arXiv:2602.09185v1 Announce Type: new Abstract: AI coding agents are rapidly transforming software engineering by performing tasks such as feature development, debugging, and testing. Despite their growing impact, the research community lacks a comprehensive dataset capturing how these agents are used in real-world projects. To address this gap, we introduce AIDev, a large-scale dataset focused on agent-authored pull requests (Agentic-PRs) in real-world GitHub repositories. AIDev aggregates 932,791 Agentic-PRs produced by five agents: OpenAI Codex, Devin, GitHub Copilot, Cursor, and […]

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Introduction This article will discuss compression in the Big Data context, covering the types and methods of compression. I will also highlight why and when each type and method should be used. Diving in What is compression? According to the general English definition of compression, it refers to reducing something to occupy a smaller space. In Computer Science, compression is the process of reducing data to a smaller size. Data, in this case, could be represented in text, […]

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Introduction Disclaimer: This article is a cross post from Pytorch Medium Blog Post. One of the most exciting advancements, that has pushed the frontier of the Artificial Intelligence (AI) in recent years, is Deep Reinforcement Learning (DRL). DRL belongs to the family of machine learning algorithms. It assumes that intelligent machines can learn from their actions similar to the way humans learn from experience. Over the recent years we could witness some impressive real-world applications of DRL. The […]

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arXiv:2604.20516v1 Announce Type: new Abstract: Determining identifiability of causal effects from observational data under latent confounding is a central challenge in causal inference. For linear structural causal models, identifiability of causal effects is decidable through symbolic computation. However, standard approaches based on Gr”obner bases become computationally infeasible beyond small settings due to their doubly exponential complexity. In this work, we study how to practically use symbolic computation for deciding rational identifiability. In particular, we present an efficient algorithm […]

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We live in a world obsessed with speed. Faster apps, quicker deliveries, instant updates, and endless streams of information define our daily lives. In this race to move faster, topics that require patience and long-term thinking—like the environment—often feel slow, inconvenient, or easy to ignore. Yet, the environmental challenges we face today are deeply connected to the technology-driven world we are building. Writing about the environment in such a fast-paced, tech-addicted era feels both challenging and necessary. The […]

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arXiv:2602.09076v1 Announce Type: new Abstract: Predicting human trajectory is crucial for social robot navigation in crowded environments. While most existing approaches treat human as point mass, we present a study on multi-agent trajectory prediction that leverages different human skeletal features for improved forecast accuracy. In particular, we systematically evaluate the predictive utility of 2D and 3D skeletal keypoints and derived biomechanical cues as additional inputs. Through a comprehensive study on the JRDB dataset and another new dataset for […]

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arXiv:2602.22243v1 Announce Type: new Abstract: The online fusion and tracking of static objects from heterogeneous sensor detections is a fundamental problem in robotics, autonomous systems, and environmental mapping. Although classical data association approaches such as JPDA are well suited for dynamic targets, they are less effective for static objects observed intermittently and with heterogeneous uncertainties, where motion models provide minimal discriminative with respect to clutter. In this paper, we propose a novel method for static object data association […]

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Integrating human expertise into machine learning systems often reduces the role of experts to labeling oracles, a paradigm that limits the amount of information exchanged and fails to capture the nuances of human judgment. We address this challenge by developing a human-in-the-loop framework to learn binary classifiers with rich query types, consisting of item ranking and exemplar selection. We first introduce probabilistic human response models for these rich queries motivated by the relationship experimentally observed between the perceived […]

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arXiv:2603.03415v1 Announce Type: new Abstract: In this work, we investigate how Large Language Models (LLMs) adapt their internal representations when encountering inputs of increasing difficulty, quantified as the degree of out-of-distribution (OOD) shift. We reveal a consistent and quantifiable phenomenon: as task difficulty increases, whether through harder reasoning questions, longer contexts, or adding answer choices, the last hidden states of LLMs become substantially sparser. In short, textbf{textit{the farther the shift, the sparser the representations}}. This sparsity–difficulty relation is […]

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