A Literature Survey of Multi-Agent Large Language Models for Startup Evaluation and Founder Decision Support

Emilin Shyni C; Charumathi D; Deepak Raj T; Pradeep K; Sanju R Bannikodu; Vishnu C S

A Literature Survey of Multi-Agent Large Language Models for Startup Evaluation and Founder Decision Support

Author(s)	Prof. Dr. Emilin Shyni C, Ms. Charumathi D, Mr. Deepak Raj T, Mr. Pradeep K, Mr. Sanju R Bannikodu, Mr. Vishnu C S
Country	India
Abstract	Generative artificial intelligence (AI) has made it cheap to produce startup artifacts such as interface copy, mock-ups, pitch decks and working prototypes, but deciding whether those artifacts are any good has not become cheaper. This survey examines the research behind that gap. The work covered was published between 2017 and 2025 in five areas: generative models for product and code tasks, multi-agent systems built on large language models (LLMs), reasoning, reflection and memory techniques, LLM-as-a-judge evaluation and its known biases, and empirical studies of human and AI collaboration and entrepreneurial decision-making. Comparative tables bring together the main frameworks, judge biases and field studies. Across these areas, generation is studied far more than judgment, and four gaps stand out: evaluation is rarely the main job of a multi-agent system, reviewer independence is little studied, judgments cannot yet be compared across product versions, and the effect on founders' decisions has not been measured. The survey ends with open challenges and a methodological outlook.
Keywords	Literature Survey, Multi-Agent Systems, Large Language Models, LLM-as-a-Judge, Startup Evaluation, Human-AI Collaboration, Decision Support
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 5, September-October 2026
Published On	2026-10-14

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A Literature Survey of Multi-Agent Large Language Models for Startup Evaluation and Founder Decision Support

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