# DQA: Diagnostic Question Answering for IT Support

Source: [DQA: Diagnostic Question Answering for IT Support](<https://www.sota2.com/research/paper/69d5e0e97d0172140ec758e1>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-04-11T05:00:04.810Z

Authors: Vishaal Kapoor, Mariam Dundua, Sarthak Ahuja, Neda Kordjazi, Evren Yortucboylu, Vaibhavi Padala, Derek Ho, Jennifer Whitted, Rebecca Steinert

Published: 2026-04-07T02:42:32.000Z

## Abstract

Enterprise IT support interactions are fundamentally diagnostic: effective resolution requires iterative evidence gathering from ambiguous user reports to identify an underlying root cause. While retrieval-augmented generation (RAG) provides grounding through historical cases, standard multi-turn RAG systems lack explicit diagnostic state and therefore struggle to accumulate evidence and resolve competing hypotheses across turns. We introduce DQA, a diagnostic question-answering framework that maintains persistent diagnostic state and aggregates retrieved cases at the level of root causes rather than individual documents. DQA combines conversational query rewriting, retrieval aggregation, and state-conditioned response generation to support systematic troubleshooting under enterprise latency and context constraints. We evaluate DQA on 150 anonymized enterprise IT support scenarios using a replay-based protocol. Averaged over three independent runs, DQA achieves a 78.7% success rate under a trajectory-level success criterion, compared to 41.3% for a multi-turn RAG baseline, while reducing average turns from 8.4 to 3.9.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Multi-turn Enterprise IT Support Diagnosis on 150 Enterprise IT Support Scenarios (test)](<https://www.sota2.com/research/sota/multi-turn-enterprise-it-support-diagnosis-on-150-enterprise-it-support-scenarios-test.md>): Multi-turn enterprise IT support diagnosis; 150 enterprise IT support scenarios (test); Success Rate: 78.7.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2604.05350>)

[PDF](<https://arxiv.org/pdf/2604.05350.pdf>)