Mapping Molecular Interactions with Graph Networks
A new deep learning approach harnesses the power of graph neural networks to predict how strongly proteins bind to other molecules.
Science
Can AI Police Itself? Fact-Checking with Internal Knowledge
New research demonstrates that large language models can verify claims using the information already encoded within their parameters, eliminating the need for external databases.
Beyond Graphene: A Unified View of 2D Materials
A new database is bridging the gap between theoretical predictions and experimental results, accelerating the discovery of next-generation two-dimensional materials.
Robots That Rewrite Their Own Code
New research demonstrates a system where robots learn to manipulate objects by iteratively refining their control programs based on visual feedback and outcomes.
Teaching Robots to Tread Carefully: Risk-Aware Control for Mobile Manipulation
A new framework enables mobile robots to learn complex manipulation tasks while dynamically adjusting their sensitivity to potential risks and failures.
Modeling the Mind: A New Framework for Simulating Human Decision-Making
![The BioLLMAgent framework integrates an Internal Reinforcement Learning Engine-which generates utilities based on Expected Value, Expected Frequency, and Perseveration-with an External Large Language Model Shell simulating complete trials, and a Decision Fusion mechanism balances these approaches via parameter ω, effectively converting probabilistic outputs into static utility-scale priors [latex]\Pi_{\text{util}}[/latex].](https://arxiv.org/html/2603.05016v1/2603.05016v1/x2.png)
Researchers have developed a novel hybrid approach combining artificial intelligence and computational psychiatry to create more realistic and interpretable simulations of how people make choices.
Swimming with Data: Teaching Robots to Move Like Fish
Researchers are leveraging machine learning to grant robots the fluid, efficient movements of aquatic life, paving the way for more agile underwater vehicles.
Reasoning Machines: A New Approach to Neural Inference
![A Recursive Inference Machine iteratively refines a solution-beginning with an initial estimate [latex] y^{(0)} [/latex] and state [latex] z^{(0)} [/latex]-through [latex] T [/latex] steps of recursive state updates by a Solver, followed by solution generation via a Reweighter, and repeating this process [latex] N [/latex] times to converge on a final solution [latex] y^{(N)} [/latex].](https://arxiv.org/html/2603.05234v1/2603.05234v1/x1.png)
Researchers have developed a unified framework called Recursive Inference Machines that models reasoning as an iterative process, enhancing performance and adaptability across diverse tasks.
Robots Need Memories: A New Benchmark for Skillful Manipulation
Researchers have created a challenging new testbed to evaluate how well robots can learn and retain information for complex, long-duration tasks.
Decoding Antibody Power: A New Model Predicts Viral Binding
Researchers have developed a powerful language model capable of accurately predicting how strongly antibodies bind to the SARS-CoV-2 virus, opening new avenues for therapeutic design.