust vector-space aggregation strategy, complemented by a curiosity bonus to promote diverse exploration. CoVo enables LLMs to perform RL in a self-rewarding manner, offering a scalable pathway for learning to reason without external supervision. Extensive experiments on diverse reasoning benchmarks show that CoVo achieves performance comparable to or even surpassing supervised RL. Our code is available at https://github.com/sastpg/CoVo.

Large Language Models (LLMs) have achieved state-of-the-art results across diverse domains, yet their development remains reliant on vast amounts of publicly available data, raising concerns about data scarcity and the lack of access to domainspecific, sensitive information. Federated Learning (FL) presents a compelling framework to address these challenges by enabling decentralized fine-tuning on pre-trained LLMs without sharing raw data. However, the compatibility and performance of pre-trained LLMs in FL settings remain largely under explored. We introduce the FlowerTune LLMLeaderboard, a first-of-its-kind benchmarking suite designed to evaluate federated fine-tuning of LLMs across four diverse domains: general NLP, finance, medical, and coding. Each domain includes federated instruction-tuning datasets and domain-specific evaluation metrics. Our results, obtained through a collaborative, open-source and community-driven approach, provide the first comprehensive comparison across 26 pre-trained LLMs with different aggregation and fine-tuning strategies under federated settings, offering actionable insights into model performance, resource constraints, and domain adaptation. This work lays the foundation for developing privacy-preserving, domain-specialized LLMs for real-world applications.

Direct preference optimization (DPO) methods have shown strong potential in aligning text-to-image diffusion models with human preferences by training on paired comparisons. These methods improve training stability by avoiding the REINFORCE algorithm but still struggle with challenges such as accurately estimating image probabilities due to the non-linear nature of the sigmoid function and the limited diversity of offline datasets. In this paper, we introduce Diffusion Denoising Ranking Optimization (Diffusion-DRO), a new preference learning framework grounded in inverse reinforcement learning. Diffusion-DRO removes the dependency on a reward model by casting preference learning as a ranking problem, thereby simplifying the training objective into a denoising formulation and overcoming the non-linear estimation issues found in prior methods. Moreover, Diffusion-DRO uniquely integrates offline expert demonstrations with online policy-generated negative samples, enabling it to effectively capture human preferences while addressing the limitations of offline data. Comprehensive experiments show that Diffusion-DRO delivers improved generation quality across a range of challenging and unseen prompts, outperforming state-of-the-art baselines in both both quantitative metrics and user studies.

Israeli forces have carried out new strikes in southern Lebanon, state media say, despite renewed criticism from US President Donald Trump of Israel's actions in the country. Israeli drone strikes injured several people in Mansouri and Aaziyyeh on Wednesday, while jets attacked Nabatieh al-Fawqa and Kfar Tebnit, Lebanon's National News Agency reported. Israel's military has not commented, but it did say five soldiers were injured in a drone attack in Lebanon by the Iran-backed armed group Hezbollah. Mediator Pakistan has said the deal between the US and Iran to end the war includes Lebanon. On Tuesday, Trump said Israel's prime minister needed to be more responsible with respect to Lebanon.

'The CEOs are telling us, "We're on track to create superhuman intelligence, which has a good chance of causing human extinction."' 'The CEOs are telling us, "We're on track to create superhuman intelligence, which has a good chance of causing human extinction."' Will it take a'Chernobyl-scale disaster' for us to regulate cyber weapons of mass destruction? T he AI company Anthropic has been making major headlines recently. Its trillion-dollar IPO plan and its blood feud with secretary of defense Pete Hegseth have attracted much attention, but two other events may be even more consequential.

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WIRED goes on location--and on camera--with the cult hit. On March 16, 2026, at 5:45 pm in a leafy suburb of Atlanta called Sandy Springs, police pound on the door of a neglected French Country-style mansion, rifles at the ready, bodycams rolling. Minutes earlier, a distress call came from someone claiming to be hiding from a gunman in the mansion's downstairs bathroom. The dispatcher heard a gunshot ring out in the distance, then the line disconnected. "Open the door!" an officer yells. A calm young man with a mullet and woolly eyebrows steps out, hands raised. The police ask him who else is in the house. "Just my friends," he replies, as seven other young people, men and women, silently file out behind him, less evidently relaxed. They remain outside while two officers search the house. Inside the mansion there are no immediate signs of a massacre, but the decor alone arouses suspicion. All of the windows are frosted over, so only a chilly light leaks in. The place is a mess, and the walls are adorned with lurid, seemingly AI-generated art: a frowning baby holding an assault rifle, a rubber ducky bobbing in a mug of what looks like black coffee, a lidless and levitating eyeball crying into a martini glass. The rooms are painted primary colors, grass green and cherry red, like a kindergarten class. A vape dangles from a doorframe by a chain, suspended at mouth level. The pantry is practically empty. The bedroom is a dormitory featuring seven identical twin beds. No one is hiding in the bathroom. The call, it seems, was a prank. The police return to the driveway and ask, "What is it that you guys are doing here?" "We're just livestreaming," says a man in a camo hat named Matt. "You guys don't have any firearms or anything inside the house?" There are guns in the house, Matt says, for self-defense. Fans of their livestream can be obsessive, he explains, and tend to have perverse ideas about jokes. The officer asks to see their weapons, and they go downstairs. The room is cluttered with ergonomic swivel chairs, desks strewn with takeout containers and energy drinks, two flatscreen TVs, and a dozen computer monitors.