Neural processes (NPs) are a class of models that learn stochastic processes directly from data and can be used for inference, sampling and conditional sampling. We introduce a new NP model based on flow matching, a generative modeling paradigm that has demonstrated strong performance on various data modalities. Following the NP training framework, the model provides amortized predictions of conditional distributions over any arbitrary points in the data. Compared to previous NP models, our model is simple to implement and can be used to sample from conditional distributions using an ODE solver, without requiring auxiliary conditioning methods. In addition, the model provides a controllable tradeoff between accuracy and running time via the number of steps in the ODE solver. We show that our model outperforms previous state-of-the-art neural process methods on various benchmarks including synthetic 1DGaussian processes data, 2D images, and real-world weather data.

Training and inference of Large Language Models (LLMs) with tensor-parallelism requires substantial communication to synchronize activations. Our findings suggest that with a few minor adjustments to current practices, LLMs can be trained without fully synchronizing activations, reducing bandwidth demands. We name this "Communication-Aware Architecture for Tensor-parallelism" (CAAT-Net). We train a 7B parameter CAAT-Net model and show that tensor-parallel communication can be reduced by up to 50% with no significant drop in pretraining accuracy across nearly all evaluated benchmarks. We also experiment with smaller 130M and 1.1B models to show the robustness and scalability of our method. We find that, in some scenarios, validation loss can even improve when reducing communication. Finally, we demonstrate how CAAT-Net accelerates both training and inference workloads across various settings and model sizes.

We study the problem of learning in the presence of an adversary that can corrupt an η fraction of the training examples with the goal of causing failure on a specific test point. In the realizable setting, prior work established that the optimal error under such instance-targeted poisoning attacks scales as Θ(dη), where d is the VC dimension of the hypothesis class [Hanneke, Karbasi, Mahmoody, Mehalel, and Moran (NeurIPS 2022)]. In this work, we resolve the corresponding question in the agnostic setting. We show that the optimal excess error is eΘ( dη), answering one of the main open problems left by Hanneke et al. To achieve this rate, it is necessary to use randomized learners: Hanneke et al. showed that deterministic learners can be forced to suffer error close to 1 even under small amounts of poisoning.

The BBC has been given rare access to the part of southern Lebanon that is under Israeli occupation, as part of a humanitarian convoy of the Order of Malta distributing aid to Christian villages that have been isolated because of the war. The mission happened on Thursday, a day before the announcement of a new ceasefire in the conflict between Israel and the Shia Muslim armed group Hezbollah. The team saw the Israeli military presence but was not allowed to film much of the journey. Israel says it has no intention of withdrawing its troops from Lebanon, and that its plan is to create a security zone along the border, Hezbollah-free, to protect its northern communities from the group's rockets and drones. In the occupied areas, mainly Shia villages have been completely destroyed by Israeli air strikes or demolitions.

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We address the problem of looking into the water from the air, where we seek to remove image distortions caused by refractions at the water surface. Our approach is based on modeling the different water surface structures at various points in time, assuming the underlying image is constant. To this end, we propose a model that consists of two neural-field networks. The first network predicts the height of the water surface at each spatial position and time, and the second network predicts the image color at each position. Using both networks, we reconstruct the observed sequence of images and can therefore use unsupervised training.

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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.

Could Israel sabotage the deal? Leaders of the G7 have pledged at a summit in France to strengthen Ukraine's air defences and increase pressure on Moscow's war economy, including by tightening sanctions on the Russian oil and gas sectors. "We, the Leaders of the G7, stand united in our unwavering support for Ukraine in defending its freedom, sovereignty, and territorial integrity," a statement released on Wednesday said. They added that the bloc, which includes Canada, France, Germany, Italy, Japan, the United Kingdom, the United States and the European Union, was "ready to consider extending to Ukraine the benefit of licenses to allow for an increase in Ukraine's military production". President Volodymyr Zelenskyy, who joined the summit on Tuesday and also held bilateral talks with US President Donald Trump and Secretary of State Marco Rubio, has been pressing allies for more than a year to allow Ukraine to produce its own interceptors because of a shortage of US anti-ballistic systems and interceptors.

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