Unmasking Hidden Causes: A New Approach to Causal Discovery
![Performance of a decorrelation method diminishes with increasing pervasive and localized confounding, as evidenced by the reduction in [latex]\Delta F1[/latex] score across varying strengths and densities of these confounding factors.](https://arxiv.org/html/2512.24696v1/figures/dcl2_rep10_SHD_vs_Ld.png)
Researchers have developed a method to infer causal relationships even when unobserved variables are actively distorting the data.
Science
Flowing Towards Better Policies: Reinforcement Learning Gets a Generative Boost
Researchers are integrating flow-based generative models into reinforcement learning algorithms to improve policy optimization and sample efficiency.
Uncovering Hidden Symmetries in High-Dimensional Data
A new machine learning approach reveals the fundamental invariants governing complex tensors, offering a powerful tool for analyzing data across diverse scientific fields.
Bridging the Reality Gap: Adapting 3D Object Detection to New Environments
New research demonstrates effective techniques for training 3D object detection systems with limited labeled data in unfamiliar driving conditions.
Bridging Scales with Learned Interactions
![Machine learning corrections to density functional theory-specifically, the [latex]\phi\_{\theta}^{(1,3)}[/latex] terms-accurately reproduce the bulk binodal, establishing distinct liquid and gas phases in the density profile, while further refinements-[latex]\phi\_{\theta}^{(2,4)}[/latex]-capture the nuanced layering near solid walls and define the slope of the vapor-liquid interface.](https://arxiv.org/html/2512.23840v1/images/def/correction_effects.png)
A new framework combines machine learning with density functional theory to accurately model complex physical phenomena, like wetting, with limited data.
DNA Storage Gets a Robotic Boost
Researchers demonstrate a fully integrated robotic platform, PCRobot, for high-fidelity PCR amplification and scalable DNA data storage applications.
Can AI Decipher Life’s Interactions?
A new benchmark assesses how well artificial intelligence can understand and predict biomolecular relationships from scientific literature.
Designing Proteins from Scratch: A New Diffusion Approach
![SeedProteo successfully generated binders-molecules designed to bind to specific protein targets-for notoriously difficult multi-chain proteins, including H1 (as a dimer), VEGF-A (as a dimer), and TNF-[latex]αα[/latex] (as a trimer), demonstrating its capacity to address complex protein interactions and meet predefined computational success criteria.](https://arxiv.org/html/2512.24192v1/x4.png)
Researchers have developed a novel method for creating entirely new protein structures with precise binding capabilities, pushing the boundaries of protein engineering.
Making Machines Understand Memes
Researchers are developing new AI frameworks to bridge the gap between visual understanding and the nuances of online humor.
Putting Manipulation in Human Hands
Researchers have unveiled a comprehensive ecosystem designed to teach robots how to perform everyday tasks with human-like dexterity.