Who Decides What’s Real Online?
As social media bot detection becomes increasingly sophisticated, critical ethical questions about fairness, accountability, and transparency demand urgent attention.
Science
Beyond Labels: Scaling Linguistic Insight with AI Agents
![The LinguistAgent architecture establishes a framework for reasoning about language through the formalization of linguistic structures, enabling the agent to decompose complex sentences into their constituent parts and derive meaning based on underlying grammatical relationships - a process fundamentally rooted in the principles of compositional semantics, akin to evaluating [latex] f(g(x)) [/latex] where <i>f</i> represents semantic interpretation and <i>g</i> syntactic parsing.](https://arxiv.org/html/2602.05493v1/x1.png)
A new platform leverages the power of large language models and multi-agent systems to automate complex linguistic tasks, offering a transparent and reproducible approach to annotation.
Two Hands Are Better Than One: Mastering Robotic Dexterity with Multimodal AI
Researchers have developed a new AI framework that enables robots to perform complex, two-handed tasks with greater precision and adaptability.
Seeing is Reasoning: An Agent That Learns to Look
Researchers have developed a new system, Weaver, that learns to actively gather visual evidence from videos to improve its reasoning abilities.
Drone Autonomy Takes Flight with AI Brains
Researchers are exploring how large language models can give drones the reasoning skills needed to navigate complex indoor environments without pre-mapping.
Navigating the Long Read: Smarter Document Search with AI Agents
A new framework improves question answering over lengthy documents by understanding their structure and intelligently navigating content.
Understanding What Users *Really* Want: A New Approach to Recommendation
Researchers have developed a system that uses the power of large language models to decipher user motivations from reviews and behavior, leading to more relevant and effective recommendations.
Uncovering Hidden Drivers in Continuous-Time Data
New research bridges causal inference and stochastic modeling to reveal the underlying mechanisms generating complex event sequences.
Can Robots Truly Grasp the Basics?
![Despite all BusyBox configurations being within the training data’s affordance distribution, the algorithms [latex]\pi_{0.5}\pi_{0.5}-canon[/latex] and GR00T-N1.6-canon demonstrated robust performance only with visually familiar canonical configurations, revealing a significant failure to generalize to even slightly altered, out-of-distribution visual arrangements of the same underlying affordances.](https://arxiv.org/html/2602.05441v1/figures/res_fully_s.jpeg)
New research reveals that even advanced AI-powered robots struggle to reliably perform simple physical tasks when faced with slight variations in their environment.
Mapping Proteins in Space: A New Era for Tissue Biology
![The STProtein training framework leverages a multi-stage approach-initial protein structure prediction, followed by iterative refinement using [latex] \nabla_{\theta} L(\theta, x) [/latex]-to sculpt protein conformations capable of fulfilling designated functional requirements, ultimately demonstrating an adaptive system responding to the inherent entropy of structural possibilities.](https://arxiv.org/html/2602.05811v1/images/structure/framework4.jpg)
Researchers have developed a powerful new method to predict where proteins are located within tissues, even when direct protein measurements are limited.