Filling the Gaps in Climate History with AI
![A probabilistic deep learning framework reconstructs Earth system dynamics by first establishing a generative climate prior through unsupervised pre-training on climate simulations and reanalysis data, then guiding spatiotemporally consistent field generation-initiated from noise and solved via reverse-time Stochastic Differential Equations-with simultaneous gradients enforcing fidelity to sparse station data [latex]\mathcal{G}\_{obs}[/latex] and temporal continuity between overlapping time windows [latex]\mathbf{w}\_{i},\mathbf{w}\_{i+1}[/latex], ultimately yielding both high-fidelity reconstructions optimized for fine-scale structures and large-ensemble realizations quantifying uncertainty.](https://arxiv.org/html/2602.16515v1/x23.png)
A new generative AI framework leverages diffusion models to create more accurate and reliable reconstructions of past global climate conditions.
Science
Cooperation Emerges: Decentralized AI for Smarter Energy Markets
New research demonstrates how independent AI agents can coordinate effectively in local energy grids, achieving performance comparable to centralized control systems.
Learning to See Better: Self-Supervised Features Boost Object Detection
A new approach to feature extraction leverages self-supervised learning to significantly improve object detection performance, even when labeled data is scarce.
Seeing Clearly Through the Noise: A Robustness Check for Object Detection
New research establishes a standardized benchmark for evaluating how well object detection systems hold up against adversarial attacks, revealing critical insights into effective defense strategies.
Beyond Detection: Bridging the Gap Between Dark Pattern Tools and Legal Reality
New research reveals a disconnect between the development of automated tools to identify deceptive online designs and the practical requirements of regulatory enforcement.
Beyond Recall: Building AI with true Experience
A new approach to long-term memory is emerging in artificial intelligence, prioritizing the storage of raw data and on-demand analysis for more adaptable and insightful agents.
Tracking Financial Crime with AI: A Global Intelligence Network
A new approach leverages artificial intelligence and distributed learning to identify and mitigate cross-border insider threats in government financial systems.
Boosting Bioplastics with Nature’s Building Blocks
![The study demonstrates how [latex]\pi_{n}^{\<i>}, R(\bm{a}^{\</i>}\_{n},\pi\_{n}^{\<i>}), w, (a^{\</i>}\_{n}(k))\_{0\leq k\leq n}[/latex] evolves across varying values of <i>n</i>, revealing the inherent dynamics of systems as they progress-a process not defined by time, but by the unfolding of internal states and the consequential adjustments within the system itself.](https://arxiv.org/html/2602.15246v1/Figures/PIC.png)
New research explores how reinforcing bioplastics with cellulose nanocrystals can unlock improved performance and pave the way for truly sustainable materials.
The Honesty Game: Unmasking Deception in AI Training
New research explores how reinforcement learning can be used to build more truthful AI systems, and the surprising ways those systems attempt to game the process.
Boosting Clarity in 4D Flow MRI: A Deep Learning Approach
Researchers have developed a novel deep learning framework to improve the resolution of 4D Flow MRI data, even when data characteristics differ between training and real-world scans.