mlsanityData to 14 Oct 2026

DQA: Diagnostic Question Answering for IT Support

arXiv 2604.05350 ↗First posted 7 Apr 2026cs.CL cs.AIcited by 1

On the map: Retrieval-augmented generation → RAG robustness and adaptive retrieval

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation. Builds on 9 of its 14 references that are in the map. First posted on arXiv 7 Apr 2026.

Builds on

9 of 14 references.

References · 9

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  3. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  4. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  5. 2018CoQA: A Conversational Question Answering Challengecited by 683
  6. 2018QuAC : Question Answering in Contextcited by 381
  7. 2020Few-Shot Generative Conversational Query Rewritingcited by 85
  8. 2025MTRAG: A Multi-Turn Conversational Benchmark for Evaluating Retrieval-Augmented Generation Systemscited by 51
  9. 2021A Comparison of Question Rewriting Methods for Conversational Passage Retrievalcited by 16

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026SEGRA: A Structured Experience Guided Reasoning Agent for Property Graph Question Answeringcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025DO-RAG: A Domain-Specific QA Framework Using Knowledge Graph-Enhanced Retrieval-Augmented Generation
  2. 2026RAG-X: Systematic Diagnosis of Retrieval-Augmented Generation for Medical Question Answering
  3. 2026VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge
  4. 2026QO-Bench: Diagnosing Query-Operator-Preserving Retrieval over Typed Event Tuples
  5. 2025WixQA: A Multi-Dataset Benchmark for Enterprise Retrieval-Augmented Generation
  6. 2026RAFT: A Stateful Retrieval-Augmented Framework for Troubleshooting Agents
  7. 2025Evaluation of retrieval-based QA on QUEST-LOFT
  8. 2026Case-Aware LLM-as-a-Judge Evaluation for Enterprise-Scale RAG Systems