# The Wisdom of Agent Crowds: A Human-AI Interaction Innovation Ignition Framework

arXiv: https://arxiv.org/abs/2505.06947 · HTML: https://mlsanity.com/paper/2505.06947/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use.
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 11 May 2025.

## Placed in

- LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/)

## Builds on (most cited first, 12 of 12)

- [Multi-Agent Security Tax: Trading Off Security and Collaboration Capabilities in Multi-Agent Systems](https://mlsanity.com/paper/2502.19145/) (2025)
- [Synergistic Multi-Agent Framework with Trajectory Learning for Knowledge-Intensive Tasks](https://mlsanity.com/paper/2407.09893/) (2024)
- [AutoPatent: A Multi-Agent Framework for Automatic Patent Generation](https://mlsanity.com/paper/2412.09796/) (2024)
- [LiPUP-MA: A Residential Experience-centric Multi-Agent Framework for Living-in-the-loop Participatory Urban Planning](https://mlsanity.com/paper/2412.20505/) (2024)
- [LNS2+RL: Combining Multi-Agent Reinforcement Learning with Large Neighborhood Search in Multi-Agent Path Finding](https://mlsanity.com/paper/2405.17794/) (2024)
- [Anywhere: A Multi-Agent Framework for User-Guided, Reliable, and Diverse Foreground-Conditioned Image Generation](https://mlsanity.com/paper/2404.18598/) (2024)
- [CoDe: Communication Delay-Tolerant Multi-Agent Collaboration via Dual Alignment of Intent and Timeliness](https://mlsanity.com/paper/2501.05207/) (2025)
- [Loosely Synchronized Rule-Based Planning for Multi-Agent Path Finding with Asynchronous Actions](https://mlsanity.com/paper/2412.11678/) (2024)
- [Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2412.15517/) (2024)
- [Structured Reasoning for Fairness: A Multi-Agent Approach to Bias Detection in Textual Data](https://mlsanity.com/paper/2503.00355/) (2025)
- [Don't Just Demo, Teach Me the Principles: A Principle-Based Multi-Agent Prompting Strategy for Text Classification](https://mlsanity.com/paper/2502.07165/) (2025)
- [A Multiagent Path Search Algorithm for Large-Scale Coalition Structure Generation](https://mlsanity.com/paper/2502.10226/) (2025)

## Abstract (arXiv)

With the widespread application of large AI models in various fields, the automation level of multi-agent systems has been continuously improved. However, in high-risk decision-making scenarios such as healthcare and finance, human participation and the alignment of intelligent systems with human intentions remain crucial. This paper focuses on the financial scenario and constructs a multi-agent brainstorming framework based on the BDI theory. A human-computer collaborative multi-agent financial analysis process is built using Streamlit. The system plans tasks according to user intentions, reduces users' cognitive load through real-time updated structured text summaries and the interactive Cothinker module, and reasonably integrates general and reasoning large models to enhance the ability to handle complex problems. By designing a quantitative analysis algorithm for the sentiment tendency of interview content based on LLMs and a method for evaluating the diversity of ideas generated by LLMs in brainstorming based on k-means clustering and information entropy, the system is comprehensively evaluated. The results of human factors testing show that the system performs well in terms of usability and user experience. Although there is still room for improvement, it can effectively support users in completing complex financial tasks. The research shows that the system significantly improves the efficiency of human-computer interaction and the quality of decision-making in financial decision-making scenarios, providing a new direction for the development of related fields.