Twisting the Rules: How Chirality Impacts Actin Filament Mechanics
![A computational model demonstrates that helical actin filaments emerge from the interplay of local mechanical rules-specifically, torque applied to interconnected protofilaments-and are characterized by torsional rigidity [latex]K_{\tau}[/latex], bending persistence length [latex]L_P[/latex], and inter-protofilament separation rigidity [latex]K_S[/latex], as validated through Cytosim simulations.](https://arxiv.org/html/2512.24154v1/x1.png)
New computational modeling reveals the crucial role of helical structure in determining the mechanical behavior of actin filaments, key components of the cell’s internal scaffolding.
Science
Decoding the Signals: Linking Lung Scan Data to Cancer Risk
Researchers have created a comprehensive dictionary that connects quantitative image features from lung scans with established radiological assessments, paving the way for more transparent and reliable AI-driven cancer screening.
Teamwork Makes the Dream Work: AI Agents Learn to Collaborate
A new framework combines the reasoning power of large language models with reinforcement learning to build AI agents that excel in collaborative tasks.
Time Series Without the Hassle: A New Approach to Stream Computing
A novel framework unifies batch and streaming computation for time series data, prioritizing data consistency and efficient performance.
Predictive Power: Unlocking Robotic Planning with World Models
![The system learns a world model through joint embedding of video and proprioceptive data, enabling it to predict future states based on action sequences; this predictive capability is then leveraged in a planning process where iteratively refined action sampling minimizes a computed trajectory cost [latex]L^{p}[/latex].](https://arxiv.org/html/2512.24497v1/x1.png)
New research pinpoints the critical design elements that enable Joint-Embedding Predictive World Models to excel at complex robotic tasks.
Hidden Biases in Neural Networks: Finding the Patterns That Matter
A new framework identifies and explains clustered discrimination in deep learning models, moving beyond simple individual fairness checks.
Untangling Interactions: A New Way to Spot Meaningful Signals
![The methodology consistently identified genes across independent runs-exceeding a stability threshold of 0.25-and demonstrated a statistically significant differentiation between real data and random noise, as evidenced by a comparison of marker counts across [latex]10^6[/latex] permutations and reliability test runs.](https://arxiv.org/html/2512.24843v1/x2.png)
Researchers have developed a novel statistical method to identify specific interactions within complex datasets, filtering out noise and revealing crucial relationships.
Self-Governing Data Pipelines: The Rise of AI-Driven Automation
A new approach leverages artificial intelligence agents and policy enforcement to proactively manage and optimize cloud data workflows.
Following the Field: AI-Powered Crop Tracking for Smarter Farming
A new framework, CropTrack, uses advanced object tracking and re-identification techniques to monitor individual plants in challenging agricultural conditions.
Bridging Simulation and Reality: A New Era for Statistical Inference
Diffusion-based simulation-based inference is emerging as a powerful technique for tackling complex statistical challenges where traditional methods fall short.