Computer Science > Artificial Intelligence
arXiv:2603.09192 (cs)
[Submitted on 10 Mar 2026]
Title:Explainable Innovation Engine: Dual-Tree Agent-RAG with Methods-as-Nodes and Verifiable Write-Back
Authors:Renwei Meng
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Abstract: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 this https URL.
| Comments: | |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2603.09192 [cs.AI] |
| (or arXiv:2603.09192v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09192
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View a PDF of the paper titled Explainable Innovation Engine: Dual-Tree Agent-RAG with Methods-as-Nodes and Verifiable Write-Back, by Renwei Meng
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