PD3: A Project Duplication Detection Framework via Adapted Multi-Agent Debate

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 PD3, a framework for Project Duplication Detection via adapted multi-agent Debate. PD3 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. PD3 also produces quantitative duplication scores and qualitative overlap feedback. On 800+ real-world power projects, PD3 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.