用多智能体辩论框架提升项目重复检测准确率，避免重复投资。
PD$^3$: A Project Duplication Detection Framework via Adapted Multi-Agent Debate
- 设计多智能体辩论机制，实现项目集间的公平对比
- 在800多个真实项目中，参考项召回率提升4.05%，评分准确率提升9.77%
- 已部署在线平台，为442个新项目节省1344万美元
项目重复检测对项目质量评估至关重要，可避免重复投资。现有方法通常将其视为排序任务，依赖表面匹配或大模型直接判断，常忽视集合层面的参考选择需求。本文将任务重构为多对多参考集选择，需覆盖广泛候选信息，并在上下文限制下实现公平分解比较。提出PD$^3$框架，通过本地多智能体辩论与全局循环赛调度相结合，检索相关项目集。该调度器理论上保障公平性，通过均衡暴露与对比上下文实现。PD$^3$同时生成量化重复得分与定性重叠反馈。在800多个真实电力项目上，相比最强基线，相关参考选择提升4.05%，重复得分生成提升9.77%。已部署在线平台Review Dingdang，助力442个新项目节省1344万美元。
原文摘要 · Abstract (English)
Project duplication detection is critical for project quality assessment because it helps avoid investment in repeated proposals. Existing methods usually cast it as ranking and rely on surface matching or direct large language models judging, often missing practical needs in set-level reference selection. We recast the task as many-to-many reference set selection, which requires broad candidate information and fair decomposed comparison under context limits. We propose PD$^3$, a framework for Project Duplication Detection via adapted multi-agent Debate. PD$^3$ combines local multi-agent debate with global round-robin scheduling to retrieve the relevant project set. Theoretically, this scheduler guarantees fair comparison through balanced exposure and comparison context. PD$^3$ also produces quantitative duplication scores and qualitative overlap feedback. On 800+ real-world power projects, PD$^3$ outperforms the strongest baselines by 4.05% in relevant reference selection and 9.77% in duplication score generation. We deploy Review Dingdang, an online platform, which has helped save $13.44 million across 442 new projects.
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