Reproducibility, Benchmarking and Scientific Standards in Artificial Intelligence and Robotics

Abstract

Rapid advances in artificial intelligence and robotics are scientifically meaningful only when reported results can be independently understood, evaluated, compared, and, where feasible, reproduced. Yet contemporary research remains vulnerable to undocumented preprocessing, inaccessible datasets, hidden hyperparameter choices, random-seed sensitivity, weak baselines, benchmark overfitting, selective reporting, incomplete statistical analysis, simulation-to-reality gaps, and insufficient disclosure of computational resources. This editorial argues that reproducibility should be treated as a foundational condition of cumulative progress rather than an optional feature of exemplary research. Reproducibility, replicability, repeatability, robustness, and generalizability describe related but distinct dimensions of scientific reliability, each requiring appropriate evidence. The field should move toward transparent experimental reporting, standardized benchmark protocols, multiple-run evaluation, uncertainty quantification, stronger baseline comparisons, systematic ablation, accessible code and data where feasible, computational-resource disclosure, external validation, and real-world robotic testing. Journals, conferences, reviewers, and research institutions also shape these practices through incentives and publication standards. Scientific progress in intelligent systems should ultimately be measured not only by higher benchmark scores, but by whether claims remain credible when independently examined.