MMoA: An AI-Agent framework with recurrence for Memoried Mixure-of-Agent
Quick Take
MMoA introduces a recurrent architecture for improved context-aware aggregation in multi-agent systems.
Key Points
- Integrates LSTM-based gating for agent selection.
- Achieves competitive accuracy with reduced computational overhead.
- Evaluated on standard benchmarks like AlpacaEval 2.0.
📖 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
More from arXiv cs.CL
See more →Time to REFLECT: Can We Trust LLM Judges for Evidence-based Research Agents?
The reliability of LLM judges for evaluating deep research agents is critically assessed using the REFLECT benchmark.