Abstract

Most venture capital (VC) investments fail, while a few deliver outsized returns. Predicting startup success requires synthesizing relational evidence across company fundamentals, investor track records, and investment networks through explicit reasoning, which traditional machine learning and graph neural networks lack. Large language models excel at reasoning, but applying them to VC prediction must address: selecting compact evidence subgraphs from large investment networks, one-sided label noise where failures may be latent successes, and grounding decisions in structured VC domain knowledge. We present MIRAGE-VC, an evidence-grounded reasoning framework with three innovations. First, an information-gain-driven retriever distills networks into compact evidence subgraphs. Second, a dual-layer knowledge base grounds reasoning in VC principles. Third, a noise-aware mechanism down-weights mislabeled negatives via improved Positive-Unlabeled (PU) estimation. MIRAGE-VC achieves +5.9% F1 and +22.1% Precision@5 over state-of-the-art baselines. Expert evaluation confirms professional-quality rationales. We further validate our approach on public data with consistent improvements. Code and reasoning results are available at: https://github.com/ZhangDataLab/MIRAGE-VC.git