Incremental Retrieval-Augmented Generation for Human-in-the-Loop Decision Support
We introduce iRAG, a human-in-the-loop decision-support system combining Retrieval-Augmented Generation (RAG) with Hybrid Decision-Making. Unlike conventional RAG systems, relying on static Knowledge Bases (KBs), our proposal addresses settings where the KB is initially empty and is progressively populated through human decisions. The system stores previous decisions as retrievable precedents and moves from silent observation to evidence-grounded contestation and, finally, to autonomous handling with abstention. Against an independent baseline, iRAG matches final-decision error under 10% drift without retrieval decay and reduces it under 40% drift across all configurations. Retrieval deca...
| Authors | Federico Mazzoni, Andrea Failla |
|---|---|
| Primary category | cs.HC |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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