Computer Science / Artificial Intelligence

The Gaining Paths to Investment Success: Information-Driven LLM Graph Reasoning for Venture Capital Prediction

February 09, 2026

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Title: The Gaining Paths to Investment Success: Information-Driven LLM Graph Reasoning for Venture Capital Prediction
ArXiv ID: 2512.23489
Date: 2025-12-29
Authors: Haoyu Pei, Zhongyang Liu, Xiangyi Xiao, Xiaocong Du, Suting Hong, Kunpeng Zhang, Haipeng Zhang

📝 Abstract

Most venture capital (VC) investments fail, while a few deliver outsized returns. Accurately predicting startup success requires synthesizing complex relational evidence-company disclosures, investor track records, and investment network structures-through explicit reasoning to form coherent, interpretable investment theses. Traditional machine learning and graph neural networks both lack this reasoning capability. LLMs offer strong reasoning but face a modality mismatch with graphs. Recent graph-LLM methods target in-graph tasks where answers lie within the graph, whereas VC prediction is off-graph: the target exists outside the network. The core challenge becomes selecting graph paths that maximize predictor performance on an external objective while enabling step-by-step reasoning. We present MIRAGE-VC, addressing two obstacles: path explosion (thousands of candidate paths overwhelm LLM context) and heterogeneous evidence fusion (different startups need different analytical emphasis). Our information-gaindriven path retriever iteratively selects highvalue neighbors, distilling investment networks into compact chains for explicit reasoning. A mult...

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The Gaining Paths to Investment Success: Information-Driven LLM Graph Reasoning for Venture Capital Prediction

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