The most popular blogs of Hacker News in 2025 Michael Lynch maintains HN Popularity Contest, a site that tracks personal blogs on Hacker News and scores them based on how well they perform on that platform. The engine behind the project is the domain-meta.csv CSV on GiHub, a hand-curated list of known personal blogs with author and bio and tag metadata, which Michael uses to separate out personal blog posts from other types of content. I came top […]

Ver mais

arXiv:2601.18827v1 Announce Type: new Abstract: LLM-based agents are rapidly being adopted across diverse domains. Since they interact with users without supervision, they must be tested extensively. Current testing approaches focus on acceptance-level evaluation from the user’s perspective. While intuitive, these tests require manual evaluation, are difficult to automate, do not facilitate root cause analysis, and incur expensive test environments. In this paper, we present methods to enable structural testing of LLM-based agents. Our approach utilizes traces (based on […]

Ver mais

Networks of modern industrial systems are increasingly monitored by distributed sensors, where each system comprises multiple subsystems generating high dimensional time series data. These subsystems are often interdependent, making it important to understand how temporal patterns at one subsystem relate to others. This is challenging in decentralized settings where raw measurements cannot be shared and client observations are heterogeneous. In practical deployments each subsystem (client) operates a fixed proprietary model that cannot be modified or retrained, limiting existing […]

Ver mais

In the U.S. digital economy, small and medium-sized businesses (SMBs) and creators in remote regions face structural disadvantages in access to integrated advertising and incentive platforms, largely due to accelerating privacy regulations and the fragmentation of cross channel datasets. This paper proposes a unified federated and differentially private measurement framework that integrates Topics/Protected Audience, Attribution Reporting, and SKAdNetwork, aiming to achieve privacy-preserving incentive optimization and cross-channel effectiveness measurement for web and mobile environments. The framework prioritizes compliant data […]

Ver mais

Towards understanding the statistical complexity of learning from heterogeneous sources, we study the problem of multi-distribution learning. Given $k$ data sources, the goal is to output a classifier for each source by exploiting shared structure to reduce sample complexity. We focus on the bounded label noise setting to determine whether the fast $1/ε$ rates achievable in single-task learning extend to this regime with minimal dependence on $k$. Surprisingly, we show that this is not the case. We demonstrate […]

Ver mais

California’s recent wildfires were fought by over 780 inmate firefighters who received rigorous taxpayer-funded training. But until recent reforms, many of these trained firefighters faced insurmountable barriers to employment after release—state licensing laws blocked them from the very careers they’d prepared for. lead , Cato scholar Stephen Slivinski examines this issue in a new Washington Post opinion piece, revealing how occupational licensing laws create employment barriers for 70 million Americans with criminal records. The evidence is clear: employment is […]

Ver mais

We present a new self-supervised machine learning approach for symbolic simplification of complex mathematical expressions. Training data is generated by scrambling simple expressions and recording the inverse operations, creating oracle trajectories that provide both goal states and explicit paths to reach them. A permutation-equivariant, transformer-based policy network is then trained on this data step-wise to predict the oracle action given the input expression. We demonstrate this approach on two problems in high-energy physics: dilogarithm reduction and spinor-helicity scattering […]

Ver mais

arXiv:2602.10266v1 Announce Type: new Abstract: This chapter explores neural networks, topological data analysis, and topological deep learning techniques, alongside statistical Bayesian methods, for processing images, time series, and graphs to maximize the potential of artificial intelligence in the military domain. Throughout the chapter, we highlight practical applications spanning image, video, audio, and time-series recognition, fraud detection, and link prediction for graphical data, illustrating how topology-aware and uncertainty-aware models can enhance robustness, interpretability, and generalization.

Ver mais

Conversational chat bots such as ChatGPT probably will not be able replace traditional search engines and expert knowledge anytime soon. With the vast amount of misinformation available on the internet, the ability to distinguish between credible and unreliable sources remains challenging and crucial.

Ver mais

arXiv:2601.19945v1 Announce Type: new Abstract: Automatic Speech Recognition (ASR) offers significant potential to reduce the workload of medical personnel, for example, through the automation of documentation tasks. While numerous benchmarks exist for the English language, specific evaluations for the German-speaking medical context are still lacking, particularly regarding the inclusion of dialects. In this article, we present a curated dataset of simulated doctor-patient conversations and evaluate a total of 29 different ASR models. The test field encompasses both open-weights […]

Ver mais