用可追溯的方法节点构建双树系统，实现可控可验证的智能创新
Explainable Innovation Engine: Dual-Tree Agent-RAG with Methods-as-Nodes and Verifiable Write-Back
- 将知识单元升级为方法节点，构建带权重的溯源树与分层聚类树
- 策略代理生成新方法并记录可审计轨迹，验证层筛选高质量节点回写
- 在6个领域均优于基线，尤其在推导密集场景提升显著
检索增强生成（RAG）提升了事实准确性，但多数系统依赖扁平文本块检索，对多步合成控制有限。本文提出可解释创新引擎，将知识单元从文本块升级为方法节点。系统维护加权方法溯源树以支持可追溯推导，并建立分层聚类抽象树以实现高效自顶向下导航。推理时，策略代理选择显式合成算子（如归纳、演绎、类比），组合生成新方法节点，并记录可审计轨迹。验证评分层随后剔除低质量候选，将已验证节点回写，支持持续演化。在六个领域和多种骨干模型上的专家评估显示，该方法持续优于基础基线，在推导密集场景中提升最大；消融实验确认了溯源回溯与剪枝机制的互补作用。结果表明，该方法为可控、可解释、可验证的智能体式RAG创新提供了可行路径。代码已开源于项目GitHub仓库 https://github.com/xiaolu-666113/Dual-Tree-Agent-RAG。
原文摘要 · Abstract (English)
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.
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