Flow Matching (FM) (also referred to as stochastic interpolants or rectified flows) stands out as a class of generative models that aims to bridge in finite time the target distribution $\nu^\star$ with an auxiliary distribution $\mu$ leveraging a fixed coupling $\pi$ and a bridge which can either be deterministic or stochastic. These two ingredients define a path measure which can then be approximated by learning the drift of its Markovian projection. The main contribution of this paper is to provide relatively mild assumption on $\nu^\star$, $\mu$ and $\pi$ to obtain non-asymptotics guarantees for Diffusion Flow Matching (DFM) models using as bridge the conditional distribution associated with the Brownian motion. More precisely, it establishes bounds on the Kullback-Leibler divergence between the target distribution and the one generated by such DFM models under moment conditions on the score of $\nu^\star$, $\mu$ and $\pi$, and a standard $\mathrm{L}^2$-drift-approximation error assumption.

A fundamental procedure in the analysis of massive datasets is the construction of similarity graphs. Such graphs play a key role for many downstream tasks, including clustering, classification, graph learning, and nearest neighbor search. For these tasks, it is critical to build graphs which are sparse yet still representative of the underlying data. The benefits of sparsity are twofold: firstly, constructing dense graphs is infeasible in practice for large datasets, and secondly, the runtime of downstream tasks is directly influenced by the sparsity of the similarity graph. In this work, we present Stars: a highly scalable method for building extremely sparse graphs via two-hop spanners, which are graphs where similar points are connected by a path of length at most two. Stars can construct two-hop spanners with significantly fewer similarity comparisons, which are a major bottleneck for learning based models where comparisons are expensive to evaluate. Theoretically, we demonstrate that Stars builds a graph in nearly-linear time, where approximate nearest neighbors are contained within two-hop neighborhoods. In practice, we have deployed Stars for multiple data sets allowing for graph building at the Tera-Scale, i.e., for graphs with hundreds of billions of nodes and tens of trillions of edges. We evaluate the performance of Stars for clustering and graph learning, and demonstrate 10~1000-fold improvements in pairwise similarity comparisons and significant running time speedups with negligible quality loss.

Generating step-by-step chain-of-thought rationales improves language model performance on complex reasoning tasks like mathematics or commonsense question-answering. However, inducing language model rationale generation currently requires either constructing massive rationale datasets or sacrificing accuracy by using only few-shot inference. We propose a technique to iteratively leverage a small number of rationale examples and a large dataset without rationales, to bootstrap the ability to perform successively more complex reasoning. This technique, the Self-Taught Reasoner (STaR), relies on a simple loop: generate rationales to answer many questions, prompted with a few rationale examples; if the generated answers are wrong, try again to generate a rationale given the correct answer; fine-tune on all the rationales that ultimately yielded correct answers; repeat. We show that STaR significantly improves performance on multiple datasets compared to a model fine-tuned to directly predict final answers, and performs comparably to fine-tuning a 30$\times$ larger state-of-the-art language model on CommensenseQA. Thus, STaR lets a model improve itself by learning from its own generated reasoning.

Modern robotic systems, deployed across domains from industrial automation to domestic assistance, face a critical challenge: executing tasks with precision and adaptability in dynamic, unpredictable environments. To address this, we propose STAR (Smart Task Adaptation and Recovery), a novel framework that synergizes Foundation Models (FMs) with dynamically expanding Knowledge Graphs (KGs) to enable resilient task planning and autonomous failure recovery. While FMs offer remarkable generalization and contextual reasoning, their limitations, including computational inefficiency, hallucinations, and output inconsistencies hinder reliable deployment. STAR mitigates these issues by embedding learned knowledge into structured, reusable KGs, which streamline information retrieval, reduce redundant FM computations, and provide precise, scenario-specific insights. The framework leverages FM-driven reasoning to diagnose failures, generate context-aware recovery strategies, and execute corrective actions without human intervention or system restarts. Unlike conventional approaches that rely on rigid protocols, STAR dynamically expands its KG with experiential knowledge, ensuring continuous adaptation to novel scenarios. To evaluate the effectiveness of this approach, we developed a comprehensive dataset that includes various robotic tasks and failure scenarios. Through extensive experimentation, STAR demonstrated an 86% task planning accuracy and 78% recovery success rate, showing significant improvements over baseline methods. The framework's ability to continuously learn from experience while maintaining structured knowledge representation makes it particularly suitable for long-term deployment in real-world applications.

In causal inference, many estimands of interest can be expressed as a linear functional of the outcome regression function; this includes, for example, average causal effects of static, dynamic and stochastic interventions. For learning such estimands, in this work, we propose novel debiased machine learning estimators that are doubly robust asymptotically linear, thus providing not only doubly robust consistency but also facilitating doubly robust inference (e.g., confidence intervals and hypothesis tests). To do so, we first establish a key link between calibration, a machine learning technique typically used in prediction and classification tasks, and the conditions needed to achieve doubly robust asymptotic linearity. We then introduce calibrated debiased machine learning (C-DML), a unified framework for doubly robust inference, and propose a specific C-DML estimator that integrates cross-fitting, isotonic calibration, and debiased machine learning estimation. A C-DML estimator maintains asymptotic linearity when either the outcome regression or the Riesz representer of the linear functional is estimated sufficiently well, allowing the other to be estimated at arbitrarily slow rates or even inconsistently. We propose a simple bootstrap-assisted approach for constructing doubly robust confidence intervals. Our theoretical and empirical results support the use of C-DML to mitigate bias arising from the inconsistent or slow estimation of nuisance functions.

Created in 2013, the Stars of Europe awards recognize the coordinators of European collaborative research projects. On December 6, Sylvie Retailleau, Minister of Higher Education and Research, presented trophies to twelve winners at a ceremony at the Quai Branly Museum. Among them, Nicolas Mansard, CNRS researcher in robotics at LAAS-CNRS, holder of the ANITI chair " Artificial and natural movement", rewarded for the coordination of the MEMMO (memory of motion) project. Funded by the Horizon 2020 program over a four-year period, MEMMO (Memory of Motion) is a collaborative project initiated in 2018 that brought together a consortium of 10 European partners for a budget of €4 million: LAAS-CNRS (France), IDIAP (Switzerland), University of Edinburgh (UK), Max Planck Institute (Germany), Oxford University (UK), Trento University (IT), PAL-Robotics (Spain), Wandercraft (France), Airbus (France), Costain (UK) and APAJH (France). "I would like to thank the people who helped me coordinate this project. It is a project put together by a consortium of young researchers. It was a great pride for me to be chosen to coordinate this project. "We wanted to prove that it was possible to generate complex motions for arbitrary robots with arms and legs interacting in a dynamic environment in real time.

Water glimmers in the pools below, the light of dawn splashes the sky with varying hues of surreal Fantasy, and lengthy tentacles trail behind the Octopus that defy gravity in the foreground.

Monthly ranking of repos in this collection by stars, pull requests, issues. Table chart describes the Month-on-Month ranking of repos with three metrics（Star, Pull Request, Issue）. A pipeline chart displays annual ranking changes in four metrics（Star, Pull Request, Pull Request Creators, Issue） for each repository since 2011.

A new method based on artificial intelligence and text analysis could help diagnose the condition more efficiently.

In a high-tech lab on Johns Hopkins University's Homewood campus in Baltimore, engineers have been building a robot that may be able to stitch back together the broken vessels in your belly and at some point maybe your brain, no doctor needed. The robot has a high-tech camera on one arm and a high-tech sewing machine on a second arm. "It's like park assist in a car," said Axel Krieger, an assistant professor of mechanical engineering in Hopkins' Whiting School of Engineering. This kind of suturing is performed more than a million times a year in surgeries around the country, said Krieger, part of a team developing the robot and senior author on a recent paper describing the technology in science robotics. The goal is to develop in the next several years a robot that makes the intricate and delicate work of suturing more consistent.