Iterate Until Retrieved: Factual Nugget Optimization for Discoverable Continual Corrections in Agentic RAG
arXiv:2605.25641v1 Announce Type: new Abstract: Agentic retrieval-augmented generation (RAG) systems in complex B2B (business-to-business) settings may often receive free-form response feedback. Rather than generic feedback signals such as style, preference, or overall response quality, we focus on actionable factual corrections. We identify these instances and convert them into compact knowledge-base entries, which we call factual nuggets. We introduce Iterative Nugget Optimization (INO), an index-time optimization method that uses the production agentic RAG as a test harness: it creates an i
The proliferation of RAG systems in B2B contexts is leading to practical challenges in maintaining factual accuracy and incorporating feedback, driving the need for more sophisticated correction mechanisms.
Improving the accuracy and adaptability of agentic RAG systems through optimized feedback loops is critical for their reliability and broader adoption in complex business operations.
The ability to 'correct' AI agents in a structured, iterative manner moves beyond generic feedback, directly improving factual recall and reducing 'hallucinations' in enterprise applications.
- · AI software providers
- · Enterprises adopting RAG
- · Knowledge management platforms
- · Companies with static knowledge bases
- · Generic feedback systems
- · Human fact-checkers (for routine tasks)
Enterprise AI applications become more reliable and trustworthy for sensitive operations.
The cost of maintaining and updating AI knowledge bases decreases, accelerating AI deployment across industries.
Enhanced AI agent reliability further accelerates the automation of white-collar workflows, impacting labor markets.
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