Abstract
Benchmarks such as SWE-bench and ARC-AGI demonstrate how shared datasets accelerate progress toward artificial general intelligence (AGI). We introduce VCBench, the first benchmark for predicting founder success in venture capital (VC), a domain where signals are sparse, outcomes are uncertain, and even top investors perform modestly. At inception, the market index achieves a precision of 1.9%. Y Combinator outperforms the index by a factor of 1.7x, while tier-1 firms are 2.9x better. VCBench provides 9,000 anonymized founder profiles, standardized to preserve predictive features while resisting identity leakage, with adversarial tests showing more than 90% reduction in re-identification risk. We evaluate nine state-of-the-art large language models (LLMs). DeepSeek-V3 delivers over six times the baseline precision, GPT-4o achieves the highest F0.5, and most models surpass human benchmarks. Designed as a public and evolving resource available at this http URL, VCBench establishes a community-driven standard for reproducible and privacy-preserving evaluation of AGI in early-stage venture forecasting.
Abstract (translated)
诸如SWE-bench和ARC-AGI这样的基准测试表明，共享数据集如何加速向通用人工智能（AGI）的进展。我们介绍了VCBench，这是首个用于预测创业投资（VC）领域创始人成功的基准测试，在该领域中信号稀疏、结果不确定，并且即使是顶级投资者的表现也较为平庸。在初始阶段，市场指数达到了1.9%的精度。而Y Combinator比市场指数高出1.7倍，一级公司则高出2.9倍。VCBench提供了9,000个匿名化的创始人资料，这些资料被标准化以保留预测特征的同时又能防止身份泄露，对抗性测试显示重新识别的风险减少了超过90%。 我们评估了九种最先进的大型语言模型（LLM）。DeepSeek-V3的精度比基准高出六倍以上，GPT-4o实现了最高的F0.5得分，大多数模型都超过了人类基准。VCBench作为一个公开且不断更新的资源，在此网址可获取，它为早期阶段的风险投资预测中AGI的可重复性和隐私保护评估建立了社区驱动的标准。
URL
https://arxiv.org/abs/2509.14448
