![The study establishes bounds for multi-robot dispersion, demonstrating that universal exploration sequences require memory [latex]M^*[/latex] dependent on the least inter-robot distance [latex]iiis[/latex], the number of nodes [latex]n~(k)[/latex], and maximum degree Δ, with performance varying based on prior knowledge of these parameters-or the lack thereof-and where computational complexity is expressed as time [latex]T[/latex] and memory [latex]M[/latex] with polylogarithmic factors suppressed by [latex]\tilde{O}[/latex].](https://arxiv.org/html/2602.05948v1/x9.png)
Researchers have formalized a more complex version of the classic ‘dispersion’ problem, requiring robots to navigate graphs and find nodes matching their assigned color, demonstrating increased difficulty over previous models.
New research reveals that medical learners prioritize effective instruction and focused practice over realistic conversation when co-designing AI-powered standardized patients.
![The research visualizes common data corruptions inherent in multi-sensor perception systems, specifically demonstrating how noise affects both camera imagery and [latex] 360^\circ [/latex] LiDAR point clouds- modalities labeled as C and L respectively-as captured within the JRDB dataset.](https://arxiv.org/html/2602.05538v1/x7.png)
A new study rigorously compares how different sensor technologies – cameras, LiDAR, and their combinations – perform in detecting people across real-world indoor and outdoor environments.
![A spatial choice grid defines the available movement options for each pedestrian at a given time step, [latex] P_{nt} [/latex], enabling the system to model pedestrian navigation through discrete spatial possibilities.](https://arxiv.org/html/2602.05142v1/grid_def.jpg)
New research uses detailed movement analysis to understand how people adjust their walking speed when encountering autonomous vehicles.
![The validation of Theorem 5.1 leveraged a discrete sandbox environment, with error bars denoting a margin of [latex] \pm 0.2 \pm 0.2 [/latex] standard deviations calculated across five independent trials.](https://arxiv.org/html/2602.06029v1/fig/consistency.png)
New research provides a theoretical framework demonstrating that an AI agent’s inherent ‘curiosity’ can guarantee optimal learning and decision-making in complex environments.
Researchers have developed a high-performance computing library that dramatically speeds up the simulation of complex biological systems, opening doors to virtual organ microenvironment studies.
A new framework, VISTA, improves how robots understand visual cues and translate them into precise actions.
A new approach combines the power of collaborative filtering with large language models to not only predict what users want, but also explain why.
Researchers have developed a novel framework where artificial intelligence agents collaboratively refine their own code, driving continuous improvement without constant human intervention.
Researchers have developed a novel system that dynamically adjusts its computational load based on how frequently data changes, enabling more efficient and responsive artificial intelligence.