AI 中文总结
该研究针对现有LLM组合方法忽略动态互补性的问题，提出WILC框架，通过迭代反思优化与双门互补模型选择机制，在四个基准上性能优于现有方法，成本仅为GPT-5.2的约1/7，扩展了群体智慧理论并提供多AI协调设计原则。
AI 中文摘要
大语言模型（LLM）正越来越多地被应用于企业场景中，但单个模型仍受限于自身特定的能力边界。这些异构的边界既带来了部署挑战，也创造了机遇：对多个LLM进行策略性协调，可能解锁超越任何单一模型的集体智能。现有方法预先固定了模型的组合方式，却忽略了复杂问题求解中互补性的动态、状态依赖作用。借鉴群体智慧范式，我们将集体LLM智能重新概念化为接力式互补：这是一个序列过程，其中每个后继模型被选中，用于解决前序模型输出中识别出的特定瓶颈。为实现这一目标，我们提出了WILC（LLM群体的智慧整合）框架，该框架基于两项设计原则：其一，迭代反思与优化建立了状态保留的工作流，模型可借此诊断并优化先前的输出；其二，互补驱动的模型选择通过双门机制控制转换：潜在互补适配度（PCF）识别最适合当前瓶颈的工作模型，而后验互补增益（PCG）评估所选转换是否改进了不断演进的解决方案。在四个不同基准上开展的实验表明，WILC的性能优于现有方法，包括单模型自优化、集成方法和查询路由方法。在标准化定价假设下，WILC的平均基准性能与GPT-5.2相当，但每查询的预估成本约低7倍，同时支持自托管部署，保障数据主权。本研究将群体智慧理论从静态聚合扩展到序列AI互补，为多AI协调提供了可迁移的设计原则。
英文摘要
Large language models (LLMs) are increasingly deployed in enterprise settings, yet individual models remain bounded by model-specific capability limitations. These heterogeneous boundaries pose a deployment challenge, but also create an opportunity: strategically coordinating multiple LLMs may unlock collective intelligence exceeding any single model. Existing approaches fix how models are combined in advance, overlooking the dynamic, state-dependent role of complementarity in complex problem solving. Drawing on the wisdom-of-crowds paradigm, we reconceptualize collective LLM intelligence as relay-style complementarity: a sequential process in which each successor model is selected to address the specific bottleneck identified in its predecessor's output. To operationalize this, we propose WILC (Wisdom Integration of LLM Crowds), a framework grounded in two design principles. First, iterative reflection-and-refinement establishes a state-preserving workflow through which models diagnose and refine prior outputs. Second, complementarity-driven model selection governs transitions via a dual-gate mechanism: prospective complementarity fit (PCF) identifies the worker most suited to the current bottleneck, while posterior complementarity gain (PCG) evaluates whether the selected transition improves the evolving solution. Experiments across four diverse benchmarks show that WILC outperforms existing approaches, including single-model self-refinement, ensemble methods, and query-routing methods. Under standardized pricing assumptions, WILC matches the average benchmark performance of GPT-5.2 at roughly 7 times lower estimated per-query cost, while facilitating data sovereignty through self-hosted deployment. This study extends wisdom-of-crowds theory from static aggregation to sequential AI complementarity and provides transferable design principles for multi-AI coordination.
