mlsanityData to 7 Oct 2026

A2RAG: Adaptive Agentic Graph Retrieval for Cost-Aware and Reliable Reasoning

arXiv 2601.21162 ↗First posted 29 Jan 2026cs.IR cs.AI cs.DBcited by 6

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

also in 1 more
Abstract (arXiv)

Graph Retrieval-Augmented Generation (Graph-RAG) enhances multihop question answering by organizing corpora into knowledge graphs and routing evidence through relational structure. However, practical deployments face two persistent bottlenecks: (i) mixed-difficulty workloads where one-size-fits-all retrieval either wastes cost on easy queries or fails on hard multihop cases, and (ii) extraction loss, where graph abstraction omits fine-grained qualifiers that remain only in source text. We present A2RAG, an adaptive-and-agentic GraphRAG framework for cost-aware and reliable reasoning. A2RAG couples an adaptive controller that verifies evidence sufficiency and triggers targeted refinement only when necessary, with an agentic retriever that progressively escalates retrieval effort and maps graph signals back to provenance text to remain robust under extraction loss and incomplete graphs. Experiments on HotpotQA and 2WikiMultiHopQA demonstrate that A2RAG achieves +9.9/+11.8 absolute gains in Recall@2, while cutting token consumption and end-to-end latency by about 50% relative to iterative multihop baselines.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation. Builds on 24 of its 30 references that are in the map. First posted on arXiv 29 Jan 2026.

Builds on

24 of 30 references.

Closest ancestors · 5

Same direction, newest first

  1. 2025Graphy'our Data: Towards End-to-End Modeling, Exploring and Generating Report from Raw Data24 Feb
  2. 2024Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation28 Oct
  3. 2024LightRAG: Simple and Fast Retrieval-Augmented Generation8 Oct
  4. 2024Graph Retrieval-Augmented Generation: A Survey15 Aug
  5. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarization24 Apr

Other references · 19

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  5. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  6. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  7. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
  8. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  9. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  10. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
  11. 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,259
  12. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
7 more
  1. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 911
  2. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 437
  3. 2018From Louvain to Leiden: guaranteeing well-connected communitiescited by 379
  4. 2023CodeT5+: Open Code Large Language Models for Code Understanding and Generationcited by 327
  5. 2024Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphscited by 116
  6. 2024Adaptive Retrieval-Augmented Generation for Conversational Systemscited by 22
  7. 2025Scaling Graph Chain-of-Thought Reasoning: A Multi-Agent Framework with Efficient LLM Servingcited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 6.

6 papers.

  1. 2026LARGER: Lexically Anchored Repository Graph Exploration and Retrievalcited by 2
  2. 2026RASER: Recoverability-Aware Selective Escalation Router for Multi-Hop Question Answeringcited by 2
  3. 2026HyperSU: Corpus-Driven Semantic-Unit Hypergraph for Retrieval-Augmented Generationcited by 1
  4. 2026Replan, Repair, or Edit? A Unified Empirical Evaluation of Travel Agents for Itinerary Revision under Resource Disruptionscited by 1
  5. 2026Asking for What Was Never Requested: Horizontal and Vertical Proactivity in Agentscited by 0
  6. 2026What Can Component-Replacement Evidence Establish? A Critical Scoping Review of Local Decisions in LLM Agentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025GraphSearch: An Agentic Deep Searching Workflow for Graph Retrieval-Augmented Generation
  2. 2026Agent-Enhanced Heterogeneous Graph RAG for Academic Question Answering
  3. 2026Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems
  4. 2026GraphSearch: Agentic Search-Augmented Reasoning for Zero-Shot Graph Learning
  5. 2025Graph Counselor: Adaptive Graph Exploration via Multi-Agent Synergy to Enhance LLM Reasoning
  6. 2025Think-on-Graph 3.0: Efficient and Adaptive LLM Reasoning on Heterogeneous Graphs via Multi-Agent Dual-Evolving Context Retrieval
  7. 2025Beyond Static Retrieval: Opportunities and Pitfalls of Iterative Retrieval in GraphRAG
  8. 2025SUBQRAG: Sub-Question Driven Dynamic Graph RAG