LLM-Based Venture Capital Prediction with Graph Reasoning

This paper addresses the challenge of predicting venture capital success, a notoriously difficult task, by leveraging Large Language Models (LLMs) and graph reasoning. It introduces MIRAGE-VC, a novel framework designed to overcome the limitations of existing methods in handling complex relational evidence and off-graph prediction scenarios. The focus on explicit reasoning and interpretable investment theses is a significant contribution, as is the handling of path explosion and heterogeneous evidence fusion. The reported performance improvements in F1 and PrecisionAt5 metrics suggest a promising approach to improving VC investment decisions.