mlsanityData to 14 Oct 2026

CIRAG: Construction-Integration Retrieval and Adaptive Generation for Multi-hop Question Answering

arXiv 2601.06799 ↗First posted 11 Jan 2026cs.CL cs.AIcited by 2

On the map: Retrieval-augmented generation → Knowledge-graph and structured-knowledge RAG

Abstract (arXiv)

Triple-based Iterative Retrieval-Augmented Generation (iRAG) mitigates document-level noise for multi-hop question answering. However, existing methods still face limitations: (i) greedy single-path expansion, which propagates early errors and fails to capture parallel evidence from different reasoning branches, and (ii) granularity-demand mismatch, where a single evidence representation struggles to balance noise control with contextual sufficiency. In this paper, we propose the Construction-Integration Retrieval and Adaptive Generation model, CIRAG. It introduces an Iterative Construction-Integration module that constructs candidate triples and history-conditionally integrates them to distill core triples and generate the next-hop query. This module mitigates the greedy trap by preserving multiple plausible evidence chains. Besides, we propose an Adaptive Cascaded Multi-Granularity Generation module that progressively expands contextual evidence based on the problem requirements, from triples to supporting sentences and full passages. Moreover, we introduce Trajectory Distillation, which distills the teacher model's integration policy into a lightweight student, enabling efficient and reliable long-horizon reasoning. Extensive experiments demonstrate that CIRAG achieves superior performance compared to existing iRAG methods.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation. Builds on 22 of its 40 references that are in the map. First posted on arXiv 11 Jan 2026.

Builds on

22 of 40 references.

Closest ancestors · 4

Same direction, newest first

  1. 2025KnowTrace: Bootstrapping Iterative Retrieval-Augmented Generation with Structured Knowledge Tracing26 May
  2. 2025KiRAG: Knowledge-Driven Iterative Retriever for Enhancing Retrieval-Augmented Generation25 Feb
  3. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarization24 Apr
  4. 2023Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy24 May

Other references · 18

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  3. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  4. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,282
  5. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,128
  6. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 923
  7. 2023Large Language Models Can Be Easily Distracted by Irrelevant Contextcited by 841
  8. 2023In-Context Retrieval-Augmented Language Modelscited by 702
  9. 2023C-Pack: Packed Resources For General Chinese Embeddingscited by 578
  10. 2024NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Modelscited by 472
  11. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 369
  12. 2023FireAct: Toward Language Agent Fine-tuningcited by 256
6 more
  1. 2025From RAG to Memory: Non-Parametric Continual Learning for Large Language Modelscited by 231
  2. 2023Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificitycited by 206
  3. 2023RA-DIT: Retrieval-Augmented Dual Instruction Tuningcited by 195
  4. 2025Distilling LLM Agent into Small Models with Retrieval and Code Toolscited by 47
  5. 2021Multi-Step Reasoning Over Unstructured Text with Beam Dense Retrievalcited by 25
  6. 2025DualRAG: A Dual-Process Approach to Integrate Reasoning and Retrieval for Multi-Hop Question Answeringcited by 20

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Better Decomposition, Free Aggregation: A Synthesizer-Folding Framework for Multilingual Multi-Hop Question Answeringcited by 0
  2. 2026Beyond One-Shot Expansion: Contrastive Evidence Exploration for Multi-Hop Retrievalcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026MEGRAG: Multi-Granular Evidence Graphs for Answer-Aware Multi-Hop RAG
  2. 2024Tree of Reviews: A Tree-based Dynamic Iterative Retrieval Framework for Multi-hop Question Answering
  3. 2024Hierarchical Retrieval-Augmented Generation Model with Rethink for Multi-hop Question Answering
  4. 2025StepChain GraphRAG: Reasoning Over Knowledge Graphs for Multi-Hop Question Answering
  5. 2025A Comprehensive Evaluation of Transformer-Based Question Answering Models and RAG-Enhanced Design
  6. 2026Reasoning in Trees: Improving Retrieval-Augmented Generation for Multi-Hop Question Answering
  7. 2026D2F-ReAG: Dynamic Decomposition and Filtering for Multi-Hop Reasoning-Augmented Generation
  8. 2026ConRAG: Consensus-Driven Multi-View Retrieval for Multi-Hop Question Answering