On Friday, Elon Musk once again pledged to depart his role at DOGE, taking with him his bad personality, weird public behavior, complicated family life, troubled businesses, alleged regular illegal drug use, compulsive social media habits, exploding rockets, messianic conviction that he control all of earth's resources so as to colonize Mars, and a remarkably poor track record in his brief life as a quasi-public servant. He leaves behind the incredible destruction DOGE has wrought, and of course, DOGE itself, which will continue its work, as Project 2025 architect and Office of Management and Budget director Russell Vought reportedly floats making its cuts permanent without the approval of Congress. Even Trump says Musk is "really not leaving." But it would be a mistake to think that Musk's grip on the government is lessening; beyond his continued relationship with the Trump administration, Musk's companies will still have billions in lucrative and influential federal contracts. And as his recent travel shows, there are clear signs that Musk is also using his relationship with President Trump to pursue business, especially in the Middle East.

What message does Ukraine's Operation Spider's Web send to Russia and US? Ukraine carries out large-scale drone strikes on multiple Russian airbases.Read more Eighteen months in the making, Ukraine's Operation Spider's Web saw hundreds of AI-trained drones target military aircraft deep inside Russia's borders. Ukrainian President Volodymyr Zelenskyy says Sunday's attacks will go down in history. He followed them up with a proposal for an unconditional ceasefire as the two sides met in Istanbul. The European Union is preparing its 18th package of sanctions on Russia, while US President Donald Trump has threatened to use "devastating" measures against Russia if he feels the time is right. So, is the time right now?

Representing 3D scenes from multiview images remains a core challenge in computer vision and graphics, requiring both reliable rendering and reconstruction, which often conflicts due to the mismatched prioritization of image quality over precise underlying scene geometry. Although both neural implicit surfaces and explicit Gaussian primitives have advanced with neural rendering techniques, current methods impose strict constraints on density fields or primitive shapes, which enhances the affinity for geometric reconstruction at the sacrifice of rendering quality. To address this dilemma, we introduce GSDF, a dual-branch architecture combining 3D Gaussian Splatting (3DGS) and neural Signed Distance Fields (SDF). Our approach leverages mutual guidance and joint supervision during the training process to mutually enhance reconstruction and rendering. Specifically, our method guides the Gaussian primitives to locate near potential surfaces and accelerates the SDF convergence. This implicit mutual guidance ensures robustness and accuracy in both synthetic and real-world scenarios. Experimental results demonstrate that our method boosts the SDF optimization process to reconstruct more detailed geometry, while reducing floaters and blurry edge artifacts in rendering by aligning Gaussian primitives with the underlying geometry.

Central to our strategy is the collaboration between vision models and LLM to craft explanations. On one hand, the LLM is harnessed to delineate hierarchical visual attributes, while concurrently, a text-to-image API retrieves images that are most aligned with these textual concepts. By mapping the collected texts and images to the vision model's embedding space, we construct a hierarchy-structured visual embedding tree. This tree is dynamically pruned and grown by querying the LLM using language templates, tailoring the explanation to the model. Such a scheme allows us to seamlessly incorporate new attributes while eliminating undesired concepts based on the model's representations. When applied to testing samples, our method provides human-understandable explanations in the form of attributeladen trees. Beyond explanation, we retrained the vision model by calibrating it on the generated concept hierarchy, allowing the model to incorporate the refined knowledge of visual attributes. To access the effectiveness of our approach, we introduce new benchmarks and conduct rigorous evaluations, demonstrating its plausibility, faithfulness, and stability.

We introduce the Adaptive Skills, Adaptive Partitions (ASAP) framework that (1) learns skills (i.e., temporally extended actions or options) as well as (2) where to apply them. We believe that both (1) and (2) are necessary for a truly general skill learning framework, which is a key building block needed to scale up to lifelong learning agents. The ASAP framework can also solve related new tasks simply by adapting where it applies its existing learned skills. We prove that ASAP converges to a local optimum under natural conditions. Finally, our experimental results, which include a RoboCup domain, demonstrate the ability of ASAP to learn where to reuse skills as well as solve multiple tasks with considerably less experience than solving each task from scratch.

From the onset of infanthood, humans naturally develop the ability to closely observe and interpret the visual gaze of others. This skill, known as gaze following, holds significance in developmental theory as it enables us to grasp another person's mental state, emotions, intentions, and more [6]. In computer vision, gaze following is defined as the prediction of the pixel coordinates where a person in the image is focusing their attention. Existing methods in this research area have predominantly centered on pinpointing the gaze target by predicting a gaze heatmap or gaze point. However, a notable drawback of this approach is its limited practical value in gaze applications, as mere localization may not fully capture our primary interest -- understanding the underlying semantics, such as the nature of the gaze target, rather than just its 2D pixel location. To address this gap, we extend the gaze following task, and introduce a novel architecture that simultaneously predicts the localization and semantic label of the gaze target. We devise a pseudo-annotation pipeline for the GazeFollow dataset, propose a new benchmark, develop an experimental protocol and design a suitable baseline for comparison. Our method sets a new state-of-the-art on the main GazeFollow benchmark for localization and achieves competitive results in the recognition task on both datasets compared to the baseline, with 40% fewer parameters.

Ukrainian drones struck multiple military airbases deep inside Russia on Sunday in a major operation a day before the neighbours held peace talks in Istanbul. The Russian Defence Ministry said Ukraine had launched drone strikes targeting Russian military airfields across five regions, causing several aircraft to catch fire. The attacks occurred in the Murmansk, Irkutsk, Ivanovo, Ryazan, and Amur regions. Air defences repelled the assaults in all but two regions – Murmansk and Irkutsk, the ministry said. "In the Murmansk and Irkutsk regions, the launch of FPV drones from an area in close proximity to airfields resulted in several aircraft catching fire," the Defence Ministry said.

We consider massive distributed datasets that consist of elements modeled as keyvalue pairs and the task of computing statistics or aggregates where the contribution of each key is weighted by a function of its frequency (sum of values of its elements). This fundamental problem has a wealth of applications in data analytics and machine learning, in particular, with concave sublinear functions of the frequencies that mitigate the disproportionate effect of keys with high frequency. The family of concave sublinear functions includes low frequency moments ( 1), capping, logarithms, and their compositions. A common approach is to sample keys, ideally, proportionally to their contributions and estimate statistics from the sample. A simple but costly way to do this is by aggregating the data to produce a table of keys and their frequencies, apply our function to the frequency values, and then apply a weighted sampling scheme. Our main contribution is the design of composable sampling sketches that can be tailored to any concave sublinear function of the frequencies. Our sketch structure size is very close to the desired sample size and our samples provide statistical guarantees on the estimation quality that are very close to that of an ideal sample of the same size computed over aggregated data. Finally, we demonstrate experimentally the simplicity and effectiveness of our methods.

Former U.S. ambassador to Ukraine John Herbst explains the impact of the drone strike on Russian air bases. Russian and Ukrainian delegations have begun talks in Istanbul, Turkey, on Monday, less than 24 hours after a massive Ukrainian drone attack struck Russian airfields. The two delegations entered Ciragan Palace in Istanbul alongside a group of senior Turkish officials. It is the second round of peace talks to take place in the three years since Russia invaded Ukraine. Images from the event show many of the Ukrainian delegation wearing military uniforms, while the Russian group exclusively wore suits.

There is a growing interest in societal concerns in machine learning systems, especially in fairness. Multicalibration gives a comprehensive methodology to address group fairness. In this work, we address the multicalibration error and decouple it from the prediction error. The importance of decoupling the fairness metric (multicalibration) and the accuracy (prediction error) is due to the inherent tradeoff between the two, and the societal decision regarding the "right tradeoff" (as imposed many times by regulators). Our work gives sample complexity bounds for uniform convergence guarantees of multicalibration error, which implies that regardless of the accuracy, we can guarantee that the empirical and (true) multicalibration errors are close. We emphasize that our results: (1) are more general than previous bounds, as they apply to both agnostic and realizable settings, and do not rely on a specific type of algorithm (such as differentially private), (2) improve over previous multicalibration sample complexity bounds and (3) implies uniform convergence guarantees for the classical calibration error.