Beyond Buzzwords: How Collaboration Really Drives Bioeconomy Innovation

A new study analyzing five decades of Swedish innovation reveals that direct collaborative ties are more crucial for success than network structure or shared expertise.

A new study analyzing five decades of Swedish innovation reveals that direct collaborative ties are more crucial for success than network structure or shared expertise.
A new study explores whether automated optimization can create effective agentic systems for verifying software correctness.

Researchers have developed a novel robotic hand that leverages the mechanics of human fingernails to enhance precision and stability in manipulation tasks.
A new vision for scientific progress emphasizes collaborative, standardized workflows as the key to unlocking greater scalability and impact.

A new communication framework leverages the power of large language models to enable more effective teamwork between robotic agents.
![The study demonstrates that accurate prediction of protein-ligand binding affinity relies on precise consideration of functional group interactions-specifically, the affinity between carbonyl and pyridine groups-as neglecting these constraints leads to erroneous predictions of weak interactions, such as those incorrectly posited between carbon and oxygen atoms [latex] (C \leftrightarrow O) [/latex] instead of the correct interaction between carbon and nitrogen [latex] (C \leftrightarrow N) [/latex].](https://arxiv.org/html/2602.05479v1/x1.png)
Researchers have developed a novel deep learning framework to more accurately predict how small molecules interact with proteins, a crucial step in drug discovery and understanding biological processes.

Researchers have developed a new framework enabling a single AI agent to control diverse humanoid robots, significantly simplifying the challenge of robotic locomotion.

A new review examines how large language models are being used to automate and improve the creation of software documentation and modeling.
Researchers have developed a new method for understanding the complex decision-making processes within autonomous AI systems.

New research reveals how people interpret and react to errors made by robots operating in public spaces, highlighting the complexities of real-world human-robot interaction.