In this still from the Sora 2 promotional video, an ai-generated cameo of Sam Altman shows us through worlds of generated content. Last week OpenAI released Sora, a TikTok-style app that presents an endless feed of exclusively AI-generated videos, each up to 10 seconds long. The app allows you to create a "cameo" of yourself--a hyperrealistic avatar that mimics your appearance and voice--and insert other peoples' cameos into your own videos (depending on what permissions they set). To some people who believed earnestly in OpenAI's promise to build AI that benefits all of humanity, the app is a punchline. A former OpenAI researcher who left to build an AI-for-science startup referred to Sora as an "infinite AI tiktok slop machine." That hasn't stopped it from soaring to the top spot on Apple's US App Store.

OpenAI has rolled out a social app powered by Sora 2, its artificial intelligence video generator, which was quickly flooded with videos featuring iconic Japanese intellectual property. In a short video widely shared online, Pokemon frolic through a lush green field while OpenAI CEO Sam Altman watches from the sidelines. He then turns to the camera and says, "I hope Nintendo doesn't sue us." Named for the Japanese word for "sky" due to the product's "limitless potential," according to company lore, the platform was released to a handful of users last week and was quickly flooded with videos featuring iconic Japanese intellectual property (IP), including Pokemon, One Piece and Dragon Ball Z. Such videos, which are only possible to generate because of OpenAI "training" Sora 2 on the work of human creators, have been widely branded "AI slop" by critics.

Jony Ive Says He Wants His OpenAI Devices to'Make Us Happy' "I don't think we have an easy relationship with our technology at the moment," the former Apple designer said at OpenAI's developer conference in San Francisco on Monday. At OpenAI's developer conference in San Francisco on Monday, CEO Sam Altman and ex-Apple designer Jony Ive spoke in vague terms about the "family of devices" the pair are currently working to develop . "As great as phones and computers are, there's something new to do," Altman said on stage with Ive. The duo confirmed that OpenAI is working on more than one hardware product but finer details, ranging from use cases to to specifications, remain under wraps. Figuring out new computing form factors is hard," said Altman in a media briefing earlier in the day. "I think we have a chance to do something amazing, but it will take a while." Ive said that his team has generated "15 to 20 really compelling product" ideas on the journey to find the right kind of ...

Conformal prediction offers a powerful framework for building distribution-free prediction intervals for exchangeable data. Existing methods that extend conformal prediction to sequential data rely on fitting a relatively complex model to capture temporal dependencies. However, these methods can fail if the sample size is small and often require expensive retraining when the underlying data distribution changes. To overcome these limitations, we propose Reservoir Conformal Prediction (ResCP), a novel training-free conformal prediction method for time series. Our approach leverages the efficiency and representation learning capabilities of reservoir computing to dynamically reweight conformity scores. In particular, we compute similarity scores among reservoir states and use them to adaptively reweight the observed residuals at each step. With this approach, ResCP enables us to account for local temporal dynamics when modeling the error distribution without compromising computational scalability. We prove that, under reasonable assumptions, ResCP achieves asymptotic conditional coverage, and we empirically demonstrate its effectiveness across diverse forecasting tasks.

Deep generative models have made significant advances in generating complex content, yet conditional generation remains a fundamental challenge. Existing conditional generative adversarial networks often struggle to balance the dual objectives of assessing authenticity and conditional alignment of input samples within their conditional discriminators. To address this, we propose a novel discriminator design that integrates three key capabilities: unconditional discrimination, matching-aware supervision to enhance alignment sensitivity, and adaptive weighting to dynamically balance all objectives. Specifically, we introduce Sum of Naturalness and Alignment (SONA), which employs separate projections for naturalness (authenticity) and alignment in the final layer with an inductive bias, supported by dedicated objective functions and an adaptive weighting mechanism. Extensive experiments on class-conditional generation tasks show that \ours achieves superior sample quality and conditional alignment compared to state-of-the-art methods. Furthermore, we demonstrate its effectiveness in text-to-image generation, confirming the versatility and robustness of our approach.

Despite recent progress, deep learning methods for tabular data still struggle to compete with traditional tree-based models. A key challenge lies in modeling complex, dataset-specific feature interactions that are central to tabular data. Graph-based tabular deep learning (GTDL) methods aim to address this by representing features and their interactions as graphs. However, existing methods predominantly optimize predictive accuracy, neglecting accurate modeling of the graph structure. This position paper argues that GTDL should move beyond prediction-centric objectives and prioritize the explicit learning and evaluation of feature interactions. Using synthetic datasets with known ground-truth graph structures, we show that existing GTDL methods fail to recover meaningful feature interactions. Moreover, enforcing the true interaction structure improves predictive performance. This highlights the need for GTDL methods to prioritize quantitative evaluation and accurate structural learning. We call for a shift toward structure-aware modeling as a foundation for building GTDL systems that are not only accurate but also interpretable, trustworthy, and grounded in domain understanding.

Generative models have witnessed remarkable progress in recent years, with diffusion models (Sohl-Dickstein et al., 2015) emerging as a dominant paradigm across various domains including images (Ho et al., 2020; Dhariwal & Nichol, 2021), audio (Kong et al., 2021) and video (Ho et al., 2022). While continuous diffusion models have garnered significant attention, discrete diffusion models (Austin et al., 2021; Campbell et al., 2022; Gu et al., 2022; Lou et al., 2024) offer compelling advantages for inherently discrete data, such as tokenized representations in all the above domains. Among discrete diffusion approaches, masked diffusion models (Sahoo et al., 2024; Shi et al., 2024; Ou et al., 2025) have demonstrated exceptional performance, particularly in generating high-quality samples in language domains. However, their sampling process remains computationally intensive, requiring hundreds of function evaluations. Recent efforts to accelerate sampling in discrete/masked diffusion models have explored various directions, including optimized scheduling (Park et al., 2025), distillation (Deschenaux & Gulcehre, 2025; Hayakawa et al., 2025; Zhu et al., 2025), and adaptive token selection (Kim et al., 2025; Ben-Hamu et al., 2025).

Policy targets evolve faster than the Couple Model Intercomparison Project cycles, complicating adaptation and mitigation planning that must often contend with outdated projections. Climate model output emulators address this gap by offering inexpensive surrogates that can rapidly explore alternative futures while staying close to Earth System Model (ESM) behavior. We focus on emulators designed to provide inputs to impact models. Using monthly ESM fields of near-surface temperature, precipitation, relative humidity, and wind speed, we show that deep generative models have the potential to model jointly the distribution of variables relevant for impacts. The specific model we propose uses score-based diffusion on a spherical mesh and runs on a single mid-range graphical processing unit. We introduce a thorough suite of diagnostics to compare emulator outputs with their parent ESMs, including their probability densities, cross-variable correlations, time of emergence, or tail behavior. We evaluate performance across three distinct ESMs in both pre-industrial and forced regimes. The results show that the emulator produces distributions that closely match the ESM outputs and captures key forced responses. They also reveal important failure cases, notably for variables with a strong regime shift in the seasonal cycle. Although not a perfect match to the ESM, the inaccuracies of the emulator are small relative to the scale of internal variability in ESM projections. We therefore argue that it shows potential to be useful in supporting impact assessment. We discuss priorities for future development toward daily resolution, finer spatial scales, and bias-aware training. Code is made available at https://github.com/shahineb/climemu.

We introduce a cumulant-expansion framework for quantifying how large language models (LLMs) internalize higher-order statistical structure during next-token prediction. By treating the softmax entropy of each layer's logit distribution as a perturbation around its "center" distribution, we derive closed-form cumulant observables that isolate successively higher-order correlations. Empirically, we track these cumulants in GPT-2 and Pythia models on Pile-10K prompts. (i) Structured prompts exhibit a characteristic rise-and-plateau profile across layers, whereas token-shuffled prompts remain flat, revealing the dependence of the cumulant profile on meaningful context. (ii) During training, all cumulants increase monotonically before saturating, directly visualizing the model's progression from capturing variance to learning skew, kurtosis, and higher-order statistical structures. (iii) Mathematical prompts show distinct cumulant signatures compared to general text, quantifying how models employ fundamentally different processing mechanisms for mathematical versus linguistic content. Together, these results establish cumulant analysis as a lightweight, mathematically grounded probe of feature-learning dynamics in high-dimensional neural networks.

Reinforcement learning (RL) has become central to enhancing reasoning in large language models (LLMs). Yet on-policy algorithms such as Group Relative Policy Optimization (GRPO) often suffer in early training: noisy gradients from low-quality rollouts lead to unstable updates and inefficient exploration. We introduce Slow-Fast Policy Optimization (SFPO), a simple yet efficient framework to address these limitations via decomposing each step into three stages: a short fast trajectory of inner steps on the same batch, a reposition mechanism to control off-policy drift, and a final slow correction. This reposition-before-update design preserves the objective and rollout process unchanged, making SFPO plug-compatible with existing policy-gradient pipelines. Extensive experiments demonstrate that SFPO consistently improves stability, reduces rollouts, and accelerates convergence of reasoning RL training. Specifically, it outperforms GRPO by up to 2.80 points in average on math reasoning benchmarks. It also achieves up to 4.93\texttimes{} fewer rollouts and a 4.19\texttimes{} reduction in wall-clock time to match GRPO's best accuracy.