arXiv:2602.13301v1 Announce Type: new Abstract: Recent advances towards End-to-End Autonomous Driving (E2E-AD) have been often devoted on integrating modular designs into a unified framework for joint optimization e.g. UniAD, which follow a sequential paradigm (i.e., perception-prediction-planning) based on separable Transformer decoders and rely on dense BEV features to encode scene representations. However, such manual ordering design can inevitably cause information loss and cumulative errors, lacking flexible and diverse relation modeling among different modules and sensors. Meanwhile, insufficient training […]

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arXiv:2603.25894v1 Announce Type: new Abstract: This paper presents a data-driven framework for modeling plastic deformation in crystalline metals through acoustic emission (AE) analysis. Building on experimental data from compressive loading of nickel micropillars, the study introduces a wavelet-based method using Morlet transforms to detect AE events across distinct frequency bands, enabling identification of both large and previously overlooked small-scale events. The detected events are validated against stress-drop dynamics, demonstrating strong physical consistency and revealing a relationship between AE […]

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Growth hacks work, until they don’t. Most startups begin with quick wins: viral reels, aggressive ads, referral incentives, clever copy. For a while, momentum builds. Metrics go up. Dashboards look healthy. It feels like progress. Then something changes. Your customer acquisition costs start rising. Engagement flattens. What once felt like growth starts to feel like a constant fight. At this point, you usually assume the answer is another tactic. A new channel. A new funnel. A new trick. […]

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arXiv:2602.23373v1 Announce Type: new Abstract: Adverse media screening is a critical component of anti-money laundering (AML) and know-your-customer (KYC) compliance processes in financial institutions. Traditional approaches rely on keyword-based searches that generate high false-positive rates or require extensive manual review. We present an agentic system that leverages Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to automate adverse media screening. Our system implements a multi-step approach where an LLM agent searches the web, retrieves and processes relevant documents, […]

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:::info Authors: Brendan Nyhan Jaime Settle Emily Thorson Magdalena Wojcieszak Pablo Barberá Annie Y. Chen Hunt Allcott Taylor Brown Adriana Crespo-Tenorio Drew Dimmery Deen Freelon Matthew Gentzkow Sandra González-Bailón Andrew M. Guess Edward Kennedy Young Mie Kim David Lazer Neil Malhotra Devra Moehler Jennifer Pan Daniel Robert Thomas Rebekah Tromble Carlos Velasco Rivera Arjun Wilkins Beixian Xiong Chad Kiewiet de Jonge Annie Franco Winter Mason Natalie Jomini Stroud Joshua A. Tucker ::: Abstract Many critics raise concerns about […]

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Predicting atomic-scale crack propagation in aluminum nitride (AlN) is critical for semiconductor reliability but remains prohibitively expensive via molecular dynamics (MD). We develop a diffusion-based generative machine learning model to predict atomic-scale crack propagation in AlN, a critical semiconductor material, by conditioning solely on initial microstructure embeddings. Trained on MD simulations of single-crack systems, the model achieves a significant speedup while accurately forecasting dynamic fracture processes, including stress-driven crack initiation, crack branching, and atomic-scale bridging ligaments. Crucially, it […]

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Recent large language models have shifted SVG generation from differentiable rendering optimization to autoregressive program synthesis. However, existing approaches still rely on generic byte-level tokenization inherited from natural language processing, which poorly reflects the geometric structure of vector graphics. Numerical coordinates are fragmented into discrete symbols, destroying spatial relationships and introducing severe token redundancy, often leading to coordinate hallucination and inefficient long-sequence generation. To address these challenges, we propose HiVG, a hierarchical SVG tokenization framework tailored for autoregressive […]

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I am a huge fan of gistpreview.github.io, the site by Leon Huang that lets you append ?GIST_id to see a browser-rendered version of an HTML page that you have saved to a Gist. The last commit was ten years ago and I needed a couple of small changes so I’ve forked it and deployed an updated version at gisthost.github.io. Some background on gistpreview The genius thing about gistpreview.github.io is that it’s a core piece of GitHub infrastructure, hosted […]

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The medical industry is finally stopping the charade of treating software as a tool and starting to treat AI agents as colleagues. We have moved past the era of passive Large Language Models (LLMs) that act like fancy encyclopedias. The current landscape is defined by agentic AI, digital entities that do not just suggest; they execute. If 2024 was about the “ambient scribe” that sat in the corner recording conversations, 2026 is about the “hireable agent” that navigates […]

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Federated learning in vehicular edge networks faces major challenges in efficient resource allocation, largely due to high vehicle mobility and the presence of imperfect channel state information. Many existing methods oversimplify these realities, often assuming fixed communication rounds or ideal channel conditions, which limits their effectiveness in real-world scenarios. To address this, we propose variable rate vehicular federated learning (VR-VFL), a novel federated learning method designed specifically for vehicular networks under imperfect channel state information. VR-VFL combines dynamic […]

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