CORE-Acu: Structured Reasoning Traces and Knowledge Graph Safety Verification for Acupuncture Clinical Decision Support

TL;DR

This work proposes CORE-Acu, a neuro-symbolic framework for acupuncture clinical decision support that integrates Structured Chain-of-Thought (S-CoT) with knowledge graph (KG) safety verification and establishes CORE-Acu as a robust neuro-symbolic framework for acupuncture clinical decision support, guaranteeing both reasoning auditability and strict safety compliance.

Abstract

Large language models (LLMs) show significant potential for clinical decision support (CDS), yet their black-box nature -- characterized by untraceable reasoning and probabilistic hallucinations -- poses severe challenges in acupuncture, a field demanding rigorous interpretability and safety. To address this, we propose CORE-Acu, a neuro-symbolic framework for acupuncture clinical decision support that integrates Structured Chain-of-Thought (S-CoT) with knowledge graph (KG) safety verification. First, we construct the first acupuncture Structured Reasoning Trace dataset and a schema-constrained fine-tuning framework. By enforcing an explicit causal chain from pattern identification to treatment principles, treatment plans, and acupoint selection, we transform implicit Traditional Chinese Medicine (TCM) reasoning into interpretable generation constraints, mitigating the opacity of LLM-based CDS. Furthermore, we construct a TCM safety knowledge graph and establish a ``Generate--Verify--Revise'' closed-loop inference system based on a Symbolic Veto Mechanism, employing deterministic rules to intercept hallucinations and enforce hard safety boundaries. Finally, we introduce the Lexicon-Matched Entity-Reweighted Loss (LMERL), which corrects terminology drift caused by the frequency--importance mismatch in general optimization by adaptively amplifying gradient contributions of high-risk entities during fine-tuning. Experiments on 1,000 held-out cases demonstrate CORE-Acu's superior entity fidelity and reasoning quality. Crucially, CORE-Acu achieved 0/1,000 observed safety violations (95\% CI: 0--0.37\%), whereas GPT-4o exhibited an 8.5\% violation rate under identical rules. These results establish CORE-Acu as a robust neuro-symbolic framework for acupuncture clinical decision support, guaranteeing both reasoning auditability and strict safety compliance.

Figures (5)

• Figure 1: Overall architecture of the CORE-Acu framework. The framework operates in two phases: (1) Phase I (Offline Training):Module 1 constructs the S-CoT corpus to model the clinical causal chain, while Module 2 builds the TCM KG. These inputs guide Module 3 to fine-tune the LLM via LMERL, focusing on safety-critical entities. (2) Phase II (Online Inference):Module 4 implements a neuro-symbolic closed-loop mechanism, leveraging KG constraints from Module 2 to verify prescriptions and trigger bounded self-correction for guaranteed safety.
• Figure 2: The Triangulated Neuro-Symbolic Validation Framework of CORE-Acu. The protocol begins with a Symbolic Veto Mechanism that utilizes the TCM KG to deterministically intercept hard safety violations, effectively reducing the Safety Violation Rate. Validated outputs then undergo multidimensional adjudication and expert cognitive verification, which scrutinize the causal reasoning chain to distinguish authentic clinical logic from black-box memorization traps.
• Figure 3: Comparative performance across five clinical dimensions.
• Figure 4: Distribution of total scores across 1,000 test cases. The comparison includes CORE-Acu, Qwen-Max, Qwen3-8B, GPT-4o, GLM-4.6, and HuatuoGPT-o1-7B.
• Figure 5: Safety Violation Rate across models (lower is better).