Fortifying Future Networks: A Resilience Blueprint
![Resilience against attack vectors isn’t a static property, but emerges from a dynamic interplay between vulnerabilities, network state [latex]X_{t}[/latex], and three classes of defense-proactive hardening, responsive adaptation, and retrospective learning from performance outcomes [latex]Y_{t}[/latex]-that continuously refine the system’s ability to withstand compromise.](https://arxiv.org/html/2512.22721v1/Figures/resilienceMechanism.png)
This review explores the evolving threat landscape and emerging strategies for building cyber-resilient next-generation networks.
Science
When Federated Learning Falls Apart: Why Data Splits Break AI Models
New research reveals that performance drops in decentralized learning aren’t just random, but a result of internal network structures disintegrating under non-ideal data conditions.
Beyond Relief: AI for Fairer Flood Recovery
A new framework uses artificial intelligence to address bias in post-disaster aid distribution, ensuring more equitable support for vulnerable communities.
Securing the Software Pipeline with Intelligent Agents
A new framework leverages artificial intelligence to proactively defend software supply chains against evolving vulnerabilities.
Powering Through: Building Resilient Communities in the Face of Disruption
This review examines how to strengthen power systems and the communities they serve against increasingly frequent and severe disruptions.
Beyond the Hype: Real-World AI Security Concerns Emerge
A new analysis of developer forums reveals a disconnect between theoretical AI risks and the practical security challenges faced in building and deploying AI-powered projects.
Predicting Bitcoin’s Future: Global Money Supply as a Key Indicator
A new expert system leverages global liquidity data alongside advanced time-series forecasting to achieve more accurate long-term Bitcoin price predictions.
AI’s Growing Power: A New Framework for Responsible Development
![The ARC framework establishes a recursive approach to reasoning, wherein complex problems are decomposed into simpler subproblems until reaching a base case solvable with a defined operator [latex] \mathcal{R} [/latex], thus enabling compositional generalization and systematic program execution.](https://arxiv.org/html/2512.22211v1/arc_overview3.png)
As artificial intelligence systems gain increasing autonomy, a structured approach to managing their potential risks is becoming critical.
Beyond Defense: AI Agents and the Future of Cyber Resilience
As cyberattacks grow more sophisticated, a new security paradigm is emerging that leverages intelligent, autonomous AI agents to adapt and respond to threats in real-time.
Predicting CAT Bond Risk with Climate Data
A new study explores how incorporating climate variability indices into machine learning models can improve the pricing and risk assessment of catastrophe bonds.