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You don't want a soda bath. Breakthroughs, discoveries, and DIY tips sent six days a week. Picture this: You're at a picnic on a hot day and grab a cold Coke out of the cooler. It's wet, you drop it, but think "eh, it's fine." You pop the can open and SPLASH--there's soda all over white shirt.

The rise of large language models (LLMs) has significantly transformed both the construction and application of information retrieval (IR) systems. However, current interactions between IR systems and LLMs remain limited, with LLMs merely serving as part of components within IR systems, and IR systems being constructed independently of LLMs. This separated architecture restricts knowledge sharing and deep collaboration between them. In this paper, we introduce Self-Retrieval, a novel end-to-end LLM-driven information retrieval architecture.

We study the fundamental problems of Gaussian mean estimation and linear regression with Gaussian covariates in the presence of Huber contamination. Our main contribution is the design of the first sample near-optimal and almost linear-time algorithms with optimal error guarantees for both these problems. Specifically, for Gaussian robust mean estimation on Rd with contamination parameter ϵ (0,ϵ0) for a small absolute constant ϵ0, we give an algorithm with sample complexity n = O(d/ϵ2) and almost linear runtime that approximates the target mean within ℓ2-error O(ϵ). This improves on prior work that achieved this error guarantee with polynomially suboptimal sample and time complexity. For robust linear regression, we give the first algorithm with sample complexity n = O(d/ϵ2) and almost linear runtime that approximates the target regressor within ℓ2-error O(ϵ). This is the first polynomial sample and time algorithm achieving the optimal error guarantee, answering an open question in the literature. At the technical level, we develop a methodology that yields almost-linear time algorithms for multi-directional filtering that may be of broader interest.

ThismakesROARmorereliable.4 Reviewer 1 (R1) re: portrayal of human studies: R1 correctly points out our portrayal of human stud-5 ies requires more nuance. We would be glad to correct this and will update the manuscript accordingly.6 As the reviewer assumed correctly, the gap between estimators is far larger than the variance.10 But as the reviewer points out, sometimes the curve itself provides additional information.12 This44 minimum deletion area is identified by perturbing and evaluating the model output without retraining.

The company launched its Sora 2 model in September 2025 alongside a dedicated Sora app. OpenAI plans to add its Sora video generation model directly into ChatGPT, reports . The standalone Sora app was seen as a smash hit when it launched alongside Sora 2 in September 2025, but interest in the video generation app has fallen in the time since as users ran into limits on the amount and kinds of videos they could create. Adding Sora to the ChatGPT could give the model a second life, and ideally grow the ChatGPT app's weekly active users from the 900 million OpenAI reported in February, to a billion or more. According to, the standalone Sora app will stick around after the model is integrated, even though the app has fallen out of the App Store's top 100 free apps and only a small number of users reportedly share their videos publicly in the app.

A theoretical performance analysis of the graph neural network (GNN) is presented. For classification tasks, the neural network approach has the advantage in terms of flexibility that it can be employed in a data-driven manner, whereas Bayesian inference requires the assumption of a specific model. A fundamental question is then whether GNN has a high accuracy in addition to this flexibility. Moreover, whether the achieved performance is predominately a result of the backpropagation or the architecture itself is a matter of considerable interest. To gain a better insight into these questions, a mean-field theory of a minimal GNN architecture is developed for the graph partitioning problem. This demonstrates a good agreement with numerical experiments.

Many structured prediction problems admit a natural loss function for evaluation such as the edit-distance or $n$-gram loss. However, existing learning algorithms are typically designed to optimize alternative objectives such as the cross-entropy. This is because a na\{i}ve implementation of the natural loss functions often results in intractable gradient computations. In this paper, we design efficient gradient computation algorithms for two broad families of structured prediction loss functions: rational and tropical losses. These families include as special cases the $n$-gram loss, the edit-distance loss, and many other loss functions commonly used in natural language processing and computational biology tasks that are based on sequence similarity measures. Our algorithms make use of weighted automata and graph operations over appropriate semirings to design efficient solutions. They facilitate efficient gradient computation and hence enable one to train learning models such as neural networks with complex structured losses.