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

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.

💡 Research Summary

The paper introduces an Explainable Innovation Engine that upgrades the traditional Retrieval‑Augmented Generation (RAG) paradigm from flat text‑chunk retrieval to a structured, method‑level knowledge base. Instead of treating each document fragment as an isolated evidence piece, the authors index “methods‑as‑nodes” – reusable research artifacts such as algorithms, theorems, experimental protocols, or proof tactics. Two complementary trees are built over these nodes:

1. Method Provenance Tree (T_M) – a directed weighted graph where each node is a method and each edge (m_i → m_j) carries a contribution weight w_ij ∈