# VCBench: Benchmarking LLMs in Venture Capital

arXiv: https://arxiv.org/abs/2509.14448 · HTML: https://mlsanity.com/paper/2509.14448/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 17 Sep 2025.

## Builds on (most cited first, 12 of 12)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Rethinking Benchmark and Contamination for Language Models with Rephrased Samples](https://mlsanity.com/paper/2311.04850/) (2023)
- [ARC-AGI-2: A New Challenge for Frontier AI Reasoning Systems](https://mlsanity.com/paper/2505.11831/) (2025)
- [Benchmark Data Contamination of Large Language Models: A Survey](https://mlsanity.com/paper/2406.04244/) (2024)
- [Sequential Diagnosis with Language Models](https://mlsanity.com/paper/2506.22405/) (2025)
- [AntiLeakBench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World Knowledge](https://mlsanity.com/paper/2412.13670/) (2024)
- [A Fused Large Language Model for Predicting Startup Success](https://mlsanity.com/paper/2409.03668/) (2024)
- [Finding the unicorn: Predicting early stage startup success through a hybrid intelligence method](https://mlsanity.com/paper/2105.03360/) (2021)
- [Policy Induction: Predicting Startup Success via Explainable Memory-Augmented In-Context Learning](https://mlsanity.com/paper/2505.21427/) (2025)
- [Startup success prediction and VC portfolio simulation using CrunchBase data](https://mlsanity.com/paper/2309.15552/) (2023)

## Built on by (most cited first, 3 of 3)

- [From Stochastic Answers to Verifiable Reasoning: Interpretable Decision-Making with LLM-Generated Code](https://mlsanity.com/paper/2603.13287/) (2026)
- [When Career Data Runs Out: Structured Feature Engineering and Signal Limits for Founder Success Prediction](https://mlsanity.com/paper/2604.00339/) (2026)
- [IPO Finance Agent: Benchmark of LLM Financial Analysts Beyond Finance Agent v2, with Automated Rubric Generation, on the SpaceX (SPCX) IPO](https://mlsanity.com/paper/2606.23032/) (2026)

## Abstract (arXiv)

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 vcbench.com, VCBench establishes a community-driven standard for reproducible and privacy-preserving evaluation of AGI in early-stage venture forecasting.