Foreign key discovery and related schema-level prediction tasks are often modeled using graph neural networks (GNNs), implicitly assuming that relational inductive bias improves performance. However, it remains unclear when multi-hop structural reasoning is actually necessary. In this work, we introduce locality radius, a formal measure of the minimum structural neighborhood required to determine a prediction in relational schemas. We hypothesize that model performance depends critically on alignment between task locality radius and architectural aggregation depth. We conduct a […]

Ver mais

In Teilhard’s vision, creation moves from fragmented multiplicity toward unity through Christ’s love. All suffering springs from our scattered state; all joy from convergence. The world’s destiny is neither dissolution nor isolation, but the gathering of countless souls into one radiant, cosmic consciousness—spirit triumphant over dust.

Ver mais

arXiv:2602.21031v1 Announce Type: cross Abstract: We study the use of exchangeable multi-task Gaussian processes (GPs) for causal inference in panel data, applying the framework to two settings: one with a single treated unit subject to a once-and-for-all treatment and another with multiple treated units and staggered treatment adoption. Our approach models the joint evolution of outcomes for treated and control units through a GP prior that ensures exchangeability across units while allowing for flexible nonlinear trends over time. […]

Ver mais

How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## How to Break Your PostgreSQL IIoT Database and Learn Something in the Process By @tigerdata [ 6 Min read ] Learn how to stress test your IIoT database using PostgreSQL. Identify bottlenecks, simulate scale, and design systems that won’t fail in production. Read More. […]

Ver mais

:::info Astounding Stories of Super-Science October 2022, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. THE MURDER OF ROGER ACKROYD – DR. SHEPPARD AT THE BREAKFAST TABLE Astounding Stories of Super-Science October 2022: THE MURDER OF ROGER ACKROYD – DR. SHEPPARD AT THE BREAKFAST TABLE By Agatha Christie ::: Mrs. Ferrars died on the night of the 16th–17th September—a Thursday. I was sent for at eight o’clock […]

Ver mais

arXiv:2604.06203v1 Announce Type: new Abstract: The integration of continuous data from built-in sensors and Large Language Models (LLMs) has fueled a surge of “Sensor-Fused LLM agents” for personal health and well-being support. While recent breakthroughs have demonstrated the technical feasibility of this fusion (e.g., Time-LLM, SensorLLM), research primarily focuses on “Ethical Back-End Design for Generative AI”, concerns such as sensing accuracy, bias mitigation in training data, and multimodal fusion. This leaves a critical gap at the front end, […]

Ver mais

arXiv:2602.01017v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning generated by large language models (LLMs) is often unfaithful: intermediate steps can be logically inconsistent or fail to reflect the causal relationship leading to the final answer. Despite extensive empirical observations, a fundamental understanding of CoT is lacking–what constitutes faithful CoT reasoning, and how unfaithfulness emerges from autoregressive training. We study these questions using well-controlled synthetic experiments, training small transformers on noisy data to solve modular arithmetic expressions step by […]

Ver mais

The surge in reinforcement learning (RL) applications gave rise to diverse supporting technology, such as RL frameworks. However, the architectural patterns of these frameworks are inconsistent across implementations and there exists no reference architecture (RA) to form a common basis of comparison, evaluation, and integration. To address this gap, we propose an RA of RL frameworks. Through a grounded theory approach, we analyze 18 state-of-the-practice RL frameworks and, by that, we identify recurring architectural components and their relationships, […]

Ver mais

arXiv:2603.23205v1 Announce Type: new Abstract: Standard conformal anomaly detection provides marginal finite-sample guarantees under the assumption of exchangeability . However, real-world data often exhibit distribution shifts, necessitating a weighted conformal approach to adapt to local non-stationarity. We show that this adaptation induces a critical trade-off between the minimum attainable p-value and its stability. As importance weights localize to relevant calibration instances, the effective sample size decreases. This can render standard conformal p-values overly conservative for effective error control, […]

Ver mais