Taming Plasma Chaos: AI-Powered Uncertainty Quantification
![Decomposition reveals distinct components-a background term [latex] \widetilde{\mathcal{M}} [/latex] and a perturbation term [latex] g [/latex]-each assessed against reference Maxwellian distributions characterized by varying anisotropy and isotropy, ultimately demonstrating how moment-matching techniques refine the approximation of complex systems as they evolve.](https://arxiv.org/html/2512.24205v1/x8.png)
A new framework leverages neural networks and advanced mathematical techniques to efficiently predict the behavior of complex plasmas under uncertainty.
Science
Beyond Words: Teaching Machines to Get the Joke
New research explores how to imbue AI agents with the ability to understand sarcasm, moving past simple keyword detection towards true contextual reasoning.
Mapping the Heart of Galaxies with AI
A new machine learning approach accurately reconstructs how light echoes within active galaxies, revealing crucial information about their central engines.
Synchronizing Swarms Despite Delays
A new control framework enables heterogeneous multi-agent systems to maintain coordinated behavior even with significant communication delays.
Keeping Network Traffic Classifiers Current: A Stability Benchmark
As network conditions evolve, maintaining accurate traffic classification requires continuous adaptation and a robust understanding of underlying data stability.
Seeing the Sun in Full Color: A New Approach to Image Compression
Researchers have developed a novel compression technique that preserves the critical spectral details in high-resolution solar imagery, enabling more efficient storage and analysis of this valuable data.
When Networks Unravel: Predicting the Spread of Decay
![A strategic network abandonment model demonstrates that increasing an outside option for agents initially causes exits only among those with the lowest payoff, but beyond a certain threshold-defined by the network’s payoff structure and an outside utility value of [latex]5.05[/latex] relative to minimal in-network utility of [latex]5.1[/latex]-can trigger a cascading abandonment where the departure of a few agents induces further, widespread departures, highlighting a critical transition in network stability governed by parameters [latex]\alpha = 1[/latex], [latex]\beta = 0.3[/latex], and [latex]\beta\rho(A) = 0.9[/latex].](https://arxiv.org/html/2512.24270v1/x1.png)
New research illuminates the factors that determine whether the abandonment of connections in a network will remain isolated or cascade into systemic failure.
Deeds on the Blockchain: Automating Real Estate Verification
A new system leverages document digitization and verifiable credentials to streamline property transactions and enhance trust in real estate records.
Echoes of Bias: How AI Safety Fades with Time and Language
New research reveals that the safety of large language models isn’t static, but diminishes as they encounter nuanced cultural contexts and older data.
Balancing Act: Steering Language Models Towards Safety and Helpfulness
New research introduces a method for optimizing language model behavior by carefully managing the inherent trade-offs between being both harmless and helpful.