arXiv:2603.02426v1 Announce Type: new Abstract: We study personalized multi-agent average reward TD learning, in which a collection of agents interacts with different environments and jointly learns their respective value functions. We focus on the setting where there exists a shared linear representation, and the agents’ optimal weights collectively lie in an unknown linear subspace. Inspired by the recent success of personalized federated learning (PFL), we study the convergence of cooperative single-timescale TD learning in which agents iteratively estimate […]

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Federated Learning (FL) enables a distributed client-server architecture where multiple clients collaboratively train a global Machine Learning (ML) model without sharing sensitive local data. However, FL often results in lower accuracy than traditional ML algorithms due to statistical heterogeneity across clients. Prior works attempt to address this by using model updates, such as loss and bias, from client models to select participants that can improve the global model’s accuracy. However, these updates neither accurately represent a client’s heterogeneity […]

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The NumPy library in Python, and many other systems, relied on the Mersenne Twister PRNG (pseudo-random number generator) for a long time. It was slow and did not mimic randomness well enough, failing some statistical tests. In addition, it could be cracked, raising security issues. It was replaced recently by PCG64 which addresses some of these issues. Nowadays, PCG64 is widely adopted. In the meanwhile, for over 2 decades, I worked on this topic. My efforts culminated when […]

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In this tutorial, we walk through MolmoAct step by step and build a practical understanding of how action-reasoning models can reason in space from visual observations. We set up the environment, load the model, prepare multi-view image inputs, and explore how MolmoAct produces depth-aware reasoning, visual traces, and actionable robot outputs from natural language instructions. As we move through the workflow, we run inference and also examine how the model parses actions, visualizes trajectories, and supports more advanced […]

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arXiv:2602.18537v1 Announce Type: new Abstract: Jupyter notebooks are widely used for machine learning (ML) development due to their support for interactive and iterative experimentation. However, ML notebooks are highly prone to bugs, with crashes being among the most disruptive. Despite their practical importance, systematic methods for crash detection and diagnosis in ML notebooks remain largely unexplored. We present CRANE-LLM, a novel approach that augments large language models (LLMs) with structured runtime information extracted from the notebook kernel state […]

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arXiv:2602.01018v1 Announce Type: new Abstract: Hierarchical Imitation Learning is a powerful paradigm for acquiring complex robot behaviors from demonstrations. A central challenge, however, lies in discovering reusable skills from long-horizon, multi-task offline data, especially when the data lacks explicit rewards or subtask annotations. In this work, we introduce LOKI, a three-stage end-to-end learning framework designed for offline skill discovery and hierarchical imitation. The framework commences with a two-stage, weakly supervised skill discovery process: Stage one performs coarse, task-aware […]

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arXiv:2601.05373v1 Announce Type: new Abstract: Early diagnosis of breast cancer is crucial for improving survival rates. Radiomics and deep learning (DL) have shown significant potential in assisting radiologists with early cancer detection. This paper aims to critically assess the performance of radiomics, DL, and ensemble techniques in detecting cancer from screening mammograms. Two independent datasets were used: the RSNA 2023 Breast Cancer Detection Challenge (11,913 patients) and a Mexican cohort from the TecSalud dataset (19,400 patients). The ConvNeXtV1-small […]

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arXiv:2601.12263v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are rapidly replacing unimodal encoders in modern retrieval and recommendation systems. While their capabilities are well-documented, their robustness against adversarial manipulation in competitive ranking scenarios remains largely unexplored. In this paper, we uncover a critical vulnerability in VLM-based product search: multimodal ranking attacks. We present Multimodal Generative Engine Optimization (MGEO), a novel adversarial framework that enables a malicious actor to unfairly promote a target product by jointly optimizing imperceptible image […]

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If you’ve ever built a data pipeline for analytics or business intelligence, you know the basics — ingest, transform, store. But regulated industries are a different game entirely. A missed record in a BI pipeline means a slightly off dashboard. A missed record in a compliance pipeline means a regulatory fine, a failed audit, or worse. Over the past couple of years, I’ve been designing and running ETL pipelines that process over 500,000 compliance data files every month […]

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arXiv:2604.18805v1 Announce Type: new Abstract: Large language model (LLM)-based systems are increasingly deployed to conduct scientific research autonomously, yet whether their reasoning adheres to the epistemic norms that make scientific inquiry self-correcting is poorly understood. Here, we evaluate LLM-based scientific agents across eight domains, spanning workflow execution to hypothesis-driven inquiry, through more than 25,000 agent runs and two complementary lenses: (i) a systematic performance analysis that decomposes the contributions of the base model and the agent scaffold, and […]

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