# Leveraging Multi-AI Agents for Cross-Domain Knowledge Discovery

arXiv: https://arxiv.org/abs/2404.08511 · HTML: https://mlsanity.com/paper/2404.08511/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 12 Apr 2024.

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

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [Cross-Domain Pre-training with Language Models for Transferable Time Series Representations](https://mlsanity.com/paper/2403.12372/) (2024)
- [FAIR Enough: How Can We Develop and Assess a FAIR-Compliant Dataset for Large Language Models' Training?](https://mlsanity.com/paper/2401.11033/) (2024)
- [Comparative Study of Domain Driven Terms Extraction Using Large Language Models](https://mlsanity.com/paper/2404.02330/) (2024)

## Built on by (most cited first, 4 of 4)

- [A Review of Large Language Models and Autonomous Agents in Chemistry](https://mlsanity.com/paper/2407.01603/) (2024)
- [When Disagreements Elicit Robustness: Investigating Self-Repair Capabilities under LLM Multi-Agent Disagreements](https://mlsanity.com/paper/2502.15153/) (2025)
- [EMR: Self-Evolving Medical Multi-Agent System via Experience Mining and Reuse](https://mlsanity.com/paper/2609.15161/) (2026)
- [LLM-Enabled Multi-Agent Systems: Empirical Evaluation and Insights into Emerging Design Patterns & Paradigms](https://mlsanity.com/paper/2601.03328/) (2026)

## Abstract (arXiv)

In the rapidly evolving field of artificial intelligence, the ability to harness and integrate knowledge across various domains stands as a paramount challenge and opportunity. This study introduces a novel approach to cross-domain knowledge discovery through the deployment of multi-AI agents, each specialized in distinct knowledge domains. These AI agents, designed to function as domain-specific experts, collaborate in a unified framework to synthesize and provide comprehensive insights that transcend the limitations of single-domain expertise. By facilitating seamless interaction among these agents, our platform aims to leverage the unique strengths and perspectives of each, thereby enhancing the process of knowledge discovery and decision-making. We present a comparative analysis of the different multi-agent workflow scenarios evaluating their performance in terms of efficiency, accuracy, and the breadth of knowledge integration. Through a series of experiments involving complex, interdisciplinary queries, our findings demonstrate the superior capability of domain specific multi-AI agent system in identifying and bridging knowledge gaps. This research not only underscores the significance of collaborative AI in driving innovation but also sets the stage for future advancements in AI-driven, cross-disciplinary research and application. Our methods were evaluated on a small pilot data and it showed a trend we expected, if we increase the amount of data we custom train the agents, the trend is expected to be more smooth.