Topic
knowledge graph
DeepRoot Multi-Agent System Enables Therapeutic Reasoning Over Historical Medical Texts with 47.6% Accuracy
DeepRoot is a multi-agent LLM system that jointly builds and utilizes a verified knowledge graph for therapeutic reasoning over historical medical texts. Applied to the Shen Nong Ben Cao Jing, it recovers 10 of 21 held-out compound-disease treatment pairs at R@20 (47.6%), significantly outperforming a raw corpus LLM (4.8%) and random baseline (2.4%). The system also reduces hallucination to 7-10% compared to 87% for tool-using LLMs, offering a scalable method for mining historical medical knowledge.
DYNA Framework Uses Temporal Knowledge Graphs to Reduce LLM Forgetting Without Retraining
Researchers propose DYNA, a lightweight framework that connects frozen large language models (LLMs) to a temporal knowledge graph, enabling continuous learning without costly retraining. On three temporal recall tasks, DYNA reduces catastrophic forgetting by ~7% compared to fine-tuning and improves temporal ordering by ~5% over standard retrieval-augmented generation (RAG). The paper also finds that higher graph clustering coefficients correlate with better retrieval, indicating the importance of graph structure.