# Founder-GPT: Self-play to evaluate the Founder-Idea fit

arXiv: https://arxiv.org/abs/2312.12037 · HTML: https://mlsanity.com/paper/2312.12037/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 6 references that are in the map.
- First posted on arXiv 19 Dec 2023.

## Builds on (most cited first, 5 of 5)

- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Least-to-Most Prompting Enables Complex Reasoning in Large Language Models](https://mlsanity.com/paper/2205.10625/) (2022)
- [CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing](https://mlsanity.com/paper/2305.11738/) (2023)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback](https://mlsanity.com/paper/2305.10142/) (2023)

## Built on by (most cited first, 6 of 6)

- [SSFF: Investigating LLM Predictive Capabilities for Startup Success through a Multi-Agent Framework with Enhanced Explainability and Performance](https://mlsanity.com/paper/2405.19456/) (2024)
- [Automating Venture Capital: Founder assessment using LLM-powered segmentation, feature engineering and automated labeling techniques](https://mlsanity.com/paper/2407.04885/) (2024)
- [Reasoning-Based AI for Startup Evaluation (R.A.I.S.E.): A Memory-Augmented, Multi-Step Decision Framework](https://mlsanity.com/paper/2504.12090/) (2025)
- [DIALECTIC: A Multi-Agent System for Startup Evaluation](https://mlsanity.com/paper/2603.12274/) (2026)
- [Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data](https://mlsanity.com/paper/2505.24622/) (2025)
- [From Limited Data to Rare-event Prediction: LLM-powered Feature Engineering and Multi-model Learning in Venture Capital](https://mlsanity.com/paper/2509.08140/) (2025)

## Abstract (arXiv)

This research introduces an innovative evaluation method for the "founder-idea" fit in early-stage startups, utilizing advanced large language model techniques to assess founders' profiles against their startup ideas to enhance decision-making. Embeddings, self-play, tree-of-thought, and critique-based refinement techniques show early promising results that each idea's success patterns are unique and they should be evaluated based on the context of the founder's background.