# Policy Induction: Predicting Startup Success via Explainable Memory-Augmented In-Context Learning

arXiv: https://arxiv.org/abs/2505.21427 · HTML: https://mlsanity.com/paper/2505.21427/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 27 May 2025.

## Builds on (most cited first, 6 of 6)

- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Least-to-Most Prompting Enables Complex Reasoning in Large Language Models](https://mlsanity.com/paper/2205.10625/) (2022)
- [LLM Voting: Human Choices and AI Collective Decision Making](https://mlsanity.com/paper/2402.01766/) (2024)
- [GPTree: Towards Explainable Decision-Making via LLM-powered Decision Trees](https://mlsanity.com/paper/2411.08257/) (2024)
- [A Taxonomy for Data Contamination in Large Language Models](https://mlsanity.com/paper/2407.08716/) (2024)

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

- [VCBench: Benchmarking LLMs in Venture Capital](https://mlsanity.com/paper/2509.14448/) (2025)
- [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)
- [LLM-AR: LLM-powered Automated Reasoning Framework](https://mlsanity.com/paper/2510.22034/) (2025)
- [Predicting Startup Success Using Large Language Models: A Novel In-Context Learning Approach](https://mlsanity.com/paper/2601.16568/) (2026)
- [Can Large Language Models Improve Venture Capital Exit Timing After IPO?](https://mlsanity.com/paper/2601.00810/) (2025)
- [When Career Data Runs Out: Structured Feature Engineering and Signal Limits for Founder Success Prediction](https://mlsanity.com/paper/2604.00339/) (2026)

## Abstract (arXiv)

Early-stage startup investment is a high-risk endeavor characterized by scarce data and uncertain outcomes. Traditional machine learning approaches often require large, labeled datasets and extensive fine-tuning, yet remain opaque and difficult for domain experts to interpret or improve. In this paper, we propose a transparent and data-efficient investment decision framework powered by memory-augmented large language models (LLMs) using in-context learning (ICL). Central to our method is a natural language policy embedded directly into the LLM prompt, enabling the model to apply explicit reasoning patterns and allowing human experts to easily interpret, audit, and iteratively refine the logic. We introduce a lightweight training process that combines few-shot learning with an in-context learning loop, enabling the LLM to update its decision policy iteratively based on structured feedback. With only minimal supervision and no gradient-based optimization, our system predicts startup success far more accurately than existing benchmarks. It is over 20x more precise than random chance, which succeeds 1.9% of the time. It is also 7.1x more precise than the typical 5.6% success rate of top-tier venture capital (VC) firms.