This collection explores the dynamic frontier of research spanning from carbon nanotubes to organic semiconductors, where chemists and materials scientists are redefining what is possible at the atomic scale. These studies investigate how molecular structures interact to create new technologies, often bridging the gap between theoretical chemistry and real-world applications like flexible electronics or advanced energy storage.

Every new preprint in this category arrives directly from arXiv, and Gist.Science immediately processes each submission to make the findings accessible to everyone. We provide both clear, plain-language overviews for general readers and detailed technical summaries for specialists, ensuring that complex discoveries in this rapidly evolving field are easy to understand and verify. Below are the latest papers exploring these groundbreaking materials and their transformative potential.

TinyContainer: Container Runtime Middleware Enabling Multi-tenant Microcontrollers with Built-in Security

This paper introduces TinyContainer, a lightweight middleware for multi-tenant microcontrollers that enables dynamic, fine-grained resource scheduling and security through a metadata-driven approach, demonstrating its effectiveness with a 4ms overhead in IoT environments and a specific TinyML use case.

Bastien Buil, Chrystel Gaber, Samuel Legouix, Emmanuel Baccelli, Samia Bouzefrane2026-06-09💻 cs

FMplex: Model Virtualization for Serving Extensible Foundation Models

FMplex is a model virtualization system that enables multiple customized foundation model tasks to share a single physical backbone through logically isolated virtual instances and a batch-aware fair-queueing scheduler, significantly reducing latency and increasing cluster-scale task capacity compared to existing serving approaches.

Hetvi Shastri, Pragya Sharma, Walid A. Hanafy, David Irwin, Mani Srivastava, Prashant Shenoy2026-06-09🤖 cs.LG

TuneAgent: Agentic Operating System Kernel Tuning with Reinforcement Learning

TuneAgent is an agentic framework that leverages rule-based reinforcement learning and large language models to autonomously and safely optimize Linux kernel configurations, achieving significant performance improvements through a structured two-phase training strategy that addresses challenges like sparse feedback and workload sensitivity.

Hongyu Lin, Yuchen Li, Haoran Luo, Zhenghong Lin, Libo Zhang, Mingjie Xing, Yanjun Wu2026-06-02🤖 cs.LG