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A Machine-to-Machine Knowledge-Guided LLM Agent for Generalizable Radiotherapy Treatment Planning
arXiv:2606.00922v1 Announce Type: cross Abstract: In this work, we propose a prototype machine-to-machine (M2M) knowledge-guided Large Language Model (LLM) framework for automated radiotherapy treatment planning. In the proposed paradigm, Treatment Planning Parameter (TPP) distribution knowledge discovered by a Deep Reinforcement Learning (DRL) agent is transferred to an LLM agent through in-context learning, enabling autonomous iterative planning without human intervention. While standard...
A Machine-to-Machine Knowledge-Guided LLM Agent for Generalizable Radiotherapy Treatment Planning
arXiv:2606.00922v1 Announce Type: new Abstract: In this work, we propose a prototype machine-to-machine (M2M) knowledge-guided Large Language Model (LLM) framework for automated radiotherapy treatment planning. In the proposed paradigm, Treatment Planning Parameter (TPP) distribution knowledge discovered by a Deep Reinforcement Learning (DRL) agent is transferred to an LLM agent through in-context learning, enabling autonomous iterative planning without human intervention. While standard...
Public Machine Learning Solver Framework for Novices in the Machine Learning Domain
Announce Type: new Abstract: Solving machine learning problems is complex and typically reserved for experts. Over the past two decades, systems have emerged to support non-experts. Based on our review, we identify three categories: (1) fully automated AutoML systems, (2) expert cheat sheets for algorithm selection, and (3) decision-support systems using selection criteria (accuracy, transparency, data requirements).
Podman 6: machine usability improvements
I wanted to introduce a significant change we are making in Podman 6 for our machine function. To understand the problem and change, we first need to establish that Podman machine is based on a concept of providers. The provider is a generic term that describes how Podman runs the Linux virtual machine.
Description and Discussion on DCASE 2026 Challenge Task 2: Noise-aware Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
arXiv:2606.01578v1 Announce Type: cross Abstract: This paper presents an overview of DCASE 2026 Challenge Task 2, titled "Noise-aware unsupervised anomalous sound detection (UASD) for machine condition monitoring." The task aims to advance noise-robust anomalous sound detection for machine condition monitoring under the unsupervised setting, where only normal machine sounds are available for training.
A Web-based software toolkit for accessible and best-practice machine learning analyses in biomedical research
Machine learning is increasingly central to biomedical research, but using machine learning well often requires substantial computational expertise and methodological care to produce high-quality results. To make machinelearning tools more accessible to biomedical researchers while supporting best-practice approaches, we developed the Galaxy Learning and Modeling (GLEAM) software toolkit. GLEAM enables researchers to performsupervised machine learning analyses through a set of web-based,...
Warren's Abstract Machine: A Tutorial Reconstruction
Warren's Abstract Machine: A Tutorial Reconstruction by Hassan Ait-Kaci Warren's Abstract Machine: A Tutorial Reconstruction is a book written by Hassan Ait-Kaci. The book explains the Warren Abstract Machine and is now out of print. The author owns the copyright and for a time the book was available at: http://vanx.org/archive/wam/wam.html for free to anyone who wanted to use it for non-commercial purposes.
macOS Container Machines
Container machine provides a highly integrated Linux environment that works seamlessly on your Mac. Container machines are fast, lightweight and persistent. They are based on standard OCI images that can be built and shared.
Ultrafast machine learning on FPGAs via Kolmogorov-Arnold Networks
Ultrafast machine learning on FPGAs via Kolmogorov-Arnold Networks This post is a high-level explainer for my Master’s thesis, which involves designing hardware architectures for ultrafast inference and online learning using the Kolmogorov-Arnold Network (KAN) architecture. I’ll assume familiarity with standard machine learning concepts, as well as some understanding of hardware and digital circuits; read my previous post here for the latter. Please read the two papers below for more...
Steam Machine and Steam Frame are coming 'this summer'
Steam Machine and Steam Frame are coming 'this summer' Still no price in sight. The Steam Machine and Steam Frame are officially scheduled to land in summer 2026, Valve announced today in a blog post about something else entirely. There's still no word on how much either bit of hardware will cost.