A new arXiv paper proposes a three-layer diagnostic framework for Graph-RAG systems running on cloud-native databases. The approach separates errors stemming from data perturbations so they can be attributed orthogonally to reasoning loss rather than assumed-clean data. It targets the common gap where such systems overlook the effects of degraded database inputs.
A new arXiv preprint examines how machine-learning pipelines behave when training or test data is incomplete, imbalanced, poorly labelled, or drawn from mismatched distributions. The authors survey measurement approaches and methods designed to keep models reliable under these common real-world conditions. The work is framed as an overview of challenges and remedies rather than a new model release.
A new arXiv paper addresses quality issues in large e-commerce catalogs, including duplicate entries and unit mismatches that frustrate shoppers. The authors introduce a positional task conditioning technique that lets a single model reason over several error types in long product listings while scaling across many product families. The approach aims to automate catalog curation more reliably for retailers with extensive inventories.
A new arXiv paper argues that when datasets are built by running a detector, heuristic, or model over candidate pools, the resulting label precision depends on the true-positive rate within each pool rather than on detector quality alone. The authors apply Bayes' rule to show how this prevalence effect introduces hidden contamination into detector-defined datasets, a problem they describe as silent. The work suggests dataset builders should account for pool-level prevalence when estimating or reporting precision.
A new arXiv preprint describes a method for converting queries over cultural heritage knowledge graphs, such as the NFDI4Culture-KG, into narrative data stories that are easier for non-expert users to follow. The authors also use these generated narratives as a way to assess the quality of the underlying graph data. The work targets the gap between large triple stores covering art, music, inscriptions and historical events and the difficulty many users face in exploring them.