mlsanityData to 7 Oct 2026

Explainable Innovation Engine: Dual-Tree Agent-RAG with Methods-as-Nodes and Verifiable Write-Back

arXiv 2603.09192 ↗First posted 10 Mar 2026cs.AIcited by 0

On the map: Retrieval-augmented generation → Agentic RAG, agents and memory

Abstract (arXiv)

Retrieval-augmented generation (RAG) improves factual grounding, yet most systems rely on flat chunk retrieval and provide limited control over multi-step synthesis. We propose an Explainable Innovation Engine that upgrades the knowledge unit from text chunks to methods-as-nodes. The engine maintains a weighted method provenance tree for traceable derivations and a hierarchical clustering abstraction tree for efficient top-down navigation. At inference time, a strategy agent selects explicit synthesis operators (e.g., induction, deduction, analogy), composes new method nodes, and records an auditable trajectory. A verifier-scorer layer then prunes low-quality candidates and writes validated nodes back to support continual growth. Expert evaluation across six domains and multiple backbones shows consistent gains over a vanilla baseline, with the largest improvements on derivation-heavy settings, and ablations confirm the complementary roles of provenance backtracking and pruning. These results suggest a practical path toward controllable, explainable, and verifiable innovation in agentic RAG systems. Code is available at the project GitHub repository https://github.com/xiaolu-666113/Dual-Tree-Agent-RAG.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation. Builds on 14 of its 16 references that are in the map. First posted on arXiv 10 Mar 2026.

Builds on

14 of 16 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023LeanDojo: Theorem Proving with Retrieval-Augmented Language Models27 Jun

Other references · 13

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  3. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
  4. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  5. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,411
  6. 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrievalcited by 519
  7. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 508
  8. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 450
  9. 2023Ragas: Automated Evaluation of Retrieval Augmented Generationcited by 408
  10. 2022Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofscited by 278
  11. 2023LEGO-Prover: Neural Theorem Proving with Growing Librariescited by 108
  12. 2025Knowledge Graph-Guided Retrieval Augmented Generationcited by 57
1 more
  1. 2025A Survey of Multimodal Retrieval-Augmented Generationcited by 37

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  1. 2026Iterate Until Retrieved: Factual Nugget Optimization for Discoverable Continual Corrections in Agentic RAG
  2. 2025Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges
  3. 2026MASS-RAG: Multi-Agent Synthesis Retrieval-Augmented Generation
  4. 2025FAIR-RAG: Faithful Adaptive Iterative Refinement for Retrieval-Augmented Generation
  5. 2026Towards Trustworthy and Cost-Efficient Data Integration: From Na"ive RAG to Agentic RAG
  6. 2026Agent-Orchestrated Adaptive RAG: A Comparative Study on Structured and Multi-Hop Retrieval
  7. 2025RAGCap-Bench: Benchmarking Capabilities of LLMs in Agentic Retrieval Augmented Generation Systems
  8. 2025Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs