MMoA: An AI-Agent framework with recurrence for Memoried Mixure-of-Agent
Quick Answer
MMoA introduces a novel AI-Agent framework that utilizes recurrence in a Memoried Mixure-of-Agent approach, enhancing the efficiency of multi-agent systems.
Quick Take
This framework aims to improve the interaction and memory retention of AI agents, potentially impacting applications in collaborative AI environments.
Key Points
- MMoA leverages recurrence for improved memory retention in AI agents.
- The framework enhances interaction efficiency among multiple agents.
- Potential applications include collaborative environments for AI systems.
- Focuses on optimizing memory usage in AI-agent interactions.
- Developed as part of ongoing research in AI frameworks.
Paper Resources
📖 Reader Mode
~3 min read
Bibliographic and Citation Tools
Code, Data and Media Associated with this Article
Demos
Recommenders and Search Tools
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
— Originally published at arxiv.org
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.CL
See more →TriAgent: Divergence-Aware Committees for Cost-Efficient Financial Sentiment Analysis
TriAgent introduces a cost-efficient multi-agent system for financial sentiment analysis, combining VADER, FinBERT, and Qwen2.5. It achieves an F1 score of ~0.87 with significant savings of $9.3M/year at a 10M-user scale compared to GPT-4o-mini, while also detecting hallucinations with an AUC of 0.90.