Can LLMs improve multi-agent perception efficiency?
CollaMamba: Efficient Collaborative Perception with Cross-Agent Spatial-Temporal State Space Model
September 13, 2024
https://arxiv.org/pdf/2409.07714This paper introduces CollaMamba, a novel system for multi-agent perception (like in self-driving cars) that helps agents "see" better by sharing information. Instead of relying on resource-intensive methods, it uses a more efficient architecture called "Mamba" to process spatial and temporal data.
Key points for LLM-based multi-agent systems:
- CollaMamba uses a compact, sequence-based feature representation suitable for exchanging information between agents.
- It leverages historical data to improve current perception, which could be valuable for LLMs to maintain context and predict future actions.
- It addresses unreliable communication by predicting missing information, which is crucial for real-world LLM-based agents that might not have constant communication.