arXiv:2603.00623v1 Announce Type: new Abstract: Agentic systems augment large language models with external tools and iterative decision making, enabling complex tasks such as deep research, function calling, and coding. However, their long and intricate execution traces make failure diagnosis and root cause analysis extremely challenging. Manual inspection does not scale, while directly applying LLMs to raw traces is hindered by input length limits and unreliable reasoning. Focusing solely on final task outcomes further discards critical behavioral information required […]

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arXiv:2602.15155v1 Announce Type: new Abstract: Implicit Neural Representations (INRs) have emerged as promising surrogates for large 3D scientific simulations due to their ability to continuously model spatial and conditional fields, yet they face a critical fidelity-speed dilemma: deep MLPs suffer from high inference cost, while efficient embedding-based models lack sufficient expressiveness. To resolve this, we propose the Decoupled Representation Refinement (DRR) architectural paradigm. DRR leverages a deep refiner network, alongside non-parametric transformations, in a one-time offline process to […]

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arXiv:2603.26690v1 Announce Type: new Abstract: Embodied intelligence fundamentally requires a capability to determine where to act in 3D space. We formalize this requirement as embodied localization — the problem of predicting executable 3D points conditioned on visual observations and language instructions. We instantiate embodied localization with two complementary target types: touchable points, surface-grounded 3D points enabling direct physical interaction, and air points, free-space 3D points specifying placement and navigation goals, directional constraints, or geometric relations. Embodied localization is […]

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Human interventions are a common source of data in autonomous systems during testing. These interventions provide an important signal about where the current policy needs improvement, but are often noisy and incomplete. We define Robust Intervention Learning (RIL) as the problem of learning from intervention data while remaining robust to the quality and informativeness of the intervention signal. In the best case, interventions are precise and avoiding them is sufficient to solve the task, but in many realistic […]

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arXiv:2603.16873v1 Announce Type: new Abstract: Recent advances in AI enable the automatic generation of visualizations directly from textual prompts using agentic workflows. However, visualizations produced via one-shot generative methods often suffer from insufficient quality, typically requiring a human in the loop to refine the outputs. Human evaluation, though effective, is costly and impractical at scale. To alleviate this problem, we propose an automated metric that evaluates visualization quality without relying on extensive human-labeled datasets. Instead, our approach uses […]

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Anthropic has been trending all over the news lately following its conflict with the Department of Defense (DoD). After negotiation talks broke down with the Pentagon, the Trump administration labeled it a supply-chain risk in the U.S. However, Anthropic is reportedly getting backing from employees at OpenAI and Google Deepmind. Since, impact of powerful AI system has become a hot topic in the tech industry these days, Anthropic has announced an initiative that focuses on studying the same. […]

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Welcome to Meet the Contest Winner—a HackerNoon interview series celebrating the writers behind our most memorable contest entries. Let’s get started! Tell us who you are and why you decided to participate in the HackerNoon Writing Contest in the first place. Hi, I’m Sibasis Padhi, an expert in Agentic AI microservices and cloud performance optimization for financial systems. I joined the HackerNoon writing contest as a personal experiment, not just to publish, but to share real infrastructure-level lessons […]

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:::info Authors: Robert Avram Jeffrey E. Olgin Zeeshan Ahmed Louis Verreault-Julien Alvin Wan Joshua Barrios Sean Abreau Derek Wan Joseph E. Gonzalez Jean-Claude Tardif Derek Y. So Krishan Soni Geoffrey H. Tison ::: Abstract Coronary angiography is the primary procedure for diagnosis and management decisions in coronary artery disease (CAD), but ad-hoc visual assessment of angiograms has high variability. Here we report a fully automated approach to interpret angiographic coronary artery stenosis from standard coronary angiograms. Using 13,843 […]

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Federated graph learning (FGL) enables collaborative training of graph neural networks (GNNs) across decentralized subgraphs without exposing raw data. While existing FGL methods often achieve high overall accuracy, we show that this average performance can conceal severe degradation on disadvantaged node groups. From a fairness perspective, these disparities arise systematically from three coupled sources: label skew toward majority patterns, topology confounding in message propagation, and aggregation dilution of updates from hard clients. To address this, we propose textbf{BoostFGL}, […]

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