Imagen de Portada

From paper to benchmark: agentic, framework-based reproduction of under-specified methods in machine health intelligence

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Theiler, Raffael, Comito, Ludovico, Leko, David, Von Krannichfeldt, Leandro, Telyatnikov, Lev, Fink, Olga
Formato:	Preprint
Publicado:	2026
Materias:	Artificial Intelligence Machine Learning Software Engineering
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Picid: A Modular Evaluation Infrastructure for Reproducible PHM Across Tasks and Domains
por: Telyatnikov, Lev, et al.
Publicado: (2026)

Integrating the Expected Future in Load Forecasts with Contextually Enhanced Transformer Models
por: Theiler, Raffael, et al.
Publicado: (2024)

Heterogeneous Graph Neural Networks for Short-term State Forecasting in Power Systems across Domains and Time Scales: A Hydroelectric Power Plant Case Study
por: Theiler, Raffael, et al.
Publicado: (2025)

Graph Neural Networks for Electric and Hydraulic Data Fusion to Enhance Short-term Forecasting of Pumped-storage Hydroelectricity
por: Theiler, Raffael, et al.
Publicado: (2024)

Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling
por: Von Krannichfeldt, Leandro, et al.
Publicado: (2025)

Combining Physics-based and Data-driven Modeling for Building Energy Systems
por: Von Krannichfeldt, Leandro, et al.
Publicado: (2024)

SWE-PolyBench: A multi-language benchmark for repository level evaluation of coding agents
por: Rashid, Muhammad Shihab, et al.
Publicado: (2025)

An LLM-based multi-agent framework for agile effort estimation
por: Bui, Thanh-Long, et al.
Publicado: (2025)

Flow Gym: A framework for the development, benchmarking, training, and deployment of flow-field quantification methods
por: Banelli, Francesco, et al.
Publicado: (2025)

PACIFIC: a framework for generating benchmarks to check Precise Automatically Checked Instruction Following In Code
por: Dreyfuss, Itay, et al.
Publicado: (2025)

From Physics to Machine Learning and Back: Part II - Learning and Observational Bias in PHM
por: Fink, Olga, et al.
Publicado: (2025)

Embedding computational neurorehabilitation in clinical practice using a modular intelligent health system
por: Weikert, Thomas, et al.
Publicado: (2025)

RITFIS: Robust input testing framework for LLMs-based intelligent software
por: Xiao, Mingxuan, et al.
Publicado: (2024)

The Agentic Automation Canvas: a structured framework for agentic AI project design
por: Lobentanzer, Sebastian
Publicado: (2026)

Visualisation for the CIS benchmark scanning results
por: Zhao, Zhenshuo, et al.
Publicado: (2025)

Reliable agent engineering should integrate machine-compatible organizational principles
por: Xian, R. Patrick, et al.
Publicado: (2025)

Optimizing and benchmarking the computation of the permanent of general matrices
por: Masschelein, Cassandra, et al.
Publicado: (2025)

Dynamic Provisioning of REST APIs for Model Management
por: Tuyishime, Adiel, et al.
Publicado: (2024)

A Systematic Mapping Study on Smart Cities Modeling Approaches
por: Rossi, Maria Teresa, et al.
Publicado: (2025)

Hyper-reduction methods for accelerating nonlinear finite element simulations: open source implementation and reproducible benchmarks
por: Larsson, Axel, et al.
Publicado: (2026)

On the need to perform comprehensive evaluations of automated program repair benchmarks: Sorald case study
por: Liyanage, Sumudu, et al.
Publicado: (2025)

TypeEvalPy: A Micro-benchmarking Framework for Python Type Inference Tools
por: Venkatesh, Ashwin Prasad Shivarpatna, et al.
Publicado: (2023)

On the Need for Configurable Travel Recommender Systems: A Systematic Mapping Study
por: Pereira, Rickson Simioni, et al.
Publicado: (2024)

Guiding the Search Towards Failure-Inducing Test Inputs Using Support Vector Machines
por: Sorokin, Lev, et al.
Publicado: (2024)

A declarative approach and benchmark tool for controlled evaluation of microservice resiliency patterns
por: Carlos M. Aderaldo, et al.
Publicado: (2024)

NNTile: a machine learning framework capable of training extremely large GPT language models on a single node
por: Mikhalev, Aleksandr, et al.
Publicado: (2025)

STELLAR: A Search-Based Testing Framework for Large Language Model Applications
por: Sorokin, Lev, et al.
Publicado: (2026)

Automated structural testing of LLM-based agents: methods, framework, and case studies
por: Kohl, Jens, et al.
Publicado: (2026)

Automated Program Repair: Emerging trends pose and expose problems for benchmarks
por: Renzullo, Joseph, et al.
Publicado: (2024)

Project MPG: towards a generalized performance benchmark for LLM capabilities
por: Spangher, Lucas, et al.
Publicado: (2024)

Empowering smart app development with SolidGPT: an edge-cloud hybrid AI agent framework
por: Hu, Liao, et al.
Publicado: (2025)

Topological Deep Learning with State-Space Models: A Mamba Approach for Simplicial Complexes
por: Montagna, Marco, et al.
Publicado: (2024)

Reproducibility in Machine Learning-based Research: Overview, Barriers and Drivers
por: Semmelrock, Harald, et al.
Publicado: (2024)

A benchmark for vericoding: formally verified program synthesis
por: Bursuc, Sergiu, et al.
Publicado: (2025)

A new object-oriented framework for solving multiphysics problems via combination of different numerical methods
por: Sargado, Juan Michael
Publicado: (2019)

Simulator Ensembles for Trustworthy Autonomous Driving Testing
por: Sorokin, Lev, et al.
Publicado: (2025)

Can Search-Based Testing with Pareto Optimization Effectively Cover Failure-Revealing Test Inputs?
por: Sorokin, Lev, et al.
Publicado: (2024)

Performance of explicit and IMEX MRI multirate methods on complex reactive flow problems within modern parallel adaptive structured grid frameworks
por: Loffeld, John J., et al.
Publicado: (2022)

A quantitative framework for evaluating architectural patterns in ML systems
por: Emanuilov, Simeon, et al.
Publicado: (2025)

Risk-based test framework for LLM features in regulated software
por: Zhou, Zhiyin
Publicado: (2026)