Tootfinder

A Flexible Multi-Agent Deep Reinforcement Learning Framework for Dynamic Routing and Scheduling of Latency-Critical Services
Vincenzo Norman Vitale, Antonia Maria Tulino, Andreas F. Molisch, Jaime Llorca
https://arxiv.org/abs/2510.11535

A Flexible Multi-Agent Deep Reinforcement Learning Framework for Dynamic Routing and Scheduling of Latency-Critical Services
Timely delivery of delay-sensitive information over dynamic, heterogeneous networks is increasingly essential for a range of interactive applications, such as industrial automation, self-driving vehicles, and augmented reality. However, most existing network control solutions target only average delay performance, falling short of providing strict End-to-End (E2E) peak latency guarantees. This paper addresses the challenge of reliably delivering packets within application-imposed deadlines by l…

I have been driving, cycling and walking past this old house for years* waiting for it to fail completely but it is still holds up for the most part.
* My wife has been paying attention to it since she was a young girl.

NewtonBench: Benchmarking Generalizable Scientific Law Discovery in LLM Agents
Tianshi Zheng, Kelvin Kiu-Wai Tam, Newt Hue-Nam K. Nguyen, Baixuan Xu, Zhaowei Wang, Jiayang Cheng, Hong Ting Tsang, Weiqi Wang, Jiaxin Bai, Tianqing Fang, Yangqiu Song, Ginny Y. Wong, Simon See
https://arxiv.org/abs/2510.07172

NewtonBench: Benchmarking Generalizable Scientific Law Discovery in LLM Agents
Large language models are emerging as powerful tools for scientific law discovery, a foundational challenge in AI-driven science. However, existing benchmarks for this task suffer from a fundamental methodological trilemma, forcing a trade-off between scientific relevance, scalability, and resistance to memorization. Furthermore, they oversimplify discovery as static function fitting, failing to capture the authentic scientific process of uncovering embedded laws through the interactive explora…

A4FN: an Agentic AI Architecture for Autonomous Flying Networks
Andr\'e Coelho, Pedro Ribeiro, Helder Fontes, Rui Campos
https://arxiv.org/abs/2510.03829 https://

A4FN: an Agentic AI Architecture for Autonomous Flying Networks
This position paper presents A4FN, an Agentic Artificial Intelligence (AI) architecture for intent-driven automation in Flying Networks (FNs) using Unmanned Aerial Vehicles (UAVs) as access nodes. A4FN leverages Generative AI and Large Language Models (LLMs) to enable real-time, context-aware network control via a distributed agentic system. It comprises two components: the Perception Agent (PA), which semantically interprets multimodal input -- including imagery, audio, and telemetry data -- f…

Deconstruction of the anisotropic magnetic interactions from spin-entangled optical excitations in van der Waals antiferromagnets
Dipankar Jana, Swagata Acharya, Milan Orlita, Clement Faugeras, Dimitar Pashov, Mark van Schilfgaarde, Marek Potemski, Maciej Koperski
https://arxiv.org/abs/2510.03010

Deconstruction of the anisotropic magnetic interactions from spin-entangled optical excitations in van der Waals antiferromagnets
Magneto-optical excitations in antiferromagnetic d systems can originate from a multiplicity of light-spin and spin-spin interactions, as the light and spin degrees of freedom can be entangled. This is exemplified in van der Waals systems with attendant strong anisotropy between in-plane and out- of-plane directions, such as MnPS3 and NiPS3 films studied here. The rich interplay between the magnetic ordering and sub-bandgap optical transitions poses a challenge to resolve the mechanisms driving…