How can I improve multi-agent perception efficiency with limited bandwidth?
Fast2comm: Collaborative perception combined with prior knowledge
May 5, 2025
https://arxiv.org/pdf/2505.00740This paper introduces Fast2comm, a framework for improving multi-agent perception (like in self-driving cars) by efficiently sharing information between agents (cars). It addresses the problem of limited bandwidth and inaccurate location data by selectively sharing only the most important visual information, prioritizing areas around detected objects.
Key points relevant to LLM-based multi-agent systems:
- Prioritized Communication: Fast2comm's selective sharing strategy prioritizes informative features, mirroring how LLMs could prioritize relevant information during agent communication.
- Confidence Feature Generation: The use of a confidence map for feature selection is analogous to how LLMs use attention mechanisms to focus on crucial parts of input data.
- Adaptability to Errors: Fast2comm's robustness to localization errors is relevant to how LLMs can handle noisy or incomplete information.
- Dynamic Bandwidth Adaptation: Decoupling training and testing feature fusion strategies in Fast2comm can inspire similar dynamic adjustments in LLM-agent communication bandwidth based on context.
- Prior Knowledge Integration: The use of ground truth supervision and bounding boxes relates to how LLMs can leverage existing knowledge or specific instructions.
- Fusion of Information: Combining confidence and prior-knowledge-based features using concatenation and self-attention parallels how LLMs combine information from different sources.