Tootfinder

On vacation in Australia and out on a road trip to the Blue Mountains. Sadly the weather gods did not cooperate and it's been raining and foggy the whole day. So just hibernating in our Airbnb and hoping that tomorrow's weather forecast of cloudy but dry does hold.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges
Senyao Li, Haozhao Wang, Wenchao Xu, Rui Zhang, Song Guo, Jingling Yuan, Xian Zhong, Tianwei Zhang, Ruixuan Li
https://arxiv.org/abs/2507.16731

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges
As large language models (LLMs) evolve, deploying them solely in the cloud or compressing them for edge devices has become inadequate due to concerns about latency, privacy, cost, and personalization. This survey explores a collaborative paradigm in which cloud-based LLMs and edge-deployed small language models (SLMs) cooperate across both inference and training. We present a unified taxonomy of edge-cloud collaboration strategies. For inference, we categorize approaches into task assignment, t…

MX-AI: Agentic Observability and Control Platform for Open and AI-RAN
Ilias Chatzistefanidis, Andrea Leone, Ali Yaghoubian, Mikel Irazabal, Sehad Nassim, Lina Bariah, Merouane Debbah, Navid Nikaein
https://arxiv.org/abs/2508.09197

MX-AI: Agentic Observability and Control Platform for Open and AI-RAN
Future 6G radio access networks (RANs) will be artificial intelligence (AI)-native: observed, reasoned about, and re-configured by autonomous agents cooperating across the cloud-edge continuum. We introduce MX-AI, the first end-to-end agentic system that (i) instruments a live 5G Open RAN testbed based on OpenAirInterface (OAI) and FlexRIC, (ii) deploys a graph of Large-Language-Model (LLM)-powered agents inside the Service Management and Orchestration (SMO) layer, and (iii) exposes both observ…