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SustainML integrates Retrieval-Augmented Generation (RAG) to intelligently recommend machine learning models to users — without retraining or fine-tuning large language models (LLMs). Instead, SustainML combines vector search, graph-based reasoning, and language generation to query a massive repository of models hosted on Hugging Face.

The SustainML Developer Framework v0.2.0, built by eProsima, provides a unified and extensible interface to the backend services and the core SustainML_Library v0.2.0.This release enhances stability, visualization, and hardware integration, while preserving the simple, modular workflow that defines SustainML.

Presented at the Symposium on Foundations of Responsible Computing (FORC 2025), researchers from the University of Copenhagen’s Department of Computer Science (KU) explore how memorization, typically linked to overfitting, can, under certain conditions, be leveraged to improve fairness metrics in AI classification models. Their paper, titled “When Can Memorization Improve Fairness?”, analyzes how key group fairness metrics, statistical parity, equal opportunity, and equalized odds, can be influenced, and in some cases superficially optimized, by memorizing targeted subpopulations.

Researchers from the German Research Center for Artificial Intelligence (DFKI) and ETH Zurich have introduced a novel neural architecture for sensor-based Human Activity Recognition (HAR), spotlighting the potential of Kolmogorov-Arnold Networks (KANs) for low-power, high-accuracy AI. Their paper, “Initial Investigation of Kolmogorov-Arnold Networks (KANs) as Feature Extractors for IMU Based Human Activity Recognition”, offers a new approach to building sustainable AI models optimized for time-series data from inertial sensors.