Learning to Work With Us: Robots That Adapt to Human Teammates
New research details a framework enabling robots to learn and predict human behavior over time, leading to more effective and intuitive collaboration.
Science
Beyond the Cloud: Accelerating Scientific Computing at the Edge
![Artificial Intelligence Engines (AIEs), guided by newly established design rules, enable the implementation of larger neural networks-such as Variational Autoencoders, Qubit Readout systems, and Deep Autoencoders-to surpass the 40 MHz throughput requirement of the Large Hadron Collider trigger system, a performance level unattainable with Programmable Logic for these complex models, though sufficient for smaller networks like Jet-taggers and [latex] \tau\tau [/latex] Event Selection systems.](https://arxiv.org/html/2604.19106v1/2026-pics/intro.png)
Deploying neural networks directly onto specialized hardware offers a pathway to low-latency inference for demanding scientific applications.
Agents of Discovery: Automating Molecular Dynamics with AI
A new multi-agent system, MDAgent, is poised to transform molecular dynamics research by moving beyond workflow automation to enable truly AI-driven scientific exploration.
Hand Signals for Robot Swarms: A New Era of Control
Researchers have developed a system allowing users to direct a swarm of miniature robots using intuitive hand gestures and visual cues.
Code Research, Fast: Building Tools for Accelerated Discovery
A new framework leverages the power of large language models and dynamic research resources to rapidly generate code for scientific exploration.
Beyond Hands and Feet: Teaching Robots to ‘See’ What They Can Do
![The system maps a robot’s perceptual input and physical characteristics to a dynamic scene representation, identifying objects and their potential affordances-[latex] \langle Object, Affordance, Location \rangle [/latex]-through visual language models and geometric triangulation, while semantic similarity metrics refine object recognition and consolidate variance within the established scene graph.](https://arxiv.org/html/2604.19509v1/pipeline.png)
New research explores how large vision-language models can enable robots with unconventional bodies to understand their potential interactions with the world.
Can AI Replicate Science?
A new framework uses artificial intelligence agents to automate the process of reproducing scientific analyses, paving the way for more reliable and transparent research.
Can Robots ‘See’ What They Can Do? Bridging the Perception Gap for Non-Humanoid Machines
New research explores how large vision-language models can help robots understand their interaction possibilities with the world, even with unconventional body plans.
Smart Results, Shallow Reasoning: The Limits of AI Scientists
New research reveals that artificial intelligence can now produce scientific outcomes without actually understanding the underlying principles.
Beyond Performance: How We Judge Robot Swarms
New research reveals that human preference for working with robot swarms is driven more by perceived social qualities than by objective task success.