Continuous benchmarking: Keeping pace with an evolving ecosystem of models and technologies

2026-04-17

AI Summary

This work addresses the reproducibility challenges posed by the rapid evolution of large models and high-performance computing systems, where existing benchmarks lack sustainable and automated evaluation mechanisms. To bridge this gap, the authors propose a user-agnostic continuous benchmarking framework that integrates principles from software engineering—particularly continuous integration—to establish an automated pipeline. This pipeline seamlessly combines systematic workflows with community-driven collaboration, delivering a reproducible and scalable benchmarking infrastructure for artificial intelligence and neuroscience research. The framework significantly enhances the sustainability, transparency, and collaborative efficiency of scientific evaluation in these fields.

Technology Category

• Humans and AI: Other Foundations of Human Computation & AI
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Machine Learning: Life-Long and Continual Learning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Drawing on ideas from continuous integration, we present concepts of an automated benchmarking pipeline for high performance applications. Customization and collaboration have been key design goals owing to the requirements of research-software development as a continuous community effort. We have extended our previous conceptual work on systematic benchmarking workflows with the functionality of user-agnostic operations as well as continuous benchmarking. This fosters reproducibility and re-use of benchmarking results to ensure sustainable technological progress. We provide software-engineering solutions to keep pace with the rapid evolution of both large-scale models and high-performance computing systems with a view towards the scientific domains of neuroscience and artificial intelligence.

Problem

• continuous benchmarking
• high-performance computing
• reproducibility
• large-scale models
• automated benchmarking

Innovation

• continuous benchmarking
• automated benchmarking pipeline
• user-agnostic operations
• reproducibility
• high-performance computing