@inproceedings{pei-etal-2026-analyze,
title = "Analyze Like a Venture Capitalist: Information-Gain and Knowledge Enhanced Graph Reasoning for Startup Success Prediction",
author = "Pei, Haoyu and
Liu, Zhongyang and
Xiao, Xiangyi and
Du, Xiaocong and
Hong, Suting and
Zhang, Kunpeng and
Zhang, Haipeng",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1555/",
pages = "31087--31108",
ISBN = "979-8-89176-395-1",
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"
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<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</abstract>
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%0 Conference Proceedings
%T Analyze Like a Venture Capitalist: Information-Gain and Knowledge Enhanced Graph Reasoning for Startup Success Prediction
%A Pei, Haoyu
%A Liu, Zhongyang
%A Xiao, Xiangyi
%A Du, Xiaocong
%A Hong, Suting
%A Zhang, Kunpeng
%A Zhang, Haipeng
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F pei-etal-2026-analyze
%X 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
%U https://aclanthology.org/2026.findings-acl.1555/
%P 31087-31108
Markdown (Informal)
[Analyze Like a Venture Capitalist: Information-Gain and Knowledge Enhanced Graph Reasoning for Startup Success Prediction](https://aclanthology.org/2026.findings-acl.1555/) (Pei et al., Findings 2026)
ACL
- Haoyu Pei, Zhongyang Liu, Xiangyi Xiao, Xiaocong Du, Suting Hong, Kunpeng Zhang, and Haipeng Zhang. 2026. Analyze Like a Venture Capitalist: Information-Gain and Knowledge Enhanced Graph Reasoning for Startup Success Prediction. In Findings of the Association for Computational Linguistics: ACL 2026, pages 31087–31108, San Diego, California, United States. Association for Computational Linguistics.