Sensing with Understanding: AI for Wearable Health
![Time series decomposition enables the creation of an extended model [latex]M^\hat{M}[/latex] by transforming an original time series [latex]\mathbf{x}[/latex] into component representations [latex]{\mathbf{C}\_{\mathbf{x}}}[/latex] via a forward function FF, and then leveraging the inverse transformation [latex]F^{-1}(\cdot)[/latex] in combination with the original time series network MM, all without requiring model retraining.](https://arxiv.org/html/2603.12880v1/x1.png)
New research explores how to build artificial intelligence systems that not only predict health metrics from wearable sensors, but also clearly explain why they made those predictions.
Science
Bending the Rules: Robotic Control of Flexible Objects in Tight Spaces
Researchers have developed a new approach to controlling deformable objects – like cables or hoses – alongside rigid structures, enabling more complex manipulation in cluttered environments.
Reasoning Agents Aren’t Always Logical
New research reveals that even the most advanced AI reasoning systems can be surprisingly fragile when faced with subtly altered inputs.
Robot Tennis Player Learns from Human Mistakes
Researchers have developed a system that allows a humanoid robot to acquire athletic tennis skills by learning from imperfect motion capture data of human players.
Seeing Parkinson’s: How AI is Decoding the Retina
Artificial intelligence is offering a new window into Parkinson’s disease, analyzing retinal images to detect and track its progression.
Stress-Testing Robot Brains: A New Approach to AI Resilience
![Q-DIG iteratively refines adversarial instructions-leveraging successful prompts as exemplars-to maximize vulnerability exploitation in a target system, archiving those inducing high failure rates across diverse attack styles [latex] (z_0 \text{ to } z_7) [/latex] and establishing a self-improving cycle of systemic stress-testing.](https://arxiv.org/html/2603.12510v1/x1.png)
Researchers have developed a novel method for rigorously evaluating and improving the reliability of AI systems that control robots by challenging them with diverse and realistic scenarios.
Beyond Simulation: A New Model for Artificial Consciousness
![The Dual-Laws Model posits that consciousness arises from a bidirectional feedback system wherein error correction operates at two distinct levels: one adjusting base-level states like neural connections, and another modulating higher-order index sequences-essentially a self, shaped by both bottom-up sensory input and top-down control-allowing for representations formed through base-level adjustments to then influence the very dynamics governing those index sequences [latex] \implies [/latex] a recursive interplay defining subjective experience.](https://arxiv.org/html/2603.12662v1/x1.png)
Researchers propose a Dual-Laws Model that moves beyond simply simulating intelligence to address the fundamental requirements for genuine consciousness in machines.
Predictive Coordination: Smoothing the Way for Multi-Robot Systems
![The study visualizes a statistical model representing the likelihood of unmanned aerial vehicle movement, with colored arrows indicating possible actions, and calculates a flow cost-defined by [latex]Eq.4[/latex]-that quantifies the distance from each action to this movement probability distribution.](https://arxiv.org/html/2603.12736v1/x2.png)
New research introduces a method for anticipating the movements of dynamic obstacles to improve path planning and reduce collisions in complex, shared environments.
Robots That ‘See’ Your Preferences: Guiding Motion with Visual Language
New research shows that robots can interpret visual cues and language to select movement paths that align with human preferences for style and object avoidance.
AI and Human Insight: A Powerful Pairing for Materials Discovery
A new framework combines the speed of automated experimentation with the nuanced judgment of human experts to accelerate the search for novel materials.