Robust-GAP Framework Claims to Eliminate Hallucinations in Multi-Document AI Summarization

Researchers have published Robust-GAP, a hierarchical Retrieval-Augmented Generation framework aimed at preventing semantic hallucinations and knowledge drift when AI models summarize multi-document log data. The framework combines three core components — dynamic causal graph extraction, schema-guided topology verification, and metadata provenance propagation — to ensure that large language models do not fabricate false causal links between unrelated events. Standard RAG pipelines struggle with this problem because they lack mechanisms to track causality across unstructured event streams, sometimes incorrectly linking coincidental log entries. Robust-GAP is described as the fourth generation of a research lineage dating back to a 2016 array-reduction algorithm, with intermediate iterations in 2024 and 2026 progressively adding document hierarchy and graph-anchoring capabilities. The full academic preprint has been made openly available on Zenodo under DOI 10.5281/zenodo.21436390.