@inproceedings{hazoom-etal-2026-iterate,
title = "Iterate Until Retrieved: Factual Nugget Optimization for Discoverable Continual Corrections in Agentic {RAG}",
author = "Hazoom, Moshe and
Patel, Gal and
Talmor, Alon and
Hope, Tom",
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.196/",
pages = "3297--3307",
ISBN = "979-8-89176-460-6",
abstract = "Agentic retrieval-augmented generation (RAG) systems in 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 initial nugget, probes it with the triggering query and paraphrases, reflects over failed retrieval and answer traces, and revises the nugget until it is discoverable. We evaluate INO with two production B2B knowledge-assistance agents across multiple companies that use our system: a product support agent that answers questions over company-specific knowledge bases, and a support ticket agent that assists support engineers. INO consistently improves results over baselines in terms of discoverability and usage of factual corrections, in automated and human evaluations. A controlled experiment in production shows that the nuggets improve user satisfaction with responses."
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<abstract>Agentic retrieval-augmented generation (RAG) systems in 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 initial nugget, probes it with the triggering query and paraphrases, reflects over failed retrieval and answer traces, and revises the nugget until it is discoverable. We evaluate INO with two production B2B knowledge-assistance agents across multiple companies that use our system: a product support agent that answers questions over company-specific knowledge bases, and a support ticket agent that assists support engineers. INO consistently improves results over baselines in terms of discoverability and usage of factual corrections, in automated and human evaluations. A controlled experiment in production shows that the nuggets improve user satisfaction with responses.</abstract>
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%0 Conference Proceedings
%T Iterate Until Retrieved: Factual Nugget Optimization for Discoverable Continual Corrections in Agentic RAG
%A Hazoom, Moshe
%A Patel, Gal
%A Talmor, Alon
%A Hope, Tom
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F hazoom-etal-2026-iterate
%X Agentic retrieval-augmented generation (RAG) systems in 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 initial nugget, probes it with the triggering query and paraphrases, reflects over failed retrieval and answer traces, and revises the nugget until it is discoverable. We evaluate INO with two production B2B knowledge-assistance agents across multiple companies that use our system: a product support agent that answers questions over company-specific knowledge bases, and a support ticket agent that assists support engineers. INO consistently improves results over baselines in terms of discoverability and usage of factual corrections, in automated and human evaluations. A controlled experiment in production shows that the nuggets improve user satisfaction with responses.
%U https://aclanthology.org/2026.emnlp-industry.196/
%P 3297-3307
Markdown (Informal)
[Iterate Until Retrieved: Factual Nugget Optimization for Discoverable Continual Corrections in Agentic RAG](https://aclanthology.org/2026.emnlp-industry.196/) (Hazoom et al., EMNLP 2026)
ACL