Stance detection requires identifying an author's position toward a target, often from short-form texts where stance is implicit, indirect, or rhetorically framed. Although large language models (LLMs) achieve strong performance on this task, single-pass prompting can be brittle when multiple interpretations are plausible. Existing aggregation strategies, such as majority voting or self-consistency, improve robustness by combining labels, but they discard the intermediate reasoning needed to resolve conflicting interpretations. We introduce a multi-agent reasoning framework with adaptive worker allocation for stance detection that shifts aggregation from label-level voting to reasoning-level synthesis. The framework employs a Manager-Worker architecture in which a Manager adaptively allocates a variable number of Worker agents based on input complexity. Each Worker analyzes the input from a distinct perspective and produces a reasoning-only explanation without emitting a stance label; the Manager then synthesizes these explanations to produce the final prediction. We evaluate the proposed framework on SemEval-2016, P-Stance, and COVID-19 Stance using Llama, Mistral, and Gemini. Results show that the framework yields the largest gains on implicit and context-dependent stance cases, achieving 86.07 Macro-F1 on COVID-19 and 82.90 on SemEval-2016, while remaining competitive on more explicit stance datasets such as P-Stance. These findings suggest that adaptive reasoning-level aggregation is most beneficial when stance cannot be reliably inferred from surface cues alone.

翻译：立场检测需要识别作者对特定目标的立场，此类任务常处理短文本，其中立场隐含、间接或通过修辞手法呈现。尽管大语言模型在该任务中表现优异，但当存在多种合理解读时，单次提示推理可能不够稳健。现有聚合策略（如多数投票或自一致性）通过标签合并增强鲁棒性，但丢弃了解析冲突解读所需的中间推理过程。我们提出一种自适应工作分配的多智能体推理框架用于立场检测，将聚合从标签级投票升级为推理级综合。该框架采用管理者-工作者架构：管理者根据输入复杂度自适应分配可变数量的工作者智能体。每个工作者从独特视角分析输入，仅生成推理过程说明而不输出立场标签；管理者随后综合这些解释得出最终预测。我们在SemEval-2016、P-Stance和COVID-19 Stance数据集上基于Llama、Mistral和Gemini模型评估该框架。结果表明，框架在隐式和上下文依赖的立场样本上提升最为显著，在COVID-19和SemEval-2016数据集上分别达到86.07和82.90的Macro-F1值，同时在显性立场数据集（如P-Stance）上保持竞争力。这些发现表明，当立场无法仅凭表面线索可靠推断时，自适应推理级聚合方法最具优势。