Documenting the complex interactions within autonomous AI agents requires a clear, standardized approach, and this paper proposes a practical solution based on established software architecture principles.
New research reveals a growing disconnect between how AI is designed for the workplace and what workers actually need to feel engaged and fulfilled.
![A bioreactor system leverages machine learning to model missing physical phenomena; specifically, a neural network and Bayesian symbolic regression-represented by orange dots and green dashed lines, respectively- both predict aspects not captured by the traditional Monod equation [latex] \mu = \mu_{max} \frac{S}{K_S + S} [/latex] (shown as a solid blue line).](https://arxiv.org/html/2603.14918v1/x4.png)
A new Bayesian framework systematically searches for the underlying equations governing dynamic systems, even when key physical principles are unknown.
Research shows that artificial intelligence can assist designers in creating effective electronic textile sensor layouts for accurate motion capture.
A new architecture proposes fully autonomous systems capable of organizing and analyzing vast streams of news data, moving beyond assistance to independent computational journalism.
A project-based learning framework combining robotics and agile methodologies empowers students to tackle real-world challenges like automated disassembly and contribute to a circular economy.
A new multi-agent framework combines the power of artificial intelligence with climate and social science to offer a deeper understanding of complex, interacting systems.
As robotic systems grow in complexity, choosing the right language to define and execute missions becomes critical for success.
A new approach prioritizes specialized intelligence and structured knowledge over ever-larger, general-purpose models.
The RoCo Challenge at AAAI 2026 tested the limits of robotic assembly, revealing surprising strengths in end-to-end AI models.