Abstract: To address bottlenecks limiting LLMs in high-knowledge-density, logically-constrained domains like healthcare—namely the lack of self-correction in homogeneous multi-agent systems due to cognitive uniformity, and diminishing returns of computational investment—this study proposes a multi-agent validation fusion architecture based on heterogeneous model interaction. The architecture builds a differentiated hierarchical collaborative framework via prompt engineering, disrupting logical blind spots of single-model lineages. A high-performance large-scale model serves as the core decision-making agent, parsing medical records and generating preliminary diagnostic logic. Concurrently, heterogeneous-source models—with distinct output stylistics, conservative logical biases, and divergent training distributions—form an independent verification layer. This layer does not generate answers but conducts multi-dimensional audits through a “verification–issue enumeration–response” paradigm, scrutinizing inference pathways, factual consistency, and logical soundness. The theoretical foundation leverages cognitive divergences among heterogeneous models to enable productive logical conflict and rigorous cross-verification, enhancing output diversity, robustness, and medical factual rigor. In diabetes care evaluations, the system improves accuracy by 10% on multiple-choice questions, 8% on fill-in-the-blank tasks, and 22% on complex reasoning problems versus a single-agent baseline. Compared to Colacare, it shows superior logical consistency and inference stability. Notably, it surpasses Diabetica-7B (a deeply fine-tuned specialized model) without costly annotated data or fine-tuning. Experimental records show average inference cost of 61,881 tokens/query and latency of 71.74 seconds/query. Relative to specialized models, this system avoids prohibitive training costs and long data annotation cycles, while reducing architecturalcomplexity via a modular plug-in design. We conclude that for medical decision-making demanding high knowledge integration, introducing inter-model heterogeneity—not merely scaling homogeneous capacity—is critical for reliability. Optimizing heterogeneous-agent verification remediates logical vulnerabilities via inherent robustness without compromising large-model inference bounds. These findings offer a low-cost technical framework for medical AI and support the paradigm shift from “general intelligence” to “reliable professional intelligence.” Reduction achieved by removing phrases like “fundamental bottlenecks constraining the application,” “attributable to underlying,” “characterizing computational investment relative to performance gains,” “elaborately engineered,” “endowed with massive parameter counts and advanced generalized reasoning capabilities,” “innovatively,” “crucially,” “systematically,” “instantiate productive,” “concurrently improving model adaptability and stability across diverse clinical scenarios,” “specialized experimental evaluations targeting,” “through comprehensive comparative analysis with the state-of-the-art policy model,” “more prominently,” “professionally specialized,” “subjected to deep domain-specific fine-tuning,” “regarding the trade-off between engineering feasibility and resource consumption,” “protracted,” “overall,” “our research conclusions establish that,” “the successful empirical validation of this model demonstrates that,” “these findings furnish not only...but also,” and some redundant adjectives.

摘要： 针对当前大语言模型在医疗等高知识密度与强逻辑约束领域应用时，同源多智能体系统因底层思维逻辑一致性导致的自我纠错能力缺失、算力投入与性能产出不成正比等核心瓶颈问题，提出并实现了一种基于异质模型交互验证的多代理验证融合结构。该结构通过精密设计的提示工程构建了一个差异化的层级协作架构，其技术实现的核心在于打破单一模型系列的逻辑盲区。系统由具备极高参数量与泛化推理能力的高性能大模型担任核心决策智能体，负责解析医疗病历信息并生成初步诊疗逻辑；同时，创新性地引入了输出风格稳定、逻辑倾向保守且具备完全不同训练分布的异源模型作为验证层。其中，验证层并不参与答案的生成和决定，而是专注于对决策层输出的推理路径、医学事实一致性以及逻辑合理性进行多维度的“验证-问题列举-响应”式审计。这种特定结构的逻辑基础在于：利用异源模型间的思维差异性来实现异源智能体之间的逻辑冲突与交叉验证，从而在确保医疗事实严谨性的基础上，显著增强系统输出的多样性与稳健性，同时提升模型对于场景的适应能力和稳定性。在针对糖尿病医疗领域的专项实验分析中，该模型展现出显著的性能增强。实验结果显示，相较于相同基座配置的单智能体基线，模型在医疗知识选择题上的正确率提升了约 10%，在简单医学填空上提升了约 8%，在复杂医学题上的表现提升了约 22%。通过与当前前沿的策略模型 Colacare 进行深度对比，研究发现模型在处理医学决策时表现出更优的逻辑一致性与推理稳定性。此外，更突出的成果是，该系统在不依赖高成本的标注数据集和无需进行任何模型微调训练的情况下，其综合评测性能已超越了经过深度领域微调的专业模型 Diabetica-7B。在工程可行性与资源消耗的权衡分析中，实验数据记录显示该系统的平均推理消耗为 61,881 tokens/题，平均推理时延为 71.74 s/题；相较于 Diabetica-7B 等专用模型，该系统不仅规避了高昂的算力训练成本与数据标注周期，还通过灵活的插件式架构降低了系统的整体设计复杂度。研究结论表明，在处理高知识融合、强约束需求的医疗决策问题时，引入模型间的异质性而非单纯堆叠同源模型规模，是提升系统可靠性的关键路径。模型的成功实践证明，通过优化异构智能体间的协作校验机制，可以在不损失大模型推理上限的前提下，利用异源模型的稳健性有效填补逻辑漏洞。这一研究成果不仅为医疗 AI 系统在复杂任务下的低成本部署提供了全新的技术框架，也为大语言模型从“通用智能”向“可靠专业智能”的范式转型提供了重要的理论支撑与实践指引。