Mapping Slope Failure: A New Physics-Informed Approach
![The framework employs a differentiable graph neural network simulator to solve inverse problems in granular flows, iteratively refining parameters [latex]\boldsymbol{\theta}[/latex] through gradient-based optimization-starting from an initial state [latex]\hat{\mathbf{X}}\_{0}(\boldsymbol{\theta})[/latex]-until the simulated granular flow state at time [latex]t[/latex], [latex]\hat{\mathbf{X}}\_{t}(\boldsymbol{\theta})[/latex], converges with observed states [latex]X\_{t}^{\text{obs}}[/latex].](https://arxiv.org/html/2602.11621v1/figs/diff_gns.png)
Researchers have developed a computationally efficient framework using graph neural networks to predict post-liquefaction strength and simulate landslide runout.
Science
Building a Smarter Cybersecurity Defense with Knowledge Graphs
A new framework automatically constructs and expands a comprehensive cybersecurity knowledge graph by intelligently integrating diverse threat intelligence sources.
Mapping the Invisible: AI Learns to Read Angiograms with Anatomical Precision
![VasoMIM extracts vascular anatomy from X-ray angiograms using a Frangi filter, then employs a patch-wise anatomical distribution to prioritize vessel-relevant regions during masking, ultimately optimizing model performance by minimizing a combined loss function [latex]\mathcal{L}_{MIM}[/latex] comprising standard pixel-level reconstruction [latex]\mathcal{L}_{rec}[/latex] and a novel anatomical consistency loss [latex]\mathcal{L}_{cons}[/latex], thereby learning discriminative vascular representations.](https://arxiv.org/html/2602.11536v1/x3.png)
A new self-supervised learning approach is dramatically improving the analysis of X-ray angiograms, enabling more accurate vascular segmentation and detection.
Beyond Confidence Scores: Disentangling What AI Really Doesn’t Know
![Standard decomposition of uncertainty falters because methods attempting to derive both aleatoric-reflecting ambiguity in the true probability [latex]p^<i> [/latex]-and epistemic uncertainty-measuring deviation from [latex]p^</i> [/latex]-produce correlated estimates trapped along a diagonal, a limitation which a structurally separated Credal CBM approach successfully circumvents by recovering the inherent geometric independence of these properties.](https://arxiv.org/html/2602.11219v1/x1.png)
A new framework offers a robust method for separating genuine knowledge gaps from inherent data noise in deep learning models.
Beyond Testing: Rigorously Verifying AI Spacecraft Control
New research details a method for mathematically guaranteeing the safety of neural network controllers used in spacecraft guidance, moving beyond traditional simulation-based verification.
The Echo Chamber Effect: How Online Hype Outpaces Fact-Checking in AI Discussions
![In Reddit discussions concerning agentic AI, heightened platform visibility-specifically, engagement scores exceeding a threshold [latex]Q_{0.75}[/latex]-correlates with a delayed search for corroborating evidence, suggesting that initial credibility is often established through visibility itself, allowing nascent narratives to solidify before factual substantiation emerges, a process particularly observable in threads where verification cues are absent within a defined observation window and are thus treated as right-censored data.](https://arxiv.org/html/2602.11412v1/mechanism_sketch.png)
New research reveals that public conversations surrounding advanced artificial intelligence often prioritize engagement over rigorous verification, potentially leading to the premature acceptance of claims.
Beyond Accuracy: A New Benchmark for Forecasting the Future
![The evolution of time series forecasting benchmarks reveals a field increasingly reliant on repurposed datasets-indicated by superscripted markers-and characterized by key transitions highlighted along a timeline that now prominently features the Time Series Transformer [latex]TST[/latex], despite a historical pattern of frameworks inevitably accruing technical debt as production use cases challenge theoretical elegance.](https://arxiv.org/html/2602.12147v1/figure/timeline.png)
A new research effort introduces TIME, a comprehensive benchmark designed to rigorously evaluate the next generation of time series forecasting models.
Unmasking Bad Actors in Federated Learning
A new technique identifies and isolates malicious participants in collaborative machine learning, even when they work together.
Decoding Cyber Threats: Can AI Make Security Reports Easier to Understand?
![The GemmaAgent architecture facilitates complex reasoning through the synergistic integration of a large language model (LLM) and specialized tools, enabling it to iteratively refine plans and execute actions based on observed states and [latex] \mathbb{R} [/latex]-valued rewards, ultimately achieving robust task completion.](https://arxiv.org/html/2602.11982v1/x1.png)
Researchers are exploring how artificial intelligence can automatically simplify complex cybersecurity vulnerability descriptions, improving accessibility for a wider audience.
Beyond Outliers: Fast, Reliable Bayesian Inference with Simulations
A new method, NSM-Bayes, dramatically improves the speed and robustness of Bayesian inference by leveraging neural networks and simulation.